Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. We have developed “iDTPnd”, a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive and a negative structural signature that captures the weakly conserved structural features of drug binding sites. To facilitate assessment of unintended targets iDTPnd also provides a docking-based interaction score and its statistical significance. We were able to confirm the interaction of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity and specificity of 52% and 55% respectively. We have validated 10 predicted novel targets, using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450 or MHC Class I molecules can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein-small molecule interactions, when sufficient drug-target 3D data are available.

[1]  S. Rees,et al.  Principles of early drug discovery , 2011, British journal of pharmacology.

[2]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[3]  J. Zheng,et al.  Crystal structure of the catalytic subunit of cyclic adenosine monophosphate-dependent protein kinase. , 1991, Science.

[4]  E. Hewitt.  The MHC class I antigen presentation pathway: strategies for viral immune evasion , 2003, Immunology.

[5]  V. Craig Jordan,et al.  International Union of Pharmacology. LXIV. Estrogen Receptors , 2006, Pharmacological Reviews.

[6]  T. Habuchi,et al.  Risk factors for sorafenib-induced high-grade skin rash in Japanese patients with advanced renal cell carcinoma , 2013, Anti-cancer drugs.

[7]  Jie Liang,et al.  Structural signatures of enzyme binding pockets from order-independent surface alignment: a study of metalloendopeptidase and NAD binding proteins. , 2011, Journal of molecular biology.

[8]  L. Johnson,et al.  Structural basis for control by phosphorylation. , 1997, Chemical reviews.

[9]  Guoli Wang,et al.  PISCES: recent improvements to a PDB sequence culling server , 2005, Nucleic Acids Res..

[10]  Barry Honig,et al.  Structure-based prediction of ligand–protein interactions on a genome-wide scale , 2017, Proceedings of the National Academy of Sciences.

[11]  B. Merget,et al.  Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay. , 2017, Journal of medicinal chemistry.

[12]  Ilya A. Vakser,et al.  DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking , 2011, BMC Bioinformatics.

[13]  M. Schwab,et al.  Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. , 2013, Pharmacology & therapeutics.

[14]  J. Arrowsmith.  Trial watch: Phase II failures: 2008–2010 , 2011, Nature Reviews Drug Discovery.

[15]  L. Meijer,et al.  ATP-site directed inhibitors of cyclin-dependent kinases. , 1999, Current medicinal chemistry.

[16]  P. Brossart,et al.  Sorafenib, but not sunitinib, affects function of dendritic cells and induction of primary immune responses. , 2008, Blood.

[17]  Yuan-Tsong Chen,et al.  Direct interaction between HLA-B and carbamazepine activates T cells in patients with Stevens-Johnson syndrome. , 2012, The Journal of allergy and clinical immunology.

[18]  J. Skolnick,et al.  Comprehensive prediction of drug-protein interactions and side effects for the human proteome , 2015, Scientific Reports.

[19]  René Bernards,et al.  Feedback and redundancy in receptor tyrosine kinase signaling: relevance to cancer therapies. , 2014, Trends in biochemical sciences.

[20]  The Uniprot Consortium,et al.  UniProt: a hub for protein information , 2014, Nucleic Acids Res..

[21]  Min Huang,et al.  Molecularly targeted cancer therapy: some lessons from the past decade. , 2014, Trends in pharmacological sciences.

[22]  Luzhe Sun,et al.  Extranuclear functions of ER impact invasive migration and metastasis by breast cancer cells. , 2010, Cancer research.

[23]  Jie Liang,et al.  CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues , 2006, Nucleic Acids Res..

[24]  K. Knudsen,et al.  Nuclear targeting of cyclin-dependent kinase 2 reveals essential roles of cyclin-dependent kinase 2 localization and cyclin E in vitamin D-mediated growth inhibition. , 2010, Endocrinology.

[25]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[26]  S. Baker,et al.  Interaction of the Multikinase Inhibitors Sorafenib and Sunitinib with Solute Carriers and ATP-Binding Cassette Transporters , 2009, Clinical Cancer Research.

[27]  M. Clausen,et al.  FDA-approved small-molecule kinase inhibitors. , 2015, Trends in pharmacological sciences.

[28]  Darrell R. Abernethy,et al.  International Union of Pharmacology: Approaches to the Nomenclature of Voltage-Gated Ion Channels , 2003, Pharmacological Reviews.

[29]  Shaoyong Lu,et al.  Allosteric Modulator Discovery: From Serendipity to Structure-Based Design. , 2019, Journal of medicinal chemistry.

[30]  Bailing Zhang,et al.  Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble , 2011, BMC Bioinformatics.

[31]  Steven J. M. Jones,et al.  A Computational Approach to Finding Novel Targets for Existing Drugs , 2011, PLoS Comput. Biol..

[32]  M. McDevitt,et al.  A pharmacodynamic study of sorafenib in patients with relapsed and refractory acute leukemias , 2010, Leukemia.

[33]  W. Ma,et al.  Novel Agents on the Horizon for Cancer Therapy , 2009, CA: a cancer journal for clinicians.

[34]  Krister Wennerberg,et al.  Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth. , 2015, ACS chemical biology.

[35]  T. Golub,et al.  Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. , 2006, Cancer cell.

[36]  Aurélien Grosdidier,et al.  SwissDock, a protein-small molecule docking web service based on EADock DSS , 2011, Nucleic Acids Res..

[37]  Joel Dudley,et al.  Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets , 2010, PLoS Comput. Biol..

[38]  M. Kitagawa,et al.  Role of p27Kip1 and cyclin-dependent kinase 2 in the proliferation of non-small cell lung cancer. , 1998, The American journal of pathology.

[39]  J. Snyder,et al.  The development of a selective cyclin-dependent kinase inhibitor that shows antitumor activity. , 2009, Cancer research.

[40]  Alexander E. Ivliev,et al.  Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach , 2013, PloS one.

[41]  Ozlem Keskin,et al.  A Strategy Based on Protein-Protein Interface Motifs May Help in Identifying Drug Off-Targets , 2012, J. Chem. Inf. Model..

[42]  Philip E. Bourne,et al.  Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model , 2010, PLoS Comput. Biol..

[43]  María Martín,et al.  UniProt: A hub for protein information , 2015 .

[44]  J. Arrowsmith.  Trial watch: Phase III and submission failures: 2007–2010 , 2011, Nature Reviews Drug Discovery.

[45]  J. Adams,et al.  Kinetic and catalytic mechanisms of protein kinases. , 2001, Chemical reviews.

[46]  Tao Jiang,et al.  NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions , 2018, bioRxiv.

[47]  Yen-Kung Chen,et al.  Pazopanib Sensitivity in a Patient With Breast Cancer and FGFR1 Amplification. , 2017, Journal of the National Comprehensive Cancer Network : JNCCN.

[48]  Michael J. Keiser,et al.  Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.

[49]  Guanghui Hu,et al.  Human Disease-Drug Network Based on Genomic Expression Profiles , 2009, PloS one.

[50]  Hao Ding,et al.  Similarity-based machine learning methods for predicting drug-target interactions: a brief review , 2014, Briefings Bioinform..

[51]  U. Wolfrum,et al.  Estrogen receptor α regulates non-canonical autophagy that provides stress resistance to neuroblastoma and breast cancer cells and involves BAG3 function , 2015, Cell Death and Disease.

[52]  L. Johnson,et al.  Protein Kinase Inhibitors: Insights into Drug Design from Structure , 2004, Science.

[53]  Eric K. Neumann,et al.  Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships , 2009, BMC Bioinformatics.

[54]  Michael L Maitland,et al.  Terminal Ballistics of Kinase Inhibitors: There Are No Magic Bullets , 2006, Annals of Internal Medicine.

[55]  Avi Ma'ayan,et al.  Drug-induced adverse events prediction with the LINCS L1000 data , 2016, Bioinform..

[56]  Juho Rousu,et al.  Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors , 2017, PLoS Comput. Biol..

[57]  Mindy I. Davis,et al.  A quantitative analysis of kinase inhibitor selectivity , 2008, Nature Biotechnology.

[58]  Jian Peng,et al.  A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information , 2017, RECOMB 2017.

[59]  Yongdong Zhang,et al.  Drug-target interaction prediction: databases, web servers and computational models , 2016, Briefings Bioinform..

[60]  Richard A Norman,et al.  Structural approaches to obtain kinase selectivity. , 2012, Trends in pharmacological sciences.

[61]  V. Jordan,et al.  Development and evolution of therapies targeted to the estrogen receptor for the treatment and prevention of breast cancer , 2007, Steroids.

[62]  Xin Gao,et al.  An integrated structure- and system-based framework to identify new targets of metabolites and known drugs , 2015, Bioinform..

[63]  S. Mallal,et al.  Drug hypersensitivity caused by alteration of the MHC-presented self-peptide repertoire , 2012, Proceedings of the National Academy of Sciences.

[64]  Yan Zhao,et al.  Drug repositioning: a machine-learning approach through data integration , 2013, Journal of Cheminformatics.

[65]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[66]  Mindy I. Davis,et al.  Comprehensive analysis of kinase inhibitor selectivity , 2011, Nature Biotechnology.

[67]  Jian Peng,et al.  A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information , 2017, Nature Communications.

[68]  Daniel C. Liebler,et al.  Elucidating mechanisms of drug-induced toxicity , 2005, Nature Reviews Drug Discovery.

[69]  L. Cardon,et al.  Use of genome-wide association studies for drug repositioning , 2012, Nature Biotechnology.

[70]  S. Okabe,et al.  Identification and functional signature of genes regulated by structurally different ABL kinase inhibitors , 2007, Oncogene.

[71]  Xin Gao,et al.  Finding optimal interaction interface alignments between biological complexes , 2015, Bioinform..