Fingerprint-based 2D-QSAR Models for Predicting Bcl-2 Inhibitors Affinity

Author(s): Said Byadi, Hachim Mouhi Eddine, Karima Sadik, Črtomir Podlipnik, Aziz Aboulmouhajir*

Journal Name: Letters in Drug Design & Discovery

Volume 17 , Issue 10 , 2020


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Abstract:

Background: Bcl-2 family plays an essential role in the cell cycle events incorporating survival, proliferation, and differentiation in normal and neoplastic neuronal cells. Thus, it has been validated as a principal target for the treatment of cancer. For this reason, we will build a model based on a large number of Bcl-2 inhibitors to predict the activities of new compounds as future Bcl-2 inhibitors.

Methods: In this study, QSAR models were successfully used to predict the inhibitory activity against Bcl-2 for a set of compounds collected from BDB (Binding database). The kPLS (kernelbased Partial Least-Square) method implemented in Schrodinger's Canvas, was used for searching the correlation between pIC50 and binary fingerprints for a set of known Bcl-2 inhibitors.

Results and Discussion: Models based on binary fingerprints with two kPLS factors have been found with decent predictive power (q2 > 0.58), while the optimal number of factors is about 5. The enrichment study (148 actives, 5700 decoys) has shown excellent classification ability of our models (AUC > 0.90) for all cases).

Conclusion: We found that the kPLS method, in combination with binary fingerprints, is useful for the affinity prediction and the Bcl-2 inhibitors classification. The obtained promising results, methods, and applications highlighted in this study will help us to design more selective Bcl-2 inhibitors with better structural characteristics and improved anti-cancer activity.

Keywords: Bcl-2 inhibitors, QSAR, kernel PLS, validation, prediction, cancer.

[1]
Montero, J.; Letai, A. Why do BCL-2 inhibitors work and where should we use them in the clinic? Cell Death Differ., 2018, 25(1), 56-64.
[http://dx.doi.org/10.1038/cdd.2017.183] [PMID: 29077093]
[2]
Opferman, J.T.; Kothari, A. Anti-apoptotic BCL-2 family members in development. Cell Death Differ., 2018, 25(1), 37-45.
[http://dx.doi.org/10.1038/cdd.2017.170] [PMID: 29099482]
[3]
Ray, S.; Das, S.; Suar, M. Molecular mechanism of drug resistance.Drug Resistance in Bacteria, Fungi, Malaria, and Cancer; , 2017, pp. 47-110.
[http://dx.doi.org/10.1007/978-3-319-48683-3_3]
[4]
Nagane, M.; Levitzki, A.; Gazit, A.; Cavenee, W.K.; Huang, H.J. Drug resistance of human glioblastoma cells conferred by a tumor-specific mutant epidermal growth factor receptor through modulation of Bcl-XL and caspase-3-like proteases. Proc. Natl. Acad. Sci. USA, 1998, 95(10), 5724-5729.
[http://dx.doi.org/10.1073/pnas.95.10.5724] [PMID: 9576951]
[5]
Tsujimoto, Y.; Finger, L.R.; Yunis, J.; Nowell, P.C.; Croce, C.M. Cloning of the chromosome breakpoint of neoplastic B cells with the t(14;18) chromosome translocation. Science, 1984, 226(4678), 1097-1099.
[http://dx.doi.org/10.1126/science.6093263]
[6]
Hockenbery, D.; Nuñez, G.; Milliman, C.; Schreiber, R.D.; Korsmeyer, S.J. Bcl-2 is an inner mitochondrial membrane protein that blocks programmed cell death. Nature, 1990, 348(6299), 334-336.
[http://dx.doi.org/10.1038/348334a0] [PMID: 2250705]
[7]
Kozopas, K.M.; Yang, T.; Buchan, H.L.; Zhou, P.; Craig, R.W. MCL1, a gene expressed in programmed myeloid cell differentiation, has sequence similarity to BCL2. Proc. Natl. Acad. Sci. USA, 1993, 90(8), 3516-3520.
[http://dx.doi.org/10.1073/pnas.90.8.3516] [PMID: 7682708]
[8]
Zhang, Z.; Wu, G.; Xie, F.; Song, T.; Chang, X. 3-Thiomorpholin-8-oxo-8H-acenaphtho[1,2-b]pyrrole-9-carbonitrile (S1) based molecules as potent, dual inhibitors of B-cell lymphoma 2 (Bcl-2) and myeloid cell leukemia sequence 1 (Mcl-1): Structure-based design and structure-activity relationship studies. J. Med. Chem., 2011, 54(4), 1101-1105.
[http://dx.doi.org/10.1021/jm101181u] [PMID: 21235240]
[9]
Zhou, H.; Aguilar, A.; Chen, J.; Bai, L.; Liu, L.; Meagher, J.L.; Yang, C.Y.; McEachern, D.; Cong, X.; Stuckey, J.A.; Wang, S. Structure-based design of potent Bcl-2/Bcl-xL inhibitors with strong in vivo antitumor activity. J. Med. Chem., 2012, 55(13), 6149-6161.
[http://dx.doi.org/10.1021/jm300608w] [PMID: 22747598]
[10]
Chen, J.; Zhou, H.; Aguilar, A.; Liu, L.; Bai, L.; McEachern, D.; Yang, C.Y.; Meagher, J.L.; Stuckey, J.A.; Wang, S. Structure-based discovery of BM-957 as a potent small-molecule inhibitor of Bcl-2 and Bcl-xL capable of achieving complete tumor regression. J. Med. Chem., 2012, 55(19), 8502-8514.
[http://dx.doi.org/10.1021/jm3010306] [PMID: 23030453]
[11]
Yap, J.L.; Chen, L.; Lanning, M.E.; Fletcher, S. Expanding the cancer arsenal with targeted therapies: disarmament of the antiapoptotic bcl-2 proteins by small molecules. J. Med. Chem., 2017, 60(3), 821-838.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01888] [PMID: 27749061]
[12]
Narayanan Nair, D.; Padmavathy, S. Molecular docking studies of phytocompounds from Aloe vera (L.) Burm. F. having anticancer property, against an antiapoptotic Bcl-2 Protein. Biosci. Biotechnol. Res. Asia, 2018, 14(4), 1449-1456.
[http://dx.doi.org/10.13005/bbra/2590]
[13]
Souers, A.J.; Leverson, J.D.; Boghaert, E.R.; Ackler, S.L.; Catron, N.D.; Chen, J.; Dayton, B.D.; Ding, H.; Enschede, S.H.; Fairbrother, W.J.; Huang, D.C.; Hymowitz, S.G.; Jin, S.; Khaw, S.L.; Kovar, P.J.; Lam, L.T.; Lee, J.; Maecker, H.L.; Marsh, K.C.; Mason, K.D.; Mitten, M.J.; Nimmer, P.M.; Oleksijew, A.; Park, C.H.; Park, C.M.; Phillips, D.C.; Roberts, A.W.; Sampath, D.; Seymour, J.F.; Smith, M.L.; Sullivan, G.M.; Tahir, S.K.; Tse, C.; Wendt, M.D.; Xiao, Y.; Xue, J.C.; Zhang, H.; Humerickhouse, R.A.; Rosenberg, S.H.; Elmore, S.W. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med., 2013, 19(2), 202-208.
[http://dx.doi.org/10.1038/nm.3048] [PMID: 23291630]
[14]
Lamoree, B.; Hubbard, R.E. Current perspectives in fragment-based lead discovery (FBLD). Essays Biochem., 2017, 61(5), 453-464.
[http://dx.doi.org/10.1042/EBC20170028] [PMID: 29118093]
[15]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053.
[http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]
[16]
Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K.; Binding, D.B. A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 2007, 35(Suppl. 1), 198-201.
[http://dx.doi.org/10.1093/nar/gkl999]
[17]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[18]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. dud-enhanced - better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55, 6582-6594.
[http://dx.doi.org/10.1021/jm300687e] [PMID: 22716043]
[19]
Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model., 2010, 50(5), 771-784.
[http://dx.doi.org/10.1021/ci100062n] [PMID: 20450209]
[20]
Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W. Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model., 2010, 29(2), 157-170.
[http://dx.doi.org/10.1016/j.jmgm.2010.05.008] [PMID: 20579912]
[21]
Huang, H.; Chen, B.; Liu, C. Safety monitoring of a super-high dam using optimal kernel partial least squares. Math. Probl. Eng., 2015, 2015, 1-13.
[http://dx.doi.org/10.1155/2015/571594]
[22]
Rosipal, R.; Trejo, L.J. Kernel partial least squares regression in reproducing kernel Hilbert space. J. Mach. Learn. Res., 2002, 2, 97-123.
[23]
Muschelli, J. ROC and AUC with a Binary Predictor: A Potentially Misleading Metric, 2019, 1-20.
[24]
An, Y.; Sherman, W.; Dixon, S.L. Kernel-based partial least squares: Application to fingerprint-based QSAR with model visualization. J. Chem. Inf. Model., 2013, 53(9), 2312-2321.
[http://dx.doi.org/10.1021/ci400250c] [PMID: 23901898]
[25]
Benfenati, E. Theory, guidance and applications on QSAR and REACH., 2012.
[26]
Hajian-Tilaki, K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med., 2013, 4(2), 627-635.
[PMID: 24009950]


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Article Details

VOLUME: 17
ISSUE: 10
Year: 2020
Published on: 11 October, 2020
Page: [1206 - 1215]
Pages: 10
DOI: 10.2174/1570180817999200414155403
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