1. DMKPred: Specificity and Cross-reactivity of Kinase Inhibitors
G P S Raghava, Head Bioinformatics Centre
Web:
Institute of Microbial Technology, Sector-39A, Chandigarh, India

3. 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 .

4. 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.

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

6. Alignment of the ATP-binding site residues of kinase proteins

7. Selectivity and specificity of existing kinase inhibitors

8. 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,

9. Specificity and cross-reactivity of kinase inhibitors
Molecules data:
Data was taken from Nat. Biotechnol., 2005, 23, (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

10. 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

11. 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

12. Specificity and cross-reactivity of kinase inhibitors
Correlation between molecular descriptors and Kd
Kinase1
Kd 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 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 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

13. 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

14. 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

15. Web interface for DMKPred

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

17. 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…

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

27. Thanku
URLs: & & bic.uams.edu/