DMKPred: Specificity and Cross-reactivity of Kinase Inhibitors G P S Raghava, Head Bioinformatics Centre Email: raghava@imtech.res.in; Web: http://www.imtech.res.in/raghava/ Institute of Microbial Technology, Sector-39A, Chandigarh, India

What are protein kinases ? Critical components of cellular signal transduction cascades . They regulate cell division, differentiation, proliferation, movement & apoptosis by phosphorylating Ser, Thr and Tyr residues of specific substrates. Represent 1.7 % of all human genes .

Why kinases are so important? They are the key regulators of all aspects of neoplasia, including proliferation, invasion, angiogenesis and metastasis. A number of diseases, especially cancer involve unregulated kinase activity (overexpression / upregulation). This makes kinases as important targets for drug development. Kinase inhibitors are successfully used in cancer treatment.

Kinase inhibitors Chronic myeloid leukemia (CML) drug molecule bind to the ATP binding site of bcr-abl tyrosine kinase.

Alignment of the ATP-binding site residues of kinase proteins

Selectivity and specificity of existing kinase inhibitors

Can we solve specificity problem of kinase inhibitors ? Most of the drug molecules binds with other protein kinases and cause cross-reactivity. It’s very difficult to design a specific kinase inhibitors against a protein kinase. If Kd of inhibitors with primary intended target is ≤ 10 fold then chances of cross-reactivity is low. Fabin et al., Nat. Biotechnol., 2005, 23, 229-236

Specificity and cross-reactivity of kinase inhibitors Molecules data: Data was taken from Nat. Biotechnol., 2005, 23, 229-236 (Fabin et al., 119 × 20 Kd data) Select those kinases for which  6 chemical molecules have significant binding Finally we select 29 protein kinases for this study

Structure Descriptors: Specificity and cross-reactivity of kinase inhibitors Structure Descriptors: We calculated 8 structure descriptors from Molinspiration (on line web server for descriptor calculation) We also calculated  600 structure descriptors using PreADMET (on line web server for molecular descriptor calculation) Remove insignificant descriptors and select 62 molecular descriptors for further study

Specificity and cross-reactivity of kinase inhibitors Calculate molecular descriptors of kinase inhibitors Remove similar and insignificant descriptors Calculate correlation between Kd and descriptors Select highly significant molecular descriptors Developed model for prediction

Specificity and cross-reactivity of kinase inhibitors Correlation between molecular descriptors and Kd Kinase1 Kd 1 Des1 Des2 ….. ……………………….. Des-m (Chemical1) . Des1 Des2 ….. ………………………… Des-m (Chemical2) . Des1 Des2 ….. ………………………… Des-m (Chemical.) Kd n Des1 Des2…… ………………………… Des-m (Chemical-n) Kinase2 Kd 1 Des1 Des2 ….. ……………………… Des-m (Chemical1) . Des1 Des2 ….. ………………………. Des-m (Chemical2) . Des1 Des2 ….. ……………………….. Des-m (Chemical.) Kd n Des1 Des2…… ………………………. Des-m (Chemical-n) Kinase-n Kd 1 Des1 Des2 ….. …………………….. Des-m (Chemical1) . Des1 Des2 ….. ……………………… Des-m (Chemical2) . Des1 Des2 ….. ……………………… Des-m (Chemical.) Kd n Des1 Des2…… ……………………… Des-m (Chemical-n) Select molecular descriptors with highest average correlation

General model for chemical kinase inhibitors Protein Top 5 Top 10 Top 15 Top 17 Molinspiration AAK1 0.514 NM 0.450 0.591 0.420 ABL1 0.449 0.714 0.430 0.697 0.472 ABL1E255K 0.530 0.480 0.435 0.475 0.585 ABL1H396P 0.851 0.623 0.473 0.633 ABL1M351T 0.440 0.698 0.561 0.675 0.474 ABL1Q252H 0.719 0.491 0.706 0.519 ABL1Y253F 0.409 0.731 0.486 0.716 0.444 ABL2 0.737 0.621 0.617 BIKE 0.253 0.381 0.669 0.511 CLK2 0.282 0.763 0.523 0.360 0.329 EGFR 0.287 0.279 0.476 0.226 0.277 EPHA5 0.428 0.445 0.349 0.464 EPHA6 0.212 0.441 0.372 0.233 0.379 EPHB1 0.462 0.195 0.578 0.394 0.577 GAK 0.197 0.341 0.028 0.455 0.382 JNK2 0.601 0.627 0.647 0.064 JNK3 0.120 0.368 0.467 0.327 KIT 0.908 0.518 0.366 0.332 LCK 0.653 0.771 0.727 0.047 MAP4K5 0.658 0.515 0.410 0.154 0.129 P38ALPHA 0.306 0.539 0.805 0.494 0.735 PDGFR 0.583 0.268 0.363 0.177 0.036 RIPK2 0.736 0.249 0.250 0.557 SLK 0.643 0.293 0.345 SRC 0.770 0.541 0.540 0.395 STK10 0.792 0.396 0.552 0.373 STK18 0.443 0.210 0.403 0.529 TNIK 0.563 0.274 0.235 0.202 VEGFR 0.266 0.172 0.314 0.328 Average 0.482 0.507 0.415

Kinase specific model for chemical kinase inhibitors Protein Top 5 +ve Top 5 –ve Top 10 +ve 10 mixed AAK1 0.55 0.80 NM 0.22 ABL1 0.52 0.38 0.16 ABL1E255K 0.32 ABL1H396P 0.51 0.24 0.33 ABL1M351T 0.72 0.56 0.66 0.48 ABL1Q252H 0.43 0.44 0.25 ABL1Y253F 0.21 0.07 0.03 ABL2 0.41 0.60 0.02 BIKE 0.96 0.76 CLK2 0.06 EGFR 0.81 0.29 0.91 EPHA5 0.95 EPHA6 0.79 0.68 EPHB1 0.89 GAK JNK2 0.14 0.37 0.18 JNK3 0.69 0.63 KIT 0.57 LCK 0.62 MAP4K5 0.61 0.31 P38ALPHA PDGFR RIPK2 0.65 SLK 0.59 0.30 SRC 0.34 0.17 STK10 0.01 STK18 0.19 TNIK 0.88 VEGFR Average 0.539 0.412 0.376 0.374

Web interface for DMKPred

Computational Resources for Drug Discovery Open Source Drug Discovery An Insilico Module of Open Source Drug Discovery

I’ll start with a brief overview of how we’ll be improving the prediction algorithm Adding tools, spiffing up tools, tools recognize more sites. Left vs. right 3 novel features tell you about in a moment Increasing # of sites Now for exciting part, our new features…

Meta-Server: Prediction of subcellular localization of proteins using various server

Thanku URLs: http://www.imtech.res.in/raghava/ & http://crdd.osdd.net/ & bic.uams.edu/