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Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques

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Abstract

Selective inhibition of Bcl-2 and Bcl-xL proteins due to their dual inhibition toxicity plays an important role in treatment of cancer and chemotherapy effectiveness; therefore, in the last decade, discovery of selective inhibitors for Bcl-2 and Bcl-xL proteins has become a significant and important research topic. The present contribution paves the way for characterization of molecular features which induce selectivity for inhibition of Bcl-2 and Bcl-xL. In this line, a total of 1534 molecules related to inhibition of Bcl-2 and Bcl-xL proteins were collected from Binding Database. A diverse set of molecular descriptors was calculated for each molecule, and the best subset of descriptors were selected using variable importance in projection (VIP) approach. The molecules were classified according to their therapeutic targets (Bcl-2/Bcl-xL) and activities. Partial least square-discriminate analysis (PLS-DA) and supervised Kohonen network (SKN) models were utilized to relate the molecular structures of chemicals to their activities and selectivities. According to the VIP-selected descriptors physicochemical properties, such as polarity number, number of branches, size and cyclicity of the molecule, flexibility, functional counts and constitutional descriptors, all affect the activities of Bcl-2 and Bcl-xL inhibitors. The performances of PLS-DA and SKN methods were evaluated based on statistical parameters derived from the confusion matrices. The models were validated using tenfold cross-validation and an external test set. The best statistical results were obtained by implementing the SKN model. The classification rates range from 93.5 to 79.1% for the training and validation procedure for the optimized SKN models. The high values of the obtained classification rates demonstrate that the information provided in this work would be useful to design new drugs with selective inhibitory activities toward Bcl-2 or Bcl-xL proteins for more effective treatment of cancer.

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References

  1. World Health Organization (2017) WHO’s work on cancer. http://www.who.int/cancer/en/. Accessed 23 Aug 2017

  2. Ashkenazi A, Fairbrother WJ, Leverson JD, Souers AJ (2017) From basic apoptosis discoveries to advanced selective BCL-2 family inhibitors. Nat Rev Drug Discov 16:273–284. https://doi.org/10.1182/blood-2011-03-344812

    Article  CAS  PubMed  Google Scholar 

  3. Wickman G, Julian L, Olson MF (2012) How apoptotic cells aid in the removal of their own cold dead bodies. Cell Death Differ 19(5):735–742. https://doi.org/10.1038/cdd.2012.25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Vogler M, Hamali HA, Sun XM, Bampton ET, Dinsdale D, Snowden RT, Dyer MJ, Goodall AH, Cohen GM (2011) BCL2/BCL-XL inhibition induces apoptosis, disrupts cellular calcium homeostasis, and prevents platelet activation. Blood 117(26):7145–7154. https://doi.org/10.1038/nrd.2016.253

    Article  CAS  PubMed  Google Scholar 

  5. Zhao DP, Ding XW, Peng JP, Zheng YX, Zhang SZ (2005) Prognostic significance of bcl-2 and p53 expression in colorectal carcinoma. J Zhejiang Univ Sci B 6:1163–1169. https://doi.org/10.1631/jzus.2005.B1163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Correia C, Schneider PA, Dai H, Dogan A, Maurer MJ, Church AK, Novak AJ, Feldman AL, Wu X, Ding H, Meng XW (2015) BCL2 mutations are associated with increased risk of transformation and shortened survival in follicular lymphoma. Blood 125:658–667. https://doi.org/10.1182/blood-2014-04-571786

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Merino D, Lok SW, Visvader JE, Lindeman GJ (2016) Targeting BCL-2 to enhance vulnerability to therapy in estrogen receptor-positive breast cancer. Oncogene 35:1877–1887. https://doi.org/10.1038/onc.2015.287

    Article  CAS  PubMed  Google Scholar 

  8. Enyedy IJ, Ling Y, Nacro K, Tomita Y, Wu X, Cao Y, Guo R, Li B, Zhu X, Huang Y, Long YQ, Roller PP, Yang D, Wang S (2001) Discovery of small-molecule inhibitors of Bcl-2 through structure-based computer screening. J Med Chem 44:4313–4324. https://doi.org/10.1021/jm010016f

    Article  CAS  PubMed  Google Scholar 

  9. Colak S, Zimberlin CD, Fessler E, Hogdal L, Prasetyanti PR, Grandela CM, Letai A, Medema JP (2014) Decreased mitochondrial priming determines chemoresistance of colon cancer stem cells. Cell Death Differ 21:1170–1177. https://doi.org/10.1038/cdd.2014.37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zeuner A, Francescangeli F, Contavalli P, Zapparelli G, Apuzzo T, Eramo A, Baiocchi M, De Angelis ML, Biffoni M, Sette G, Todaro M, Stassi G, Maria RD (2014) Elimination of quiescent/slow-proliferating cancer stem cells by Bcl-XL inhibition in non-small cell lung cancer. Cell Death Differ 21:1877–1888. https://doi.org/10.1038/cdd.2014.105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wong M, Tan N, Zha J, Peale FV, Yue P, Fairbrother WJ, Belmont LD (2012) Navitoclax (ABT-263) reduces Bcl-x(L)-mediated chemoresistance in ovarian cancer models. Mol Cancer Ther 11:1026–1035. https://doi.org/10.1158/1535-7163.MCT-11-0693

    Article  CAS  PubMed  Google Scholar 

  12. Ikezawa K, Hikita H, Shigekawa M, Iwahashi K, Eguchi H, Sakamori R, Tatsumi T, Takehara T (2017) Increased Bcl-xL expression in pancreatic neoplasia promotes carcinogenesis by inhibiting senescence and apoptosis. Cell Mol Gastroenterol Hepatol 4:185–200. https://doi.org/10.1016/j.jcmgh.2017.02.001

    Article  PubMed  PubMed Central  Google Scholar 

  13. Souers AJ, Leverson JD, Boghaert ER, Ackler SL, Catron ND, Chen J, Dayton BD, Ding H, Enschede SH, Fairbrother WJ, Huang DC et al (2013) ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat Med 19:202–209. https://doi.org/10.1038/nm.3048

    Article  CAS  PubMed  Google Scholar 

  14. Wang JL, Liu D, Zhang ZJ, Shan S, Han X, Srinivasula SM, Croce CM, Alnemri ES, Huang Z (2000) Structure-based discovery of an organic compound that binds Bcl-2 protein and induces apoptosis of tumor cells. Proc Natl Acad Sci India A 97(13):7124–7129. https://doi.org/10.1073/pnas.97.13.7124

    Article  CAS  Google Scholar 

  15. Enyedy IJ, Ling Y, Nacro K, Tomita Y, Wu X, Cao Y, Guo R, Li B, Zhu X, Huang Y, Long YQ (2001) Discovery of small-molecule inhibitors of Bcl-2 through structure-based computer screening. J Med Chem 44(25):4313–4324. https://doi.org/10.1021/jm010016f

    Article  CAS  PubMed  Google Scholar 

  16. Kanakaveti V, Sakthivel R, Rayala SK, Gromiha MM (2017) Importance of functional groups in predicting the activity of small molecule inhibitors for Bcl-2 and Bcl-xL. Chem Biol Drug Des 90(2):308–316. https://doi.org/10.1111/cbdd.12952

    Article  CAS  PubMed  Google Scholar 

  17. Mukherjee P, Desai P, Zhou YD, Avery M (2010) Targeting the BH3 domain mediated protein–protein interaction of Bcl-xL through virtual screening. J Chem Inf Model 50(5):906–923. https://doi.org/10.1021/ci1000373

    Article  CAS  PubMed  Google Scholar 

  18. Lama D, Sankararamakrishnan R (2008) Anti‐apoptotic Bcl‐xL protein in complex with BH3 peptides of pro‐apoptotic Bak, Bad, and Bim proteins: comparative molecular dynamics simulations. Proteins Struct Funct Bioinform 73(2):492–514. https://doi.org/10.1002/prot.22075

    Article  CAS  Google Scholar 

  19. Wakui N, Yoshino R, Yasuo N, Ohue M, Sekijima M (2018) Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-xL: a molecular dynamics simulation approach. J Mol Graph Model 79:166–174. https://doi.org/10.1016/j.jmgm.2017.11.011

    Article  CAS  PubMed  Google Scholar 

  20. Almerico AM, Tutone M, Lauria A (2009) In-silico screening of new potential Bcl-2/Bcl-xL inhibitors as apoptosis modulators. J Mol Model 15(4):349–355. https://doi.org/10.1007/s00894-008-0405-x

    Article  CAS  PubMed  Google Scholar 

  21. Aboalhaija NH, Zihlif MA, Taha MO (2016) Discovery of new selective cytotoxic agents against Bcl-2 expressing cancer cells using ligand-based modeling. Chem Biol Interact 250:12–26. https://doi.org/10.1016/j.cbi.2016.03.006

    Article  CAS  PubMed  Google Scholar 

  22. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2016) Binding DB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D0145–D1053. https://doi.org/10.1093/nar/gkv1072

    Article  CAS  Google Scholar 

  23. Jalali-Heravi M, Mani-Varnosfaderani A, Jahromi PE, Mahmoodi MM, Taherinia D (2011) Classification of anti-HIV compounds using counter propagation artificial neural networks and decision trees. SAR QSAR Environ Res 22(7–8):639–660. https://doi.org/10.1080/1062936X.2011.623318

    Article  CAS  PubMed  Google Scholar 

  24. Jalali-Heravi M, Mani-Varnosfaderani A, Valadkhani A (2013) Integrated one-against-one classifiers as tools for virtual screening of compound databases: a case study with CNS inhibitors. Mol Inf 32:742–753. https://doi.org/10.1002/minf.201200126

    Article  CAS  Google Scholar 

  25. Jalali-Heravi M, Mani-Varnosfaderani A (2012) Navigating drug-like chemical space of anticancer molecules using genetic algorithms and counter propagation artificial neural networks. Mol Inf 31:63–74. https://doi.org/10.1002/minf.201100098

    Article  CAS  Google Scholar 

  26. Neiband MS, Mani-Varnosfaderani A, Benvidi A (2017) Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: a systematic route for designing selective SphK inhibitors. SAR QSAR Environ Res 28:91–109. https://doi.org/10.1080/1062936X.2017.1280535

    Article  CAS  PubMed  Google Scholar 

  27. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Chem Inf 3:1–14. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  Google Scholar 

  28. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P

    Article  CAS  Google Scholar 

  29. Todeschini R, Consonni V, Mauri A, Pavan M (2006) DRAGONs software for the calculation of molecular descriptors, version 5.4 for Windows. Milan, Italy. http://www.talete.mi.it/products/dragon_description.htm

  30. Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using matthews correlation coefficient metric. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0177678

    Article  CAS  Google Scholar 

  31. Chen J, Zhou H, Aguilar A, Liu L, Bai L, McEachern D, Yang CY, Meagher L, Stuckey JA, Wang S (2012) 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 55(19):8502–8514. https://doi.org/10.1021/jm3010306

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhou H, Aguilar A, Chen J, Bai L, Liu L, Meagher JL, Yang CY, McEachern D, Cong X, Stuckey JA, Wang S (2012) Structure-based design of potent Bcl-2/Bcl-xL inhibitors with strong in vivo antitumor activity. J Med Chem 55(13):6149–6161. https://doi.org/10.1021/jm300608w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Varnes JG, Gero T, Huang S, Diebold RB, Ogoe C, Grover PT, Su M, Mukherjee P, Saeh JC, MacIntyre T, Repik G (2014) Towards the next generation of dual Bcl-2/Bcl-xL inhibitors. Bioorg Med Chem Lett 24(14):3026–3033. https://doi.org/10.1016/j.bmcl.2014.05.036

    Article  CAS  PubMed  Google Scholar 

  34. Liu X, Zhang Y, Huang W, Tan W, Zhang A (2018) Design, synthesis and pharmacological evaluation of new acyl sulfonamides as potent and selective Bcl-2 inhibitors. Bioorg Med Chem 26(2):443–454. https://doi.org/10.1016/j.bmc.2017.12.001

    Article  CAS  PubMed  Google Scholar 

  35. Pellecchia M, Reed JC (2004) Inhibition of anti-apoptotic Bcl-2 family proteins by natural polyphenols new avenues for cancer chemoprevention and chemotherapy. Curr Pharm Des 10(12):1387–1398. https://doi.org/10.2174/1381612043384880

    Article  CAS  PubMed  Google Scholar 

  36. Hennessy EJ (2016) Selective inhibitors of Bcl-2 and Bcl-xL: balancing antitumor activity with on-target toxicity. Bioorg Med Chem Lett 26(9):2105–2114. https://doi.org/10.1016/j.bmcl.2016.03.032

    Article  CAS  PubMed  Google Scholar 

  37. Touré BB, Miller-Moslin K, Yusuff N, Perez L, Doré M, Joud C, Michael W, DiPietro L, van der Plas S, McEwan M, Lenoir F (2013) The role of the acidity of N-heteroaryl sulfonamides as inhibitors of Bcl-2 family protein–protein interactions. ACS Med Chem Lett 4(2):186–190. https://doi.org/10.1021/ml300321d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rizzi A, Fioni A (2008) Virtual screening using PLS discriminant analysis and ROC curve approach: an application study on PDE4 inhibitors. J Chem Inf Model 48:1686–1692. https://doi.org/10.1021/ci800072r

    Article  CAS  PubMed  Google Scholar 

  39. Ahmed I, Greenwood R, Costello B, Ratcliffe N, Probert CS (2016) Investigation of faecal volatile organic metabolites as novel diagnostic biomarkers in inflammatory bowel disease. Aliment Pharm Therap 43(5):596–611. https://doi.org/10.1111/apt.13522

    Article  CAS  Google Scholar 

  40. Armutlu P, Ozdemir ME, Uney-Yuksektepe F, Kavakli IH, Turkay M (2008) Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method. BMC Bioinform 9(1):1–14. https://doi.org/10.1186/1471-2105-9-411

    Article  CAS  Google Scholar 

  41. Reis A, Rudnitskaya A, Chariyavilaskul P, Dhaun N, Melville V, Goddard J, Webb DJ, Pitt AR, Spickett CM (2015) Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease. J Lipid Res 56:413–422. https://doi.org/10.1194/jlr.M055624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Penn BS (2005) Using self-organizing maps to visualize high-dimensional data. Comput Geosci 31:531–544. https://doi.org/10.1016/j.cageo.2004.10.009

    Article  CAS  Google Scholar 

  43. Cordel MO, Azcarraga AP (2015) Fast emulation of self-organizing maps for large datasets. Procedia Comput Sci 52:381–388. https://doi.org/10.1016/j.procs.2015.05.002

    Article  Google Scholar 

  44. Wongravee K, Lloyd GR, Silwood CJ, Grootveld M, Brereton RG (2010) Supervised self-organizing maps for classification and determination of potentially discriminatory variables: illustrated by application to nuclear magnetic resonance metabolomic profiling. Anal Chem 82:628–638. https://doi.org/10.1021/ac9020566

    Article  CAS  PubMed  Google Scholar 

  45. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set descriptor space: a review. Atla-Nottingham 33(5):445 PMID:16268757

    CAS  Google Scholar 

  46. Gramatica P (2007) Principles of QSAR models validation: internal and external. Mol Inf 26(5):694–701. https://doi.org/10.1002/qsar.200610151

    Article  CAS  Google Scholar 

  47. Randic M, Kleiner AF, De Alba LM (1994) Distance/distance matrixes. J Chem Inf Comput Sci 34:277–286. https://doi.org/10.1021/ci00018a008

    Article  CAS  Google Scholar 

  48. Todeschini R, Consoni V (2008) Handbook of molecular descriptors. Methods and principles in medicinal chemistry. Wiley, New York. https://doi.org/10.1002/9783527613106

    Book  Google Scholar 

  49. Plavšić D, Nikolić S, Trinajstić N, Mihalić Z (1993) On the Harary index for the characterization of chemical graphs. J Math Chem 12(1):235–250. https://doi.org/10.1007/BF01164638

    Article  Google Scholar 

  50. Arteca GA, Lipkowitz KB, Boyd DB (2007) Molecular shape descriptors. Rev Comput Chem 9:191–253. https://doi.org/10.1002/9780470125861.ch5

    Article  Google Scholar 

  51. Consonni V, Todeschini R, Pavan M, Gramatica P (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J Chem Inf Comput Sci 42(3):693–705

    Article  CAS  PubMed  Google Scholar 

  52. Kier LB, Hall LH, Frazer JW (1991) An index of electrotopological state for atoms in molecules. J Math Chem 7:229–241. https://doi.org/10.1007/BF01200825

    Article  CAS  Google Scholar 

  53. Sharma V, Goswami R, Madan AK (1997) Eccentric connectivity index: a novel highly discriminating topological descriptor for structure-property and structure-activity studies. J Chem Inf Comput Sci 37(2):273–282. https://doi.org/10.1021/ci960049h

    Article  CAS  Google Scholar 

  54. Bounova G, de Weck O (2012) Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles. Phys Rev E 85(1):016117–016211. https://doi.org/10.1103/PhysRevE.85.016117

    Article  CAS  Google Scholar 

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Acknowledgements

Tarbiat Modares and Yazd Universities and School of Biological Sciences in IPM are greatly acknowledged for supporting this project with Grant Number BS-1395-01-11.

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Authors and Affiliations

  1. Department of Chemistry, Tarbiat Modares University, Tehran, Iran

    Ahmad Mani-Varnosfaderani

  2. Department of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Ahmad Mani-Varnosfaderani

  3. Department of Chemistry, Yazd University, Yazd, Iran

    Marzieh Sadat Neiband & Ali Benvidi

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  1. Ahmad Mani-Varnosfaderani
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Correspondence to Ahmad Mani-Varnosfaderani.

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Mani-Varnosfaderani, A., Neiband, M.S. & Benvidi, A. Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques. Mol Divers 23, 55–73 (2019). https://doi.org/10.1007/s11030-018-9856-x

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