kinase-kernel models: accurate chemogenomic method for the entire human kinome

DOI: 10.1002/minf.201300091

Kinase-Kernel Models: Accurate Chemogenomic Methodfor the Entire Human KinomeLi Tian,[a] Prasenjit Mukherjee,[b] and Eric Martin*[a]

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922 � 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Mol. Inf. 2013, 32, 922 – 928

Review

1 Introduction

This review describes “kinase-kernel” virtual screeningmodels which predict kinase activity with unprecedentedaccuracy knowing nothing but the compound structureand the kinase protein sequence. The high cost and longtime required for experimental high-throughput screening

(HTS) cries out for fast, inexpensive computational alterna-tives. Protein Family Virtual Screening (PFVS) is comprisedof 3 virtual screen methods, as shown in Figure 1: Surro-gate AutoShim,[1,2] Profile-QSAR,[3] and kinase-kernel.[4]

These methods predict kinase IC50s for large, diverse chemi-cal libraries, with unprecedented speed and accuracy, bymany measures on par with experimental HTS. SurrogateAutoShim and Profile-QSAR models are both trained on

several hundred IC50s: Surrogate AutoShim by trainingtarget customized docking scoring functions which are ap-plied to 4 million compounds that were pre-docked intoa “universal surrogate ensemble receptor”[2] and Profile-QSAR by training a meta-QSAR model based on a few hun-dred IC50s for the new kinase and activity predictions frombinary Bayesian models as chemical descriptors derived

[a] L. Tian, E. MartinOncology and Exploratory Chemistry, Global Discovery Chemistry,Novartis Institutes for Biomedical Research4560 Horton Street, Emeryville, CA 94608fax: + 1(510)655-9910*e-mail : [email protected]

[b] P. MukherjeePresent Address: Boehringer Ingelheim Pharmaceuticals, Inc.900 Ridgebury Rd., Ridgefield, CT 06877

Abstract : Chemogenomic kinase-kernel virtual screeningmodels interpolate between very accurate, empirically-trained Profile-QSAR models of the nearest binding-site ho-

mologues with IC50 assay data. Between them, activity hasbeen predicted for the entire human kinome.

Keywords: Kernel · Chemogenomics · Kinomics · Virtual screening · Chemoinformatics

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Figure 1. An overview of Protein-Family Virtual Screening (PFVS). It is comprised of 3 virtual screening methods: Surrogate AutoShim, Pro-file-QSAR, and kinase-kernel. The Profile-QSAR method trains meta-QSAR models using the activity predictions from Bayesian modelstrained on all available assays in the family as the chemical descriptors. Thus, millions of historical IC50s inform each Profile-QSAR predictionfor unprecedented accuracy. However, it does require at least a few hundreds of IC50s for each new target as training data. Surrogate Au-toShim uses millions of pre-docked available compounds in an ensemble of diverse surrogate kinase or protease crystal structures. Pharma-cophoric “shims” in the binding sites are adjusted to reproduce IC50 data for each new kinase or protease. The result is highly accurate ac-tivity predictions in just hours rather than the weeks usually required for docking. The kinase-kernel method can interpolate predictions fora new kinase from binding-site homologue neighbors in the kinome without training data, and thus can cover the remainder of humankinome for which training data are not available.

Mol. Inf. 2013, 32, 922 – 928 � 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim www.molinf.com 923

Kinase-Kernel Models: Entire Human Kinome

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from millions of historical IC50s from previous kinase proj-ects.[3] Profile-QSAR models have now been trained for over150 kinases with at least several hundred experimental IC50sfor each model. We have developed the chemogenomic“kinase-kernel” models, reviewed here, to address the re-

maining ~350 kinases for which we have no training data.They only require the protein sequences, and predict activi-ty by interpolating between the existing, highly accuratedata-driven Profile-QSAR models. Since kinase-kernelmodels are less accurate than the empirically trained Pro-file-QSAR models on which they are based, they are oftenused to select an initial compound set for IC50 determina-tion, which is then used to train more accurate Profile-QSAR and Surrogate AutoShim models for a final virtualscreen. Kinome-wide activity modeling with heterogeneous,public data sets has also been described.[5] The kinase-kernel approach might similarly be able to extend the ap-plication of these models as well.

Docking is the most common “a priori” virtual screeningmethod. However, docking requires a protein structure, itsaffinity predictions are very poor,[6] and it is a slow expen-sive calculation. Two factors suggested that interpolationbetween highly accurate Profile-QSAR models might effec-tively predict affinity with better accuracy and speed, andwithout a protein structure: 1) existing Profile-QSAR modelscovered most branches of the sequence-based Sugenkinome tree as shown in Figure 2; and 2) the triangularpoint cloud in the plot (Figure 3) of sequence similarity vs.structure activity relationship (SAR) similarity shows that ac-tivity of a compound against a given kinase is similar to itsnear neighbors, although the reverse is not generally true.This suggested that a kernel method[7,8] might predict activ-ities for rest of the kinases as weighted averages of the Pro-file-QSAR predictions for the nearest kinase neighbors.

2 State-of-the-Art

Whole-domain sequence similarity indicates evolutionaryrelationships rather than binding site similarity. Therefore,we only included binding-site residue identity in our neigh-bor sequence comparison. Because of binding-site flexibili-ty, three pre-existing binding-site residue definitions, wereoriginally explored, both to identify the near neighbor kin-ases, and also as weights in the average. These includeda set balanced between residues from both the ATP pocketand the back pocket, a set biased for ATP pocket residues,and a set biased for back pocket residues. Two additionalproperties were considered as weighting factors: the accu-racy of each neighbor’s Profile-QSAR model measured as 5-fold Q2, and the average Tanimoto similarity of the newcompound to be predicted to the five closest analogs inthe training set of that neighbor’s Profile-QSAR model. Theoptimal number of neighbors and the rate of attenuationwith binding-site similarity were also studied.

The best kernel models used a binding-site definitionbiased toward the active conformation and attenuatedtheir contributions steeply (cubic) with decreasing binding-site similarity. The relative contributions of the 3 weightingfactors (ligand similarity, site-similarity, and model quality)were optimized, using a 3-way mixture design,[9] to best

Li Tian obtained her PhD degree withProf. Richard Friesner on QM/MMstudy of P450 catalysis mechanismand developing empirical correctionparameters in DFT calculation. She didher first postdoc with Prof. Edward So-lomon on spectroscopy study andquantum chemistry simulation of Cuproteins and model complexes thatmimic the protein active site. She iscurrently working for Dr. Eric Martin atNovartis on protein family based virtu-al screening methods. Her research isfocused on expanding the methods to other drug target familiesbesides kinases, extending the methods to fragment based virtualscreening and lead optimization, as well as applications.

Prasenjit Mukherjee obtained his PhDin Medicinal Chemistry from the Uni-versity of Mississippi working on com-puter aided drug design approachesto identify inhibitors for the SARS-3Clpro viral protease and the Bcl-2protein-protein interaction. He thenwent onto his post-doctoral workunder Dr. Eric Martin in the computa-tional chemistry group at Novartisworking on the development of Pro-tein-Family Virtual Screening (PFVS)methods and applications in kinaseand protease projects in various stages of discovery. Following hispost-doctoral work he went into a project support computationalchemist role at Astra-Zeneca and is currently in the Structural Re-search group at Boehringer-Ingelheim.

Eric Martin has a Ph.D. in physical or-ganic chemistry from Yale University.He has worked in computationalchemistry, analytical instrument devel-opment, environmental-fate modeling,drug design and herbicide design. Heis known for starting the field of com-binatorial library design in 1993. Hisrecent Protein-Family Virtual Screening(PFVS) methods achieve accuracy com-parable to experimental HTS by mod-eling individual drug targets as mem-bers of a family. Initially for kinases,PFVS has now been extended to proteases, GPCRs, CYPs and non-kinase adenosine binding proteins, covering half the estimateddruggable genome. PFVS has been applied to over 50 internal No-vartis targets. He was recently awarded the lifetime title of NovartisLeading Scientist.

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924 www.molinf.com � 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Mol. Inf. 2013, 32, 922 – 928

Review L. Tian et al.

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predict the median correlation (R2) between experimentand prediction for 51 kinase assays. The best modelsweighted binding-site similarity most heavily, followed bytraining-set ligand similarity. Neighbor model Q2 had nobenefit, suggesting that all the Profile-QSAR models aregood enough.

Since binding-site similarity was the most importantfactor, we employed a genetic algorithm (GA)[10] to identifythe optimal subset of the 46 residues from the 3 originalbinding site definitions. The fitness function was againmedian R2 for the 51 kinases. The GA identified many com-parable residue combinations, so rather than arbitrarilytaking the numerically highest, we counted how frequently

each of the 46 residues occurred in the binding site defini-tions from all models with R2>0.54. Figure 4 shows that 16privileged residues occurred in the majority of the bestmodels, followed by a sharp drop in frequency for the re-maining 30 residues. Figure 5 maps these 16 residues ontoa PKA crystal structure, along with references to literaturedocumenting their role in activity and selectivity.[4] These in-clude both the obvious candidates, as well as more subtleresidues involved in P-loop dynamics and the “hydrophobicspine”,[11] but not active-site residues with no apparent role.This lends confidence that the optimized kernel model ac-tually captures the physics of binding, rather than merecurve fitting.

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Figure 2. The distribution of kinases with Profile-QSAR models across the Sugen kinome tree. The gray scale indicates the number of ex-perimental IC50s: from ~600 to ~59 000.



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The mixture design[9] to determine the optimal weightingfactors was repeated using the global best binding-site res-idue definition from the GA optimization. As Figure 6shows, binding site sequence identity is now the onlyuseful weighting factor. Figure 7 compares the performancefor the 51 kinase assays from these optimized kinase-kernel

models to that of Profile-QSAR, Surrogate AutoShim andalso conventional docking with Flo + [12] (only 9 cases withcrystal structures). While the median R2 = 0.55 is not as highas the R2 = 0.62 for the trained Profile-QSAR models onwhich they are based, they bested trained 3D SurrogateAutoShim models. The improvement over conventionaldocking is dramatic. In practice, the correlations have beensomewhat lower, but still outstanding compared to othervirtual screening methods without training data.

When sufficient training data are available, Profile-QSARand Surrogate AutoShim are preferred for kinase virtualscreening. For early stage projects with little or no IC50

data, we typically select about 1000 kinase-kernel predictedactives to assay, and then use these IC50s to train Profile-

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Figure 3. SAR similarity vs. whole kinase domain sequence identity.Each point represents a pair of kinases with experimental IC50s forat least 100 shared compounds. The X-axis is the whole kinasedomain percentage sequence identity. The Y-axis is SAR similarity(correlation between vectors of IC50s for the shared compounds be-tween the kinases).

Figure 4. Frequency of occurrence for each of the 46 residues inthe 234 best kinase-kernel models with median R2>0.54 for 51kinases. The dotted frame indicates the 16 privileged residues withhigh frequency.

Figure 5. The 16 most important residues mapped onto the PKA binding site (PDB code 1QMZ) with descriptions of their roles in a ligand-protein interaction or protein stabilization. PDB codes of crystal structures where such a role is observed are also noted.



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QSAR and Surrogate AutoShim models in a second itera-tion. Since kinase-kernel models are not biased by trainingdata, they can also provide an orthogonal complement to

Profile-QSAR and Surrogate Autoshim. Finally, these modelsare often used to estimate activity against specific anti-tar-gets for which assays are not available.

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Figure 6. The mixture design response surface shows that kinase-kernel models using the global best binding site definition, neighborcount of 7, and cubic distance attenuation perform best without additional terms for model quality or ligand similarity. The color codesgive the range of median R2 values at different combinations of weighting factors.

Figure 7. A plot comparing performance for each of the 51 kinases by the global best kinase-kernel model (Kernel), Profile-QSAR (PQ) , Sur-rogate Autoshim (AS) and the Flo+ docking score (Flo_score) where possible. The Y-axis is external correlation between the experimentand predicted pIC50 values for a 25% held-out test set in decreasing order of kinase-kernel performance.



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A fundamental limitation shared by all untrained predic-tion methods is that activity is a function of specific assayconditions, not just the gene or even the structure of thetarget. Correlation between two assays for the same kinasecan be very low due to differences in protein constructs,substrates, cofactor concentration, phosphorylation state,incubation time, pH, buffers, etc. , factors which are rarelyconsidered in virtual screening calculations. Kinase-kernelmodels work best when assay conditions are similar tothose of the trained neighbor Profile-QSAR models.

Between trained Profile-QSAR models and a priori kinase-kernel models, activity has been predicted and stored forover 4 million internal and drug-like commercially availablecompounds against over 500 kinases. Thus, 2 billion activitypredictions are available by a simple table lookup. If thiscould even be done experimentally, it would cost over $1billion.

3 Outlook

Profile-QSAR models have been applied to over 50 drugdiscovery projects in various stages, including target fish-ing, hit finding, HTS hits triaging, selectivity prediction andin silico profiling, and more, with great success. Kinase-kernel models have also been applied to some early stagedrug discovery projects where very little experimental datais available and have been able to provide initial com-pounds to screen for target validation. Profile-QSAR modelshave now been developed for 3 additional protein families:proteases, GPCRs and CYPs. The performance of Profile-QSAR models for kinases is the highest, comparable to pro-teases (Rext

2 = 0.60), and followed by GPCRs (Rext2 = 0.53)

and CYPs (Rext2 = 0.43) ; this is mainly because there are

much more accumulated historical data for kinases andproteases to train models. We hope to develop kernel

models to predict activity without training data for thesefamilies as well. This would extend virtual screening tocover nearly half of the estimated druggable genome.

Acknowledgements

L. T. would like to thank the NIBR education office for post-doctoral funding.

References

[1] E. J. Martin, D. C. Sullivan, J. Chem. Inf. Model. 2008, 48, 861.[2] E. J. Martin, D. C. Sullivan, J. Chem. Inf. Model. 2008, 48, 873.[3] E. J. Martin, P. Mukherjee, D. C. Sullivan, H. Jansen, J. Chem. Inf.

Model. 2011, 51, 1942.[4] E. Martin, P. Mukherjee, J. Chem. Inf. Model. 2011, 52, 156.[5] S. C. Schurer, S. M. Muskal, J. Chem. Inf. Model. 2013, 53, 27.[6] G. L. Warren, C. W. Andrews, A. M. Capelli, B. Clarke, J. La-

Londe, M. H. Lambert, M. Lindvall, N. Nevins, S. F. Semus, S.Senger, G. Tedesco, I. D. Wall, J. M. Woolven, C. E. Peishoff,M. S. Head, J. Med. Chem. 2006, 49, 5912.

[7] J. Shawe-Taylor, N. Cristianini, Kernel Methods for Pattern Analy-sis, Cambridge University Press, Cambridge, UK, 2004.

[8] W. Liu, J. Principle, S. Haykin, Kernel Adaptive Filtering: A Com-prehensive Introduction, Wiley, Hoboken, NJ, 2010.

[9] R. Mead, The Design of Experiments; Statistical Principles forPractical Applications, Cambridge University Press, Cambridge,UK, 1988.

[10] D. E. Goldberg, Genetic Algorithms in Search, Optimizationand Machine Learning, Addison-Wesley, Reading, MA, 1989.

[11] A. P. Kornev, N. M. Haste, S. S. Taylor, L. F. Ten Eyck, Proc. Natl.Acad. Sci. USA 2006, 103, 17783.

[12] C. McMartin, Boston De Novo Design,http://bostondenovo.com/Allegrow_flo.htm.

Received: May 13, 2013Accepted: September 20, 2013

Published online: December 13, 2013

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kinase-kernel models: accurate chemogenomic method for the entire human kinome

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