Genome-Wide Discovery of Protein-Ligand Interactions by a Combined Computational & Energetic-Based Approach

Daisuke Kihara Department of Biological SciencesDepartment of Computer Science

Purdue University, IN, USA

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http://kiharalab.org

Comprehensive Detection of Protein-Ligand Interactions in a Cell

From single molecules to interactions and networks

Protein-protein interaction networks can be identified by several experimental methods, yeast 2 hybrid, tagged-protein + mass spec

Protein-ligand interaction network (pathways) e.g. KEGG: compilation of individual interactions in literature

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Combined Computational & Experimental Approach

3(Zeng et al., J Proteome Res, 2016)

Patch-Surfer 2.0: Local Patch-Based Pocket Comparison Method

4(Sael & Kihara, Proteins, 2012)

(Zhu, Xiong, & Kihara, Bioinformatics, 2015)

3D Zernike invariants An extension of

spherical harmonics based descriptors

A 3D object can be represented by a series of orthogonal functions, thus practically represented by a series of coefficients as a feature vector

Compact Rotation invariant

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A surface representation of 1ew0A (A) is reconstructed from its 3D Zernike invariants of the order 5, 10, 15, 20, and 25 (B-F). (Sael & Kihara, 2009)

),()(),,( mlnl

mnl YrRrZ

),( mlY )(rRnl

),,( rZ mnl

: Spherical harmonics, : radial functions

polynomials in Cartesian coordinates

143 .)()(

xxxx dZf m

nlmnl Zernike moments:

Zernike invariants: 2)( mnl

lm

lmnlF

Pocket Features to Compare

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• Shape• Electrostatic Potential• Hydrophobicity• Visibility

3DZD for

Approximate Patch Position:Histogram of Geodesic Distance to other Seed Points

The Number of Patches for Several Ligand Binding Pockets

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Non-Redundant Database of Ligand Binding Pockets

Selected from ligand-bound protein structures from PDB

2444 different ligand types 6547 pockets

117 ligands have more than 5 pockets

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Predicting Binding Ligand from Screening Results

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Query pocket

Pocket database

Matched pockets

Ligand of the pocket

1lj8_A NAD

1ebw_AB BEI

3b4y_A F42_FLC

3oa2_ACD NAD

1bxk_A NAD

3c1o_A NAP

1nuq_A DND

2jhf_A NAD

1nyt_B NAP

…… ……

ligand Pocket Score

NAP 22.87

NAD 18.75

NDP 16.55

DND 14.81

ATP 12.75

….. …..

Binding Ligand Prediction Results

Top 5 Top 10 Top 15 Top 20 Top 25117 Ligands 0.254 0.438 0.547 0.611 0.65950 Groups* 0.459 0.628 0.726 0.791 0.835(without flexible ligands)**107 Ligands 0.272 0.472 0.587 0.653 0.69950 Groups 0.487 0.663 0.754 0.810 0.845

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* Ligands are grouped with SIMCOMP ligand structure similarity. At this level, ligands with up to a few atom differences are clustered. E. g. glucose and mannose are grouped but not with sucrose. NAD and NADP are clustered but not with ATP.** After removing 10 ligands with largest flexibility (the average number of rotatable bonds per atom)

Ligand Types with High and Low Accuracies

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darunavirNADPH

Iron-sulfur cluster

Tris-aminomethane

polyethylene glycol

N-acetyl-D-glucosamine

3-Pyridinium-1-Ylpropane-1-Sulfonate

Patch-Surfer Retrieval Results forFlexible Ligands: FAD and NAD

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flavin adenine dinucleotide (FAD) FAD

Nicotinamide adenine dinucleotide(NAD)

1cqx 1jr8 1e8g 1k87 1mi3 1s7g

Patch-based: 3rd Global pocket: 31st

Patch-based: 1st Global pocket: 18th

Patch-based: 2nd Global pocket: 16th

PL-PatchSurfer2: Local Surface-Based Virtual Screening

Shin, Christoffer, Wang, & Kihara, J Chem Inf. Model. (2016)

Benchmark

25 targets from DUD set (Huang et al., 2006) Nuclear receptors: 8 Kinase: 7 Serine protease: 2 Other proteins: 8

~40~360 actives for each target. Active: Decoy ratio is kept to 1:29.

If the library is larger than 3000, 60 actives and 1740 decoy compounds are selected.

Program EF1% EF5% EF10% BEDROC

PL-PatchSurfer 15.47 5.25 3.11 0.310

AutoDock Vina 7.92 5.05 3.37 0.276

AutoDock4 7.36 3.83 2.74 0.226

DOCK6 11.47 4.02 2.47 0.239

ROCS 11.76 5.54 3.52 0.317

Screening Results on the DUD set

Programs EF1% EF5% EF10% BEDROC

PL-P.Surfer 14.69/12.48 5.12/4.55 3.03/2.91 0.30/0.28

DOCK6 12.49/7.69 4.60/3.58 2.86/2.48 0.27/0.21

Vina 7.35/3.86 4.95/2.55 3.39/2.00 0.27/0.16

Difference of Enrichment Factor for 19 Holo/Apo Target Structures

Screening Results Using Structure Models

Methods Structure

EF1% EF5% EF10% BEDROC

PL-PSurfer X-ray 12.86 5.28 3.29 0.31TBM 11.76 5.28 3.35 0.31

Autodock Vina X-ray 8.63 6.14 4.09 0.33TBM 1.68 1.30 1.30 0.09

DOCK6 X-ray 11.70 4.40 2.98 0.26TBM 2.58 1.88 1.58 0.12

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TBM: template-based models

Combined Computational & Experimental Approach

18(Zeng et al., J Proteome Res, 2016)

Identifying NAD Binding Proteins with Pulse-Proteolysis in E.coli Proteome

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• Stabilization from ligand binding leads to a change in protein abundance after pulse proteolysis.

• Digested peptides by pulse proteolysis is filtered out by FASP.

• The change in abundance was measured by tandem mass tags (TMT) labeling coupled quantitative mass spectrometry.

Detected NAD Binding Proteins

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Three urea concentrations,3.5M, 4.0M, and 4.5M were used. Considered as NAD binding if the stability changed by 1.25 fold or larger with/without NAD in 2 or more replicates.

21(Zeng et al., J Proteome Res, 2016)

Predicted NAD binding pose of the eight predicted novel NAD binding proteins

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NAD is colored in cyan and crystal structure of the cognate ligand is shown in magenta.

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(2016)

Summary Patch-Surfer2.0 compares a query pocket against a

database of known ligand binding pockets and predicts binding ligands for the pocket.

PL-PatchSurfer2 compares a pocket directly against ligand molecules. Tolerant to small difference of conformations of

molecules Combined with Pulse-proteolysis to identify novel

NAD binding proteins in the E. coli proteome.

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AcknowledgementsW. Andy Tao (Purdue) Lingfei Zeng

25@kiharalab

Woong-Hee Shin

Chiwook Park (Purdue) Nathan Gardner

Lyman Monroe