DMKPred: Specificity and Cross reactivity of Kinase Inhibitors

G P S Raghava, Head Bioinformatics Centre

Email: [email protected]

; Web: http://www.imtech.res.in/raghava/ Institute of Microbial Technology, Sector-39A, Chandigarh, India

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What are protein kinases ?

Ø Critical components of cellular signal transduction cascades .

Ø They regulate cell division, differentiation, proliferation, movement & apoptosis by phosphorylating Ser , Thr and Tyr residues of specific substrates.

Ø 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 ?

Ø Most of the drug molecules binds with other protein kinases and cause cross-reactivity.

Ø It’s very difficult to design a specific kinase inhibitors against a protein kinase.

Ø If K d 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 K d data) Select those kinases for which have significant binding 6 chemical molecules Finally we select 29 protein kinases for this study

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 K d descriptors and Select highly significant molecular descriptors Developed model for prediction

Specificity and cross-reactivity of kinase inhibitors Correlation between molecular descriptors and K Kinase1 Kinase2 Kinase-n

• K d 1 Des1 Des2 ….. ……………………….. Des m (Chemical1) • . Des1 Des2 ….. ………………………… Des m (Chemical2) • . Des1 Des2 ….. ………………………… Des m (Chemical.) • K d 1 Des1 Des2 ….. ……………………… Des-m (Chemical1) • . Des1 Des2 ….. ………………………. Des-m (Chemical2) • . Des1 Des2 ….. ……………………….. Des-m (Chemical.) • K (Chemical1) • . Des1 Des2 ….. ……………………… Des-m (Chemical2) • . Des1 Des2 ….. ……………………… Des-m (Chemical.) • K d d 1 Des1 Des2 ….. …………………….. Des-m n Des1 Des2…… ……………………… Des-m )

Select molecular descriptors with highest average d correlation

General model for chemical kinase inhibitors Protein AAK1 ABL1 ABL1E255K ABL1H396P ABL1M351T ABL1Q252H ABL1Y253F ABL2 BIKE CLK2 EGFR EPHA5 EPHA6 EPHB1 GAK JNK2 JNK3 KIT LCK MAP4K5 P38ALPHA PDGFR RIPK2 SLK SRC STK10 STK18 TNIK VEGFR Average Top 5 0.514

0.449

0.530

0.851

0.440

0.472

0.409

0.737

0.253

0.282

0.287

0.428

0.212

0.462

0.197

0.669

0.120

0.430

0.653

0.658

0.306

0.583

0.736

0.643

0.770

0.792

0.226

0.563

0.266

0.482

Top 10 NM 0.714

0.480

0.623

0.698

0.719

0.731

0.621

0.381

0.763

0.279

0.445

0.441

0.195

0.341

0.601

0.379

0.908

0.771

0.515

0.539

0.268

0.249

0.363

0.541

0.511

0.443

0.274

0.409

0.507

Top 15 0.450

0.430

0.435

0.473

0.561

0.491

0.486

0.473

NM 0.523

0.476

0.349

0.372

0.578

0.028

0.627

0.368

0.518

0.727

0.410

0.805

0.363

0.250

0.293

0.540

0.396

0.210

0.235

0.172

0.430

Top 17 0.591

0.697

0.475

0.633

0.675

0.706

0.716

0.617

0.669

0.360

0.226

0.511

0.233

0.394

0.455

0.647

0.467

0.366

0.047

0.154

0.494

0.177

0.440

0.293

0.540

0.552

0.403

0.202

0.314

0.450

Molinspiratio 0.472

0.585

0.480

0.474

0.519

0.444

0.440

0.511

0.329

0.277

0.464

0.379

0.577

0.382

0.064

0.327

0.332

0.621

0.129

0.735

0.036

0.557

0.345

0.395

0.373

0.529

0.514

0.328

0.415

Protein AAK1 ABL1 ABL1E255K ABL1H396P ABL1M351T ABL1Q252H ABL1Y253F ABL2 BIKE CLK2 EGFR EPHA5 EPHA6 EPHB1 GAK JNK2 JNK3 KIT LCK MAP4K5 P38ALPHA PDGFR RIPK2 SLK SRC STK10 STK18 TNIK VEGFR Average Top 5 +ve 0.55

0.52

0.80

0.51

0.72

0.43

0.21

0.41

0.96

NM 0.81

0.91

0.79

0.89

0.33

0.14

0.69

0.57

0.62

0.48

0.57

0.07

0.63

0.59

0.34

0.01

0.19

0.88

0.48

0.539

Top 5 –ve 0.80

0.38

0.32

0.24

0.56

0.33

0.07

NM NM NM 0.29

0.95

NM NM NM 0.37

0.52

NM 0.56

0.61

NM 0.07

0.65

0.30

0.44

0.29

NM 0.07

NM 0.412

Top 10 +ve NM 0.16

NM 0.33

0.66

0.44

0.33

0.60

0.76

0.06

0.91

NM 0.68

0.38

0.16

0.18

0.63

NM NM 0.31

NM 0.21

0.80

0.22

0.06

0.01

NM NM 0.01

0.376

10 mixed 0.22

NM NM NM 0.48

0.25

0.03

0.02

NM NM 0.91

0.95

0.22

NM 0.16

0.14

0.52

0.29

0.51

0.60

NM 0.55

0.57

NM 0.17

NM 0.30

0.76

NM 0.374

Web interface for DMKPred

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

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

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