genome-wide inference of protein interaction network and ...5 91 introduction 92 protein-protein...

1

Authors 1

Fangyuan Zhangdagger12 Shiwei Liudagger1 Ling Lidagger1 Kaijing Zuo1 Lingxia Zhao1 and 2

Lida Zhang1 3

1 Department of Plant Science School of Agriculture and Biology Shanghai Jiao Tong 4

University Shanghai 200240 China 5

2 Current Address Key Laboratory of Eco-environments in Three Gorges Reservoir 6

Region (Ministry of Education) SWU-TAAHC Medicinal Plant Joint RampD Centre 7

School of Life Sciences Southwest University Chongqing 400715 China 8

9

Running title 10

Inference of protein interaction network in Arabidopsis 11

12

Journal research area 13

SYSTEMS AND SYNTHETIC BIOLOGY 14

15

daggerThese authors contributed equally to this work 16

Fangyuan Zhang Shiwei Liu amp Ling Li 17

18

Corresponding author 19

Lida Zhang 20

E-mail address zhangldsjtueducn 21

Tel 86-21-34206144 Fax 86-21-34206144 22

23

Plant Physiology Preview Published on April 18 2016 as DOI101104pp1600057

Copyright 2016 by the American Society of Plant Biologists

wwwplantphysiolorgon February 24 2020 - Published by Downloaded from Copyright copy 2016 American Society of Plant Biologists All rights reserved

2

AUTHOR CONTRIBUTIONS 24

FZ SL and LL carried out data analyses and the experiments KZ and LZ 25

contributed experimental materials and advice on the experiments LZ conceived of the 26

project supervised the research and wrote the manuscript All authors approved the 27

manuscript 28

29

This work was supported by the National Basic Research Program of China (Grant No 30

2013CB733903-03) and the National Natural Science Foundation of China (Grant No 31

31371229) 32

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Genome-wide inference of protein interaction network and its 44

application to the study of crosstalk in Arabidopsis abscisic 45

acid signaling 46

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ABSTRACT 62

Protein-protein interactions (PPIs) are essential to almost all cellular processes To 63

better understand the relationships of proteins in Arabidopsis thaliana we have 64

developed a genome-wide protein interaction network (AraPPINet) that is inferred from 65

both three-dimensional structures and functional evidence and that encompasses 66

316747 high-confidence interactions among 12574 proteins AraPPINet exhibited high 67

predictive power for discovering protein interactions at a 50 true positive rate and for 68

discriminating positive interactions from similar protein pairs at a 70 true positive rate 69

Experimental evaluation of a set of predicted PPIs demonstrated the ability of 70

AraPPINet to identify novel protein interactions involved in a specific process at an 71

approximately 100-fold greater accuracy than random protein-protein pairs in a test case 72

of abscisic acid (ABA) signaling Genetic analysis of an experimentally validated 73

predicted interaction between ARR1 and PYL1 uncovered crosstalk between ABA and 74

cytokinin signaling in the control of root growth Therefore we demonstrate the power 75

of AraPPINet (httpnetbiosjtueducnarappinet) as a resource for discovering gene 76

function in converging signaling pathways and complex traits in plant 77

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INTRODUCTION 91

Protein-protein interactions (PPIs) are essential to almost all cellular processes 92

including DNA replication and transcription signal transduction and metabolic cycles 93

Because proteins function through coordinated interaction it is important to 94

characterize the nature of these relationships Deciphering the nature of PPIs not only 95

advances our understanding of gene function at the molecular level but also provides 96

insight into complex cellular processes 97

The importance of understanding PPIs has prompted the development of various 98

computational approaches such as gene fusion and Rosetta stone (Marcotte et al 1999) 99

phylogenetic profiling (Pellegrini et al 1999) correlated expression of gene pairs 100

(Grigoriev 2001) gene neighbour (Overbeek et al 1999) interolog (Matthews et al 101

2001) and combining multiple sources of biological data (Rhodes et al 2005) for 102

uncovering protein interactions over the past decade Recently computational methods 103

using structural information for PPI prediction have gained much attention due to the 104

rapid growth of the Protein Data Bank (PDB) (Rose et al 2013) Protein docking is a 105

promising method for discovering protein interactions that is based on 106

three-dimensional structural information but it remains a challenging and 107

computationally demanding task to predict PPIs on a genome-wide scale (Wass et al 108

2011) Alternatively knowledge-based methods that utilize the structural similarity of 109

protein pairs to interface a known protein complex have been used (Aytuna et al 2005 110

Zhang et al 2012 Mosca et al 2013) The common strategy of these structure-based 111

methods is to find a suitable template complex for the two query protein structures the 112

prediction is then based on the structural similarity of the two protein models to the 113

template complex An important advantage of structure-based approaches is their ability 114

to identify the putative interface namely they are capable of providing more 115

information than any non-structure based method 116

Arabidopsis thaliana a small flowering plant is widely used as a model organism in 117

plant biology In recent years several large-scale experimental PPI studies have begun 118

to unravel the complex cellular networks present in Arabidopsis (Arabidopsis 119


6

Interactome Mapping Consortium 2011 Jones et al 2014) According to an empirical 120

estimation of the size of the protein interactome experimentally determined interactions 121

represent a small fraction (approximately 6) of the entire protein interactome and the 122

relationships among most proteins remain to be discovered (Arabidopsis Interactome 123

Mapping Consortium 2011) To complement existing experimental resources 124

genome-wide plant PPI networks have been developed by computational approaches 125

(Cui et al 2008 Lee et al 2010 Lin et al 2011 Wang et al 2012 Wang et al 2014) 126

However these methods attempt to predict proteinndashprotein interactions using only 127

non-structural information 128

Here we developed a computational approach combining three-dimensional structure 129

with functional information to construct genome-wide PPI networks in Arabidopsis 130

Comparison of the predicted interactions with experimentally validated data revealed its 131

power for discovering protein interactions and for discriminating positive interactions 132

from similar protein pairs Experimental evaluation of the predicted PPIs demonstrated 133

the ability of AraPPINet to discovery novel protein interactions involved in specific 134

processes at 100-fold greater accuracy than screens of random protein-protein pairs for 135

the test case of abscisic acid (ABA) signaling Using AraPPINet guilt-by-association 136

and genetic analysis we identified and validated a regulator ARR1 involved in 137

crosstalk in ABA signaling mediated by PYL1 Our findings suggest that AraPPINet 138

could not only advance our understanding of gene function but could also provide 139

insight into the molecular basis of the complex interplay between signaling pathways in 140

plants 141

RESULTS 142

Construction of PPI Networks in Arabidopsis 143

We integrated three-dimensional structure and functional information using a machine 144

learning method for genome-wide PPI prediction in Arabidopsis The PPI model 145

contained four structural and seven non-structural features The structural features 146

included structural similarity structural distance preserved interface size and fraction 147


7

of the preserved interface that had been derived from structural alignments of protein 148

structure homology models to template complexes Given a PPI model with multiple 149

values for a structural feature the data with the highest structural similarity was 150

assigned to this interaction model This approach resulted in approximately 40 million 151

protein interaction models containing structural information extracted from 152

approximately 53 billion structural alignments (Supplemental Table S1) The seven 153

non-structural features of the interaction model were three gene ontologies gene 154

coexpression interolog Rosetta stone protein and phylogenetic profile similarity of 155

which the proportion ranged from 02 to 100 of expectations from all possible 156

protein pairs (Supplemental Table S1) Approximately 343 million protein pairs with 157

available data for these 11 features were then classified using a random forests 158

algorithm for PPI predictions resulting in an Arabidopsis PPI network (AraPPINet) that 159

contained 316747 high-confidence interacting pairs among 12574 (48) of the 26207 160

Arabidopsis proteins (Supplemental Fig S1) Almost a third of the interacting protein 161

pairs predicted by AraPPINet were supported by structural evidence (Supplemental Fig 162

S2) AraPPINet exhibited small-world properties similar to those of other biological 163

networks (Supplemental Fig S3A) and high clustering coefficient values indicated that 164

the topological structure of AraPPINet was highly modular (Supplemental Fig S3B) 165

Network analysis revealed that AraPPINet was composed of 1005 functional modules 166

clustered by a Markov clustering algorithm with an inflation parameter of 18 (Van 167

Dongen 2008) in which the largest module was preferentially associated with 168

transcriptional regulation (Supplemental Fig S1) 169

Evaluating the Performance of AraPPINet 170

To compare the accuracy of this combined approach with approaches utilizing either 171

structural or non-structural information a ten-fold cross-validation was carried out 172

using the reference datasets This evaluation showed that the true positive rate (TPR) of 173

this method reached 498 (Supplemental Fig S4A) while the false positive rate (FPR) 174

remained at the low level of 009 (Supplemental Fig S4B) Furthermore receiver 175

operating characteristic (ROC) curves showed that the combined method had a higher 176


8

area under the curve (AUC) value than methods relying on only structural or 177

non-structural information (Fig 1A) This approach was comparable in performance to 178

other methods over the entire range of the FPR values (Supplemental Fig S5) which 179

tended to produce fewer false positives and a lower FPR This result suggested that the 180

combined approach was more efficient than others at separating positives from a large 181

number of negatives at the genome-wide scale 182

Although cross-validation indicated that the performance of the combined method was 183

superior to those of other PPI predictive models reliant on only structural or 184

non-structural information these results could not be applied to estimate the combined 185

modelrsquos ability to discovery novel PPIs in practice Thus the performance of AraPPINet 186

was tested on an independent dataset of protein interactions determined by 187

high-throughput experiments from public databases After discarding 190 positive 188

reference interactions a total of 11669 protein interactions were remained as the 189

independent dataset for the performance evaluation Among these interactions 1401 190

PPIs were successfully predicted by this method (Fig 1B) The evaluation indicated our 191

predictive method was powerful in novel PPI discovery at the genome-wide scale 192

Next we used two independent datasets to evaluate the performance of AraPPINet 193

compared to that of three other publicly available PPI prediction methods AtPID (Cui 194

et al 2008) AtPIN (Brandatildeo et al 2009) and PAIR (Lin et al 2011) Among the 195

11669 protein interactions from high-throughput experiments approximately 120 of 196

protein interactions could be successfully recognized by AraPPINet which significantly 197

outperformed the AtPID AtPIN and PAIR methods (Fig 1C) As further validation of 198

AraPPINet we tested its performance on a dataset comprising 4533 newly reported 199

protein interactions in Arabidopsis after March 2014 Of these protein interactions 443 200

(98) were predicted by AraPPINet (Fig 1D) which exhibited better TPR than AtPID 201

AtPIN and the most recently developed PPI prediction method PAIR Furthermore we 202

also compared the accuracy of different prediction methods using F-measure From 203

table 1 apparently AraPPINet showed great improvement over all three published PPI 204

prediction methods based on the F1 score 205


9

It is reasonable to estimate the performance of AraPPINet by its ability to discriminate 206

between interacting and non-interacting pairs of proteins with similar sequences A total 207

of 1663 interactions between similar proteins in 56 families were used for a 208

performance evaluation As shown in Fig 2A only 03 of the mean TPR was 209

expected by chance alone and the three PPI prediction methods AtPIN AtPID and 210

PAIR had the mean TPRs ranging from 118 to 321 for the tested dataset 211

Compared with the performance of the other three methods AraPPINet showed high 212

predictive power for discovering interactions between protein family members with a 213

mean TPR of 695 In addition interactions between members of three specific 214

transcription factor families identified by high-throughput experiments were used to 215

evaluate the performance of AraPPINet for individual cases including 255 interactions 216

among 72 MADS 25 interactions among 17 bZIP and 418 interactions among 49 217

ARFIAA transcription factors (de Folter et al 2005 Ehlert et al 2006 Vernoux et al 218

2011) As shown in Fig 2B AraPPINet had AUC values ranging from 065 for the 219

MADS family to 077 for the BZIP family The precision of PPI prediction reached 220

221 for MADS and 506 for ARFIAA A number of the interactions between 221

similar members predicted in AraPPINet overlapped highly with those determined 222

through experimental assays (Fig 2C) suggesting that AraPPINet is a powerful tool for 223

discriminating positive interactions from similar protein pairs within gene families in 224

Arabidopsis 225

ABA Signaling Network in AraPPINet 226

The ABA signaling pathway is a gene network that plays important roles in numerous 227

biological processes in plants Although a linear core ABA signal transduction pathway 228

has been established the complexity of gene regulation suggests that ABA functions 229

through an intricate network that is interwoven with additional cellular processes Using 230

the core ABA components as query proteins including 14 ABA receptors 9 type 2C 231

protein phosphatases 10 SNF1-related protein kinases 2 (SnRK2) and 10 bZIP 232

transcription factors we identified 1912 proteins in AraPPINet predicted to interact 233

with the 43 core components in ABA signaling pathway The inferred ABA signaling 234


10

network contains 7272 interactions of which 197 connections have been 235

experimentally proven (Fig 3A) Among these node proteins ABI5 ABI1 and ABI2 236

have the top three connections in the ABA signaling network consistent with data 237

indicating that these three genes play the most important roles in the signal transduction 238

pathway 239

The accuracy of the inferred ABA signaling network was validated using two sets of 240

experimentally verified interactions Among the 31 proteins interacting with the core 241

ABA components determined by high-throughput experiments 7 (226) proteins were 242

successfully predicted by AraPPINet (Fig 3B) In addition among 131 newly reported 243

protein interactions involved in ABA signaling only two interactions were predicted by 244

the published methods AtPIN AtPID and PAIR In contrast AraPPINet recognized 39 245

(298) novel protein interactions in the dataset approximately 100-fold more than 246

screens of random protein-protein pairs involved in ABA signaling (Fig 3C) These 247

results suggest that AraPPINet is very useful for identifying novel genes using known 248

genes in this pathway as baits 249

The functional representation of proteins in the ABA signaling network was performed 250

using plant gene ontology (GO) slim categories (Ashburner et al 2005) Candidate 251

genes were significantly enriched in specific biological processes such as cellular 252

response to stimulus regulation of cellular process and signal transduction (Fig 3D) 253

The full functional enrichment data can be found in Supplemental Table S2 It is worth 254

noting that genes involved in ABA signaling were highly enriched in the protein binding 255

GO molecular function category implying that these proteins preferentially interact 256

with core ABA components to perform their functions Similarly functional analysis 257

carried out using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 258

showed that these proteins are significantly enriched in several specific pathways 259

(Supplemental Table S3) In addition to the expected plant hormone signal transduction 260

pathway pathways involved in plant-pathogen interaction and plant circadian rhythm 261

were identified (Fig 3E) In addition genes interacting with core ABA signaling 262

components were significantly altered in response to ABA treatments (Fig 3F) and 263


11

some of them were induced by other plant hormones (Supplemental Fig S6) suggesting 264

that these interacting proteins may act as nodes between ABA and other hormone 265

signaling pathways 266

Validating Novel Interactions in ABA Signaling Network 267

Given that AraPPINet successfully discovered novel PPIs involved in ABA signaling 268

we evaluated the hypothesized interaction-mediated crosstalk between ABA and other 269

signaling pathways Among the 1912 proteins interacting with ABA signaling 270

twenty-nine proteins predicted to interact with the ABA receptors PYL1 (AT5G46790) 271

or ABI5 (AT2G36270) were selected for the validation of their physical interactions by 272

yeast two-hybrid assay (Supplemental Table S4) As shown in Fig 4A AT3G16857 273

which encodes a regulator of the cytokinin response was found to interact with PYL1 274

by screening 8 candidate partners In addition ABI5 another core component in ABA 275

signaling was selected as bait to identify new binding partners We found that four 276

novel proteins (AT5G06960 AT3G22840 AT1G12610 and AT1G13260) were 277

determined to interact with ABI5 by screening 21 candidates (Fig 4A) AT5G06960 278

better known as TGA5 is a core component in salicylic acid signaling (Zhang et al 279

2003) while AT3G22840 is involved in early light response and seed germination 280

(Harari-Steinberg et al 2001 Rizza et al 2011) The proteins AT1G12610 and 281

AT1G13260 (RAV1) belong to the B3-AP2 transcription factor family the former is 282

involved in gibberellic acid (GA) signaling and response to abiotic stresses (Magome et 283

al 2008 Kang et al 2011) These results indicated that the accuracy of AraPPINet is 284

more than 17 in the test case of ABA signaling 285

A recent study showed that RAV1 co-expression with ABI5 enhanced plant resistance to 286

imposed drought stress (Mittal et al 2014) Thus bimolecular fluorescence 287

complementation (BiFC) assays were performed to validate the interaction between the 288

proteins RAV1 and ABI5 in plant cells Constructs encoding ABI5-YFPN and 289

RAV1-YFPC were co-infiltrated into tobacco leaves resulting in a visible YFP 290

fluorescence signal in nuclei (Fig 5B) As a control empty pEG201YN vector was 291

co-infiltrated with RAV1-YFPC and no fluorescence signal was observed (Fig 4B) 292


12

These results coupled with the phenotype of transgenic cotton suggested that RAV1 293

physically interacted with ABI5 to increase resistance to drought stress in plants (Mittal 294

et al 2014) 295

The structural interaction model of ARR1 and PYL1 was produced by superimposing 296

homology models on the template of the contact-dependent growth inhibition 297

toxinimmunity complex from Burkholderia pseudomallei (Fig 5A) The homology 298

model of ARR1 was structurally similar to chain A of the template complex while the 299

PYL1 model was structurally aligned to chain B The interaction model has a high 300

interacting probability score of 0788 arising from both structural similarity and a 301

conserved interface Similarly homology models of ABI5 and its four interacting 302

partners were superimposed with their respective homologous template complexes 303

These structural interaction models showed a significant level of conservation in the 304

interfaces between ABI5 and the four interacting partners (Fig 5BndashE) which resulted in 305

the interaction of protein pairs with different global structures via similar interface 306

architectures The structural details of these interactions revealed that conserved 307

interfaces could effectively improve the accuracy of discovering PPIs by computational 308

approaches 309

Identifying Crosstalk in ABA Signaling 310

The PYL1-interacting partner ARR1 a type-B response regulator is an important 311

regulator involved in the cytokinin signaling pathway (Sakai et al 2001 Wang et al 312

2011) Based on the physical interaction between ARR1 and PYL1 we hypothesized 313

that ARR1 might be involved in regulating the ABA signaling pathway mediated by 314

PYL1 To validate this hypothesis the response of the type-B arr11112 triple mutant 315

(CS6986) to ABA was tested by the primary root elongation assay to determine whether 316

the cytokinin signaling core regulator ARR1 was also involved in the regulation of ABA 317

signaling (Cary et al 1995 Leung et al 1994) The arr11112 triple mutant and 318

wild-type seedlings were grown on Murashige amp Skoog (MS) medium and they 319

showed similar primary root elongation phenotypes (Fig 6A B) Compared with 320

wild-type seedlings the arr11112 mutant seedlings displayed insensitivity to the 321


13

cytokinin-mediated inhibition of primary root growth on MS medium containing 10 microM 322

6-benzyladenine (6-BA) cytokinin (Fig 6A B) On MS medium supplemented with 10 323

microM ABA primary root elongation was inhibited in wild-type seedlings (Fig 6A B) In 324

contrast primary root elongation in arr11112 mutant seedlings was not inhibited by 325

ABA These data indicated that arr11112 mutant seedlings were insensitive to 326

cytokinin and ABA suggesting that type-B ARRs including ARR1 might be involved 327

in ABA signaling regulated by the ABA receptor PYL1 in addition to cytokinin 328

signaling (Fig 6C) 329

DISCUSSION 330

In this study we developed a computational approach through combining 331

three-dimensional structures with functional evidence to infer the genome-wide PPI 332

network in Arabidopsis The power of this method for discovering protein interactions 333

as well as predicting interactions between protein family members was demonstrated by 334

a comparison with other publicly available PPI prediction methods as well as by 335

experimentally determined PPI datasets The predicted AraPPINet network contains 336

316747 interactions covering nearly half of proteome (Arabidopsis Genome Initiative 337

2000) which is in agreement with the estimated size of the protein interactome in 338

Arabidopsis (Arabidopsis Interactome Mapping Consortium 2011) Furthermore 339

AraPPINet includes many interactions (856 of our predicted PPIs) beyond those 340

found by simple interolog prediction from reference model organisms which suggested 341

that our predictive method was powerful in novel PPI discovery 342

Structural information has been successfully used to validate or improve large-scale PPI 343

networks (Prieto et al 2010 Zhang et al 2012) Our analysis exhibited that structural 344

evidence could increase the reliability of predicted PPI networks by reducing false 345

positives from the large amount of data regarding non-interacting protein pairs at the 346

genome-wide scale (Supplemental Fig 4B) It is critical that only very low FPR can 347

produce a small enough number of false positives to be used effectively in practice In 348

addition to structural evidence functional clues preferentially contributed to the 349

increased coverage of positive interaction prediction (Supplemental Fig 4A) These two 350


14

factors combined to create balance between accuracy and coverage in PPI prediction 351

using AraPPINet 352

AraPPINet represents a reliable PPI network and was useful for identifying new 353

proteins involved in specific processes using a set of known genes as baits For example 354

using network guilt-by-association we defined an ABA signaling network 355

encompassing more than 7000 interactions involving approximately 1950 proteins in 356

Arabidopsis This protein interaction map greatly expanded the known ABA signaling 357

network in plants An evaluation based on two independent datasets showed that nearly 358

30 of the known protein interactions involved in ABA signaling were successfully 359

recognized by AraPPINet indicating that our computational approach for discovering 360

novel PPIs in ABA signaling is comparable in accuracy to high-throughput experiments 361

(von Mering et al 2002) Using yeast two-hybrid tests on a set of predicted PPIs linked 362

to the ABA and other signaling pathways five proteins were validated as newly 363

interacting partners of core components in the ABA signaling pathway Interestingly 364

ARR1 was predicted to interact with the ABA receptor PYL1 by AraPPINet and was 365

also detected by experimental screening By genetic analysis we validated ARR1 366

involvement in the regulation of cytokinin and ABA signaling through its interaction 367

with PYL1 Our findings suggested that AraPPINet could not only advance our 368

understanding of new gene functions but also provide new insight into the crosstalk 369

between pathways such as ABA signaling with respect to diverse biological processes 370

AraPPINet represents a step towards the goal of computationally reconstructing reliable 371

PPI networks at a genome-wide scale and this global protein interaction map could 372

facilitate the understanding of the biological organization of genes for specific functions 373

in plants 374

METHODS 375

Gold Standard Dataset 376

The experimentally determined PPIs for Arabidopsis were extracted from five databases 377

BioGRID (Chatr-Aryamontri et al 2015) IntAct (Orchard et al 2014) DIP (Salwinski 378


15

et al 2004) MINT (Licata et al 2012) and BIND (Isserlin et Al 2011) Proteins 379

without a valid TAIR locus or that were undefined in the Arabidopsis genome were 380

removed resulting in a total of 17569 PPIs among 6725 proteins The PPIs available in 381

different databases are listed in Supplemental Table S5 382

A dataset derived from those small-scale or high-throughput experiments with at least 383

two supporting publications was used as a primary positive reference After removing 384

protein self-interactions or interacting proteins encoded by mitochondria or chloroplast 385

genes it resulted in 5080 interacting protein pairs as the gold standard dataset 386

(Supplemental Table S6) Simultaneously a number of protein pairs were randomly 387

chosen to form the negative reference dataset The negative dataset created by this 388

method may contain several potentially interacting protein pairs however given the 389

empirically estimated ratio of interacting protein pairs of 5 to 10 interactions per 10000 390

protein pairs in the Arabidopsis genome (Arabidopsis Interactome Mapping Consortium 391

2011) this level of contamination is likely acceptable By this way 508000 protein 392

pairs not overlapping with 17569 experimentally determined interactions were 393

randomly generated as the negative reference dataset for training the protein interaction 394

classifier 395

Collection of Structural Information 396

The structure homology models of Arabidopsis proteins were taken from the ModBase 397

database (Pieper et al 2014) A protein structure was considered to be reliable if it met 398

at least one of following model evaluation criteria (1) an E-value lt 10-5 (2) an MPQS 399

score (ModPipe quality score) ge 05 (3) a GA341 score ge 05 or (4) a z-DOPE score lt 400

0 When multiple structures were available for a target protein we chose the 401

representative model with the highest MPQS score Based on the above criteria a total 402

of 17391 structure homology models were collected for Arabidopsis 403

A total of 90424 and 88999 structures involving multiple organisms were available in 404

the PDB (Protein Data Bank) (Rose et al 2013) and PISA (Proteins Interfaces 405

Structures and Assemblies) databases (Xu et al 2008) respectively The chain-chain 406

binary interface and the binding sites of protein complexes were generated in the 407


16

PIBASE software package with an interatomic distance cutoff of 605 Aring (Davis and Sali 408

2015) A total of 91652 protein complexes with 368306 interacting chain pairs among 409

317112 chains were identified for use as templates for protein interaction predictions 410

Protein structure homology models were aligned to template complexes using TM-align 411

(Zhang and Skolnick 2005) Approximately 53 billion structural alignments were 412

created between the protein structure homology models and the chains of template 413

complexes With a normalized TM-score cutoff of 04 for structural similarity a total of 414

50001762 structural alignments were generated that involved 16679 protein structure 415

models 416

Structural features were derived from the structural alignments of protein structure 417

homology models to template complexes Because two structural alignments are 418

obtained for each protein pair (ie protein structure homology model i is aligned to 419

template chain irsquo while protein model j is aligned to chain jrsquo TM-Scorei is the score 420

between protein structure model i to template chain irsquo and TM-Scorej is the score 421

between protein model j to chain jrsquo) structural similarity is calculated as the square root 422

of the product of the TM-Scorei and the TM-Scorej Similarly structural distance is 423

calculated as the square root of the product of RMSDi and RMSDj The preserved 424

interface size is defined as the number of interacting residue pairs in the potential 425

protein-protein model preserved in the template complex The fraction of the preserved 426

interface is the proportion of the conserved interface of the protein-protein model 427

relative to the corresponding interface in the template complex When multiple values 428

were available for a protein pair or a structural feature the data with the highest 429

structural similarity to the protein pair was chosen to create an interaction model 430

Similarity Analysis of Gene Function 431

The GO data used were gene_ontology1_2obo (Ashburner et al 2005) The functional 432

similarity of a gene pair was defined as log(nN)log(2N) where n is the number of 433

genes in the lowest GO category that contained both genes and N is the total number of 434

genes annotated for the organism This formula normalizes the functional similarity 435

range from 0 to 1 436


17

Interolog Analysis 437

Interolog analysis was performed similarly to a strategy proposed by Jonsson and Bates 438

(Jonsson and Bates 2006) A confidence score S for each PPI prediction was assigned 439

Given a protein pair A and B S could be defined as follows 440

)(1

=

times=N

iii ISbISaS 441

where Aprimei and Bprimei are the corresponding orthologs of A and B which show an interaction 442

in one model organism (ie the protein pair Aprimei and Bprimei is called an interolog of protein 443

pair A and B) ISai is the InParanoid score between Aprimei and A while ISbi is the 444

InParanoid score between Bprimei and B The orthologs of Arabidopsis proteins in E coli S 445

cerevisae C elegans D melanogaster M musculus and H sapiens were identified by 446

InParanoid with default settings N is the total number of interologs of protein pair A 447

and B identified in the six model organisms The experimentally determined PPI 448

datasets used for interolog analysis were obtained from the BioGRID IntAct DIP 449

MINT and BIND databases (Supplemental Table S7) 450

Coexpression Analysis 451

Arabidopsis GeneChip probe-gene mapping was performed as previously described 452

(Harb et al 2010) The oligonucleotide sequences of probes were mapped to genes 453

from the TAIR10 database using BLASTN with the E-value lt 10-5 (Altschul et al 454

1997) If two or more probe sets mapped to a single gene the expression value for that 455

gene was determined by averaging the signals across the probe sets In this way a total 456

of 21122 genes remained after disregarding probe sets that mapped to more than one 457

gene A total of 1388 Affymetrix Arabidopsis arrays were obtained from the TAIR Gene 458

Expression Omnibus (httpwwwarabidopsisorg) These slides were normalized using 459

RMAExpress software (Bolstad et al 2003) The correlations between genes were 460

determined by the Pearson correlation coefficient 461

Phylogenetic Profile Analysis 462

As previously described by Pellegrini and colleagues (Pellegrini et al 1999) 463


18

phylogenetic profiles for all Arabidopsis protein sequences were constructed using 464

BLASTP searches (E-value lt 10-10) against a collection of 30 eukaryotic and 660 465

prokaryotic genomes after applying an evolutionary filter for similar genomes This 466

calculation across collected genomes yielded a 690-dimensional vector representing the 467

presence or absence of homologues of a query protein in these genomes The probability 468

of two coevolved proteins is given by P as follows 469

P (x| K M N) = 1-minus

minusminus

1

0

x

KN

xKMN

xM

470

where x is the number of the co-occurrence homologues of proteins A and B in the 471

genomes K and M are the numbers of homologues of proteins A and B respectively 472

and N is the total number of collected genomes The negative log p-value of 473

phylogenetic profile similarity is used for prediction 474

Rosetta Stone Analysis 475

Rosetta stone proteins were detected as previously described (Marcotte et al 1999 476

Bowers et al 2004) All protein sequences in the Arabidopsis genome were BLASTPed 477

against the nonredundant (nr) database with an E-value less than 10-10 Two 478

non-homologous proteins were aligned over at least 70 of their sequences to different 479

portions of a third protein and the third protein was referred to as a candidate Rosetta 480

stone protein 481

Although proteins containing highly conserved domains may not be fused they are 482

often linked to each other by the Rosetta stone method To eliminate these confounding 483

Rosetta stone proteins the probability that two proteins are linked was computed by 484

chance alone The probability is given by P as follows 485


minusminus

1

0

x

KN

xKMN

xM

486

where x is the number of candidate Rosetta stone proteins K and M are the numbers of 487


19

homologues of proteins A and B respectively and N is the total number of sequences in 488

the nr database The negative log p-value of the Rosetta stone protein is used for 489

prediction 490

Random Forest Classifier 491

The positive and negative reference sets with 11 features were used to train random 492

forest classifier in R with the setting 500 trees (Liaw and Wiener 2002) All protein 493

pairs were then classified by the trained random forest model for PPI prediction The 494

predicted PPI network was drawn using Cytoscape (Cline et al 2007) 495

Measurement of Classification Performance 496

The positive and negative reference sets were randomly divided into ten subsets of 497

equal size Each time nine subsets were used for classifier training and the remaining 498

subset was used to test the trained classifier This procedure was repeated ten times 499

using different subsets for training and testing and a prediction for each interaction was 500

finally generated The number of TP (true positive) FP (false positive) TN (true 501

negative) and FN (false negative) predictions were counted respectively TPR (recall) = 502

TP(TP﹢FN) FPR = FP(FP﹢TN) precision = TP(TP﹢FP) F1 score = 2timesprecision 503

timesrecall( precision + recall) and the area under the curve (AUC) values were calculated 504

against the receiver operating characteristic (ROC) curves 505

Comparison of AraPPINet with Other Predicted Interactomes 506

The performance of AraPPINet was compared with those of three published PPI 507

prediction methods The AtPID dataset was retrieved from the AtPID database (version 508

4) (Cui et al 2008) The AtPIN dataset was downloaded from the AtPIN database 509

(Brandatildeo et al 2009) The latest PAIR dataset was provided by Dr Chen (version 3) 510

(Lin et al 2011) The random PPI networks were generated based on AraPPINet by 511

using an edge-shuffling method which randomly rewired edges while preserving the 512

original degree distribution of AraPPINet 513

514


20

Functional Enrichment Analysis 515

We used plant GO slim categories to characterize the functions of interacting proteins 516

The statistical enrichment of proteins in a GO category was obtained by comparing the 517

proteins to those in AraPPINet as well as the Arabidopsis genome using Fisherrsquos exact 518

test in blast2go (Conesa and Goumltz 2008) 519

Similarly KEGG pathway enrichment analysis of proteins was performed using 520

Fisherrsquos exact test implemented in a Perl script against a reference dataset of AraPPINet 521

and the Arabidopsis genome 522

Identification of Hormone-Responsive Genes 523

The identification of hormone-responsive genes was based on the normalized 524

expression profiling data (submission number ME00333 ME00334 ME00344 525

ME00337 ME00336 ME00343 and ME00335) of Arabidopsis seedlings from the 526

TAIR Gene Expression Omnibus The average signal intensities of biological replicates 527

for each sample were used to calculate the fold change of gene expression and 528

differentially expressed genes were identified as having a minimum fold change of two 529

Yeast Two-Hybrid Assay 530

Plasmids were constructed using Gateway Cloning Technology (Invitrogen) Insertions 531

of PYL1 ABI5 and the coding sequences of candidate genes were prepared using 532

pENTRD-TOPO The bait vector pDEST32 and the prey vector pDEST22 were used 533

for the yeast two-hybrid assay Sets of bait and prey constructs were co-transformed into 534

the AH109 yeast strain The transformed yeast cells were selected on synthetic minimal 535

double dropout medium deficient in Trp and Leu Protein interaction tests were assessed 536

on triple dropout medium deficient in Trp Leu and His At least six clones were 537

analysed with three repeats and generated similar results 538

BiFC Analysis 539

The BiFC vectors pEarleyagte201-YN and pEarleygate202-YC were kindly provided by 540

Professor Steven J Rothstein for analysis RAV1 was fused to the C-terminal (amino 541


21

acids 175-239) of the YFP protein (YFPC) in the vector pEG202YC which generated 542

the RAV1-YFPC protein ABI5 was fused to the N-terminal (amino acids 1ndash174) of the 543

YFP protein (YFPN) in the vector pEG201YN which generated the ABI5-YFPN 544

protein The ABI5-YFPN RAV1-YFPC and empty pEG201-YFPN constructs were 545

introduced into Agrobacterium tumefaciens strain GV3101 Pairs were co-infiltrated 546

into Nicotiana benthamiana YFP signal was observed after infiltration from 48 to 60 547

hours by confocal laser scanning microscopy (Leica TCS SP5-II) Each observation was 548

repeated at least three times 549

Plant Materials and Root Growth Assay 550

Arabidopsis thaliana Columbia (Col-0) was used as the wild-type plant The T-DNA 551

insertion mutant line arr11112 (CS6986) was kindly provided by Dr Jiawei Wang For 552

the root growth assay seeds of different genotypes were surface-sterilized and then 553

maintained in the dark for 3 days at 4 degC to disrupt dormancy The seeds were sowed 554

onto MS medium containing 15 agar To investigate the inhibition of root growth by 555

cytokinin and ABA 5-day-old seedlings were transferred onto plates supplemented with 556

6-BA and ABA (Sigma) The primary root growth of seedlings was measured five days 557

after transfer to the plates 558

559

560

SUPPLEMENTAL DATA 561

Figure S1 Global view of AraPPINet with the top six assigned modules containing 562

at least 200 node proteins 563

Figure S2 Coverage of features for interactions in AraPPINet 564

Figure S3 Topological properties of AraPPINet A) Degree distribution of the node 565

proteins B) Average clustering coefficient of proteins with the same degree 566

Figure S4 Comparisons of performance for methods using different sources of 567

information A) True positive rates of the methods from three different data sources B) 568

False positive rate of the methods from three different data sources Error bars represent 569


22

the standard error of the mean 570

Figure S5 Partial ROC curves for PPIs predicted based on different sources of 571

information 572

Figure S6 Heatmap of genes within the ABA signaling network in response to 573

plant hormones at different times 574

Table S1 Features of protein-protein pairs in Arabidopsis 575

Table S2 Enriched GO functional categories of interacting proteins within the 576

ABA signaling network 577

Table S3 Enriched KEGG pathways of interacting proteins within the ABA 578

signaling network 579

Table S4 Predicted PPIs were screened with the yeast two-hybrid assay 580

Table S5 Presence of PPIs in Arabidopsis from various databases 581

Table S6 The gold standard PPI data from various databases 582

Table S7 Availability of PPIs in six model organisms from various databases 583

584

585

ACKNOWLEDGMENTS 586

We are grateful to Dr Yi Huang (Oil Crops Research Institute Chinese Academy of 587

Agricultural Sciences) for helpful discussions and for critical reading of the manuscript 588

We appreciate the support of the computing facility in the PI cluster at Shanghai Jiao 589

Tong University We thank Jiawei Wang (Institute of Plant Physiology and Ecology 590

Chinese Academy of Sciences) for kindly providing arr11112 T-DNA insertion mutant 591

(CS6986) seeds We also thank Professor Steven J Rothstein (University of Guelph) for 592

providing the BiFC vectors 593

594

595

596

597


23

TABLES 598

Table 1 Comparison of different prediction approaches with F1 score 599

The average true positive of random network is calculated from 10 randomized networks 600

Precision = predicted PPIs with experimental evidencesall predicted PPIs and recall = 601

predicted PPIs with experimental evidences all experimentally determined PPIs 602

Method Predicted PPIs with experimental evidences

All predicted PPIs

All experimentally determined PPIs

Precision Recall F1 score

RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613

Figure 1 Evaluation of AraPPINet performance A) ROC curves for PPIs inferred 614

from different data sources AUC values are reported in parentheses B) Venn diagram 615

of predicted overlapping PPIs with experimentally determined interactions C) 616

Comparison of AraPPINet with other methods based on the high-throughput PPI dataset 617

D) Comparison of AraPPINet with other methods based on newly reported interactions 618

The random PPI networks are generated by randomly rewiring edges while preserving 619

the original degree distribution of AraPPINet The average TPR is calculated from 10 620

randomized networks 621

622

Figure 2 Evaluation of AraPPINet for predicting interactions between similar 623

protein pairs A) Comparison of performance for predicting interactions between 624

similar protein pairs The boxplots indicate inter-quartile ranges of these data The bar in 625

each boxplot indicates the median B) ROC curves for AraPPINet-based predictions for 626

the MADS BZIP and ARFIAA families AUC values are reported in parentheses C) 627

Venn diagrams of the predicted protein interactions overlapping with the experimentally 628

determined interactions of the MADS BZIP and ARFIAA families 629

630

Figure 3 The ABA signaling network in AraPPINet A) ABA signaling network 631

inferred from AraPPINet Node size represents a node degree Core ABA signaling 632

proteins are represented in red nodes and their interacting partners are represented in 633

green nodes The predicted protein interactions are shown in grey lines and 634

experimentally demonstrated interactions are shown in red lines B) Comparison of 635

AraPPINet with other methods for discovering PPIs in ABA signaling based on the 636

high-throughput PPI dataset C) Comparison of AraPPINet with other methods for 637

discovering PPIs in ABA signaling based on newly reported interactions D) Enriched 638

GO functional categories and E) enriched KEGG pathways of proteins interacting with 639

core ABA signaling proteins F) Enriched ABA-responsive genes within the ABA 640



25

642

Figure 4 Yeast two-hybrid and BiFC assays for detecting interacting partners of 643

PYL1 or ABI5 A) Two-hybrid validation in yeast of the interactions between PYL1 644

and ARR1 (AT3G16857) as well as between ABI5 and TGA5 (AT5G06960) ELIP 645

(AT3G22840) RAV1 (AT1G13260) and DDF1 (AT1G12610) BD indicates the fusion 646

protein with the Gal4 BD domain AD indicates the fusion protein with the Gal4 AD 647

domain GAD or GBD was used as a negative control +His indicates synthetic dropout 648

medium deficient in Leu and Trp -His indicates synthetic dropout medium deficient in 649

Leu Trp and His B) BiFC assay for ABI5 and RAV1 35SABI5-YFPN and 650

35SRAV1-YFPC constructs were co-infiltrated into tobacco leaves YFP signal 651

intensity was detected from 48 h to 60 h after infiltration 652

653

Figure 5 Structural models of PPIs A) Structural interaction model for ARR1 and 654

PYL1 Considering the structural template (PDB structure 4G6V) of the 655

contact-dependent growth inhibition toxinimmunity complex (chain A and B) the 656

homology models of ARR1 and PYL1 were structurally superimposed B) Structural 657

interaction model for DDF1 and ABI5 based on the template complex (PDB structure 658

1RB6) C) Structural interaction model for RAV1 and ABI5 based on the template 659

complex (PDB structure 1IJ2) D) Structural interaction model for ELIP and ABI5 based 660

on the template complex (PDB structure 2WUK) E) Structural interaction model for 661

TGA5 and ABI5 based on the template complex (PDB structure 2Z5I) The homology 662

models of PYL1 and ABI5 are shown in blue the models for the interacting protein 663

partners are shown in red and the template complexes are shown in grey The conserved 664

interfaces in the van der Waals representation are shown in matching colours 665

666

Figure 6 arr11112 mutant seedlings are insensitivity to the inhibition of CK and 667

ABA on primary root growth A) Representative examples of wild-type and 668

arr11112 seedlings on MS medium plates or plates with either 10 microM 6-BA or 10 microM 669

ABA Five-day-old seedlings grown on MS medium were transferred to MS plates or 670

plates supplemented with either 6-BA or ABA The pictures were taken five days after 671


26

the transfer (scale bar = 10 mm) B) Primary root lengths of wild-type and arr11112 672

seedlings at five days after transfer to MS media plates or plates with either 6-BA or 673

ABA The data represent the mean values and SE (n gt 15) The asterisk indicates that 674

the primary root length of arr11112 is significantly longer than that of wild-type on 675

MS medium plates with either 6-BA or ABA (Students t-test p lt 005) C) Schematic 676

representations of the interactions between ABA and cytokinin signaling mediated by 677

ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

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phosphatase Science 2641448-1452 769

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29 Lin M Zhou X Shen X Mao C Chen X (2011) The predicted Arabidopsis 775

interactome resource and network topology-based systems biology analyses Plant 776

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30 Magome H Yamaguchi S Hanada A Kamiya Y Oda K (2008) The DDF1 778

transcriptional activator upregulates expression of a gibberellin-deactivating gene 779

GA2ox7 under high-salinity stress in Arabidopsis Plant J 56613-626 780

31 Marcotte EM Pellegrini M Ng HL Rice DW Yeates TO Eisenberg D (1999) 781

Detecting protein function and protein-protein interactions from genome sequences 782

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32 Matthews LR Vaglio P Reboul J Ge H Davis BP Garrels J Vincent S Vidal M 784

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searches for conserved protein-protein interactions or interologs Genome Res 786


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33 Mittal A Gampala SS Ritchie GL Payton P Burke JJ Rock CD (2014) Related to 788

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coexpression in cotton enhances drought stress adaptation Plant biotechnol j 12 790

578-589 791

34 Mosca R Ceacuteol A Aloy P (2013) Interactome3D adding structural details to 792

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35 Orchard S Ammari M Aranda B Breuza L Briganti L Broackes-Carter F 794

Campbell NH Chavali G Chen C del-Toro N Duesbury M Dumousseau M 795

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Licata L Lovering RC Meldal B Melidoni AN Milagros M Peluso D Perfetto L 797

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input into robust patterning at the shoot apex Mol Syst Biol 7508 835

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Nature 417399-403 838

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ABA suppression to seed germination of Arabidopsis by downregulating ABI5 843

expression Plant J 68249-261 844


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Arabidopsis interactome module database and related interologs in plants Database 846

(Oxford) bau117 847

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of protein interaction partners using physical docking Mol Syst Biol 7469 849

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J Mol Biol 381487-507 852

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transcription factors TGA2 TGA5 and TGA6 reveals their redundant and essential 860

roles in systemic acquired resistance Plant Cell 152647-2653 861


Figure 1 Evaluation of AraPPINet performance A) ROC curves for PPIs inferred

from different data sources AUC values are reported in parentheses B) Venn diagram

of predicted overlapping PPIs with experimentally determined interactions C)

Comparison of AraPPINet with other methods based on the high-throughput PPI data

set D) Comparison of AraPPINet with other methods based on newly reported

interactions The random PPI networks are generated by randomly rewiring edges while

preserving the original degree distribution of AraPPINet The average TPR is calculated

from 10 randomized networks


Figure 2 Evaluation of AraPPINet for predicting interactions between similar

protein pairs A) Comparison of performance for predicting interactions between

similar protein pairs The boxplots indicate inter-quartile ranges of these data The bar in

each boxplot indicates the median B) ROC curves for AraPPINet-based predictions for

the MADS BZIP and ARFIAA families AUC values are reported in parentheses C)

Venn diagrams of the predicted protein interactions overlapping with the experimentally

determined interactions of the MADS BZIP and ARFIAA families


Figure 3 The ABA signaling network in AraPPINet A) ABA signaling network

inferred from AraPPINet Node size represents a node degree Core ABA signaling

proteins are represented in red nodes and their interacting partners are represented in

green nodes The predicted protein interactions are shown in grey lines and

experimentally demonstrated interactions are shown in red lines B) Comparison of

AraPPINet with other methods for discovering PPIs in ABA signaling based on the

high-throughput PPI data set C) Comparison of AraPPINet with other methods for

discovering PPIs in ABA signaling based on newly reported interactions D) Enriched

GO functional categories and E) enriched KEGG pathways of proteins interacting with

core ABA signaling proteins F) Enriched ABA-responsive genes within the ABA

signaling network


Figure 4 Yeast two-hybrid and BiFC assays for detecting interacting partners of

PYL1 or ABI5 A) Two-hybrid validation in yeast of the interactions between PYL1

and ARR1 (AT3G16857) as well as between ABI5 and TGA5 (AT5G06960) ELIP

(AT3G22840) RAV1 (AT1G13260) and DDF1 (AT1G12610) BD indicates the fusion

protein with the Gal4 BD domain AD indicates the fusion protein with the Gal4 AD

domain GAD or GBD was used as a negative control +His indicates synthetic dropout

medium deficient in Leu and Trp -His indicates synthetic dropout medium deficient in

Leu Trp and His B) BiFC assay for ABI5 and RAV1 35SABI5-YFPN and

35SRAV1-YFPC constructs were co-infiltrated into tobacco leaves YFP signal

intensity was detected from 48 h to 60 h after infiltration


Figure 5 Structural models of PPIs A) Structural interaction model for ARR1 and

PYL1 Considering the structural template (PDB structure 4G6V) of the

contact-dependent growth inhibition toxinimmunity complex (chain A and B) the

homology models of ARR1 and PYL1 were structurally superimposed B) Structural

interaction model for DDF1 and ABI5 based on the template complex (PDB structure

1RB6) C) Structural interaction model for RAV1 and ABI5 based on the template

complex (PDB structure 1IJ2) D) Structural interaction model for ELIP and ABI5 based

on the template complex (PDB structure 2WUK) E) Structural interaction model for

TGA5 and ABI5 based on the template complex (PDB structure 2Z5I) The homology

models of PYL1 and ABI5 are shown in blue the models for the interacting protein

partners are shown in red and the template complexes are shown in grey The conserved

interfaces in the van der Waals representation are shown in matching colours


Figure 6 arr11112 mutant seedlings are insensitivity to the inhibition of CK and

ABA on primary root growth A) Representative examples of wild-type and

arr11112 seedlings on MS medium plates or plates with either 10 microM 6-BA or 10 microM

ABA Five-day-old seedlings grown on MS medium were transferred to MS plates or

plates supplemented with either 6-BA or ABA The pictures were taken five days after

the transfer (scale bar = 10 mm) B) Primary root lengths of wild-type and arr11112

seedlings at five days after transfer to MS media plates or plates with either 10 microM

6-BA or 10 microM ABA The data represent the mean values and SE (n gt 15) The asterisk

indicates that the primary root length of arr11112 is significantly longer than that of

wild-type on MS medium plates with either 6-BA or ABA (Students t-test p lt 005) C)

Schematic representations of the interactions between ABA and cytokinin signaling

mediated by ARR1 and PYL1


Parsed CitationsAltschul SF Madden TL Schaumlffer AA Zhang J Zhang Z Miller W Lipman DJ (1997) Gapped BLAST and PSI-BLAST a newgeneration of protein database search programs Nucleic Acids Res 253389-3402

Pubmed Author and TitleCrossRef Author and TitleGoogle Scholar Author Only Title Only Author and Title

Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana Nature408796-815


Arabidopsis Interactome Mapping Consortium (2011) Evidence for network evolution in an Arabidopsis interactome map Science333601-607


Ashburner M Ball CA Blake JA Botstein D Butler H Cherry JM Davis AP Dolinski K Dwight SS Eppig JT Harris MA Hill DPIssel-Tarver L Kasarskis A Lewis S Matese JC Richardson JE Ringwald M Rubin GM Sherlock G (2005) Gene ontology toolfor the unification of biology Nat Genet 2525-29


Aytuna AS Gursoy A Keskin O (2005) Prediction of protein-protein interactions by combining structure and sequenceconservation in protein interfaces Bioinformatics 212850-2855


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Figure 4

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Figure 6

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2

AUTHOR CONTRIBUTIONS 24

FZ SL and LL carried out data analyses and the experiments KZ and LZ 25

contributed experimental materials and advice on the experiments LZ conceived of the 26

project supervised the research and wrote the manuscript All authors approved the 27

manuscript 28

29

This work was supported by the National Basic Research Program of China (Grant No 30

2013CB733903-03) and the National Natural Science Foundation of China (Grant No 31

31371229) 32

33

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43


3



acid signaling 46

47

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4

ABSTRACT 62
















78

79

80

81

82

83

84

85

86

87

88

89

90


5

INTRODUCTION 91






























6






















plants 141

RESULTS 142







7































8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

3



acid signaling 46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61


4

ABSTRACT 62
















78

79

80

81

82

83

84

85

86

87

88

89

90


5

INTRODUCTION 91






























6






















plants 141

RESULTS 142







7































8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

4

ABSTRACT 62
















78

79

80

81

82

83

84

85

86

87

88

89

90


5

INTRODUCTION 91






























6






















plants 141

RESULTS 142







7































8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

5

INTRODUCTION 91






























6






















plants 141

RESULTS 142







7































8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

6






















plants 141

RESULTS 142







7































8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

7































8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

8































9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

9




















Arabidopsis 225











10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

10





pathway 239


























11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

11































12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

12



et al 2014) 295














approaches 309














13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

13









DISCUSSION 330






















14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

14


using AraPPINet 352






















in plants 374

METHODS 375





15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

15

















classifier 395














16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

16









models 416






















17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

17





)(1

=

times=N

iii ISbISaS 441
























18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

18








minusminus

1

0

x

KN

xKMN

xM

470











stone protein 481






minusminus

1

0

x

KN

xKMN

xM

486



19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

19



prediction 490
























514


20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

20

























BiFC Analysis 539




21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

21


















559

560











22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

22



information 572












584

585

ACKNOWLEDGMENTS 586








594

595

596

597


23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

23

TABLES 598






All predicted PPIs



RandNet 348 316747 21282 0001 0016 0002

AtPID 386 24418 21282 0016 0018 0017

AtPIN 449 90043 21282 0005 0021 0008

PAIR 1995 145355 21282 0014 0094 0024

AraPPINet 6040 316747 21282 0019 0284 0036

603

604

605

606

607

608

609

610

611

612


24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

24

FIGURE LEGENDS 613









622








630













25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

25

642











653













666







26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

26







ARR1 and PYL1 678

679

680

681

682

683

684

685

686

687

688

REFERENCES 689












27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

27
































28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

28






























29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

29














218-22 771






Cell 23911-922 777






Science 285751-753 783





30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

30

112120-2126 787




578-589 791










42D358-363 801



962896-2901 804










Proteins 78109-117 814



31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

31



23951-959 818



New Phytol 190896-905 821







2941519-1521 828










Nature 417399-403 838



1581523-1533 841





32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

32



(Oxford) bau117 847





J Mol Biol 381487-507 852




490556-560 856


































signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations




























signaling network





















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations




















































































































































Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Parsed Citations

genome-wide inference of protein interaction network and ...5 91 introduction 92 protein-protein...

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