Citation: HE Bing, LUO Yong, LI Bing-Ke, XUE Ying, YU Luo-Ting, QIU Xiao-Long, YANG Teng-Kuei. Predicting and Virtually Screening Breast Cancer Targeting Protein HEC1 Inhibitors by Molecular Descriptors and Machine Learning Methods[J]. Acta Physico-Chimica Sinica, ;2015, 31(9): 1795-1802. doi: 10.3866/PKU.WHXB201507301 shu

Predicting and Virtually Screening Breast Cancer Targeting Protein HEC1 Inhibitors by Molecular Descriptors and Machine Learning Methods

HE Bing ^1,2 ,
LUO Yong ¹ ,
LI Bing-Ke ² ,
XUE Ying ^1,3 ,
YU Luo-Ting ^1, ,
QIU Xiao-Long ^4,5 ,
YANG Teng-Kuei ⁴

1.
1 State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, P. R. China;
2 College of Chemistry and Life Science, Chengdu Normal University, Chengdu 611130, P. R. China;
3 College of Chemistry, Sichuan University, Chengdu 610064, P. R. China;
4 Zhaobang Bio-Med. Institute Co., Ltd., Nantong 226000, Jiangsu Province, P. R. China;
5 Wisdom Pharmaceutical Co., Ltd., Haimen 226123, Jiangsu Province, P. R. China

Received Date: 2 April 2015
Available Online: 30 July 2015
Fund Project: 抗肿瘤(乳腺癌)一类新药SKLB1312&rdquo (乳腺癌)

Highly expressed in cancer 1 (HEC1) is a conserved mitotic regulator that is critical for spindle checkpoint control, kinetochore functionality, and cell survival. Overexpression of HEC1 has been detected in a variety of human cancers, and it is linked to poor prognosis of primary breast cancers. Thus, it is important to screen novel inhibitors with high affinity for HEC1. Machine learning (ML) methods were exhibiting od pharmacodynamics, and toxicity. In this work, two ML methods, support vector machines (SVMs) and random forests (RFs), were used to develop a classification method for searching inhibitors and non-inhibitors of HEC1 from the chemical library of structural diversity by screening characteristics of molecular descriptors. Both ML methods achieved promising prediction accuracies, and the RF model showed better performance. We performed virtual screening of HEC1 inhibitors by the RF model from an in-house database to screen potential HEC1 inhibitors. Two novel potential candidates were found. In vitro experiments of the two compounds showed that both had a certain degree of antitumor activity for the MDA-MB-468 and MDA-MB-231 breast cancer cell lines. Our study shows that ML methods are promising to design and virtually screen inhibitors of HEC1.
- HEC1,
- Selective inhibitor,
- Machine learning method,
- Support vector machine,
- Random forest,
- Virtual screening
1. [1]
  
  (1) Gan, S. J.; Wang, Q.; Zhu, L. M.; Xie, H.; Ding, X. F. Basic & Clin. Med. 2015, 35 (1), 134. [甘绍举, 王青, 朱丽敏, 谢浩, 丁先锋. 基础医学与临床, 2015, 35 (1), 134.]
2. [2]
  (2) Chen, Y.; Riley, D. J.; Chen, P. L.; Lee, W. H. Mol. Cell Biol. 1997, 17 (10), 6049.
3. [3]
  (3) Du, X. L.; Wang, M. R. Acta Acad. Med. Sin. 2007, 29 (1), 137. [杜小莉, 王明荣. 中国医学科学院学报, 2007, 29 (1), 137.]
4. [4]
  (4) Hu, C. M.; Zhu, J.; Guo, X. E.; Chen, W.; Qiu, X. L.; N , B.; Chien, R.; Wang, Y. V.; Tsai, C. Y.; Wu, G.; Kim, Y.; Lopez, R.; Chamberlin, A. R.; Lee, E. H.; Lee, W. H. Oncogene 2015, 34, 1220. doi: 10.1038/onc.2014.67
5. [5]
  (5) Huang, L. Y.; Chang, C. C.; Lee, Y. S.; Chang, J. M.; Huang, J. J.; Chuang, S. H.; Kao, K. J.; Lau, G. M.; Tsai, P. Y.; Liu, C. W.; Lin, H. S.; Lau, J. Y. Mol. Cancer Ther. 2014, 13 (6), 1419.
6. [6]
  (6) Lee, Y. S.; Chuang, S. H.; Huang, L. Y.; Lai, C. L.; Lin, Y. H.; Yang, J. Y.; Liu, C. W.; Yang, S. C.; Lin, H. S.; Chang, C. C.; Lai, J. Y.; Jian, P. S.; Lam, K.; Chang, J. M.; Lau, J. Y.; Huang, J. J. J. Med. Chem. 2014, 57 (10), 4098. doi: 10.1021/jm401990s
7. [7]
  (7) Wu, G.; Qiu, X. L.; Zhou, L.; Zhu, J.; Chamberlin, R.; Lau, J.; Chen, P. L.; Lee, W. H. Cancer Res. 2008, 68 (20), 8393. doi: 10.1158/0008-5472.CAN-08-1915
8. [8]
  (8) Qiu, X. L.; Li, G.; Wu, G.; Zhu, J.; Zhou, L.; Chen, P. L.; Chamberlin, A. R.; Lee, W. H. J. Med. Chem. 2009, 52 (6), 1757. doi: 10.1021/jm8015969
9. [9]
  (9) Chen, Y.; Riley, D. J.; Zheng, L.; Chen, P. L.; Lee, W. H. J. Biol. Chem. 2002, 277 (51), 49408. doi: 10.1074/jbc.M207069200
10. [10]
  (10) Diaz-Rodríguez, E.; Sotillo, R.; Schvartzman, J. M.; Benezra, R. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (43), 16719. doi: 10.1073/pnas.0803504105
11. [11]
  (11) Ferretti, C.; Totta, P.; Fiore, M.; Mattiuzzo, M.; Schillaci, T.; Ricordye, R.; Di Leonardo, A.; Degrassi, F. Cell Cycle 2010, 9 (20), 4174. doi: 10.4161/cc.9.20.13457
12. [12]
  (12) Wei, R.; N , B.; Wu, G.; Lee, W. H. Mol. Biol. Cell 2011, 22 (19), 3584. doi: 10.1091/mbc.E11-01-0012
13. [13]
  (13) Xue, Y.; Li, H.; Ung, C.; Yap, C.; Chen, Y. Chem. Res. Toxicol. 2006, 19, 1030. doi: 10.1021/tx0600550
14. [14]
  (14) Xue, Y.; Yap, C. W.; Sun, L. Z.; Cao, Z. W.; Wang, J.; Chen, Y. Z. J. Chem. Inf. Comput. Sci. 2004, 44, 1497. doi: 10.1021/ci049971e
15. [15]
  (15) Xue, Y.; Li, Z.; Yap, C. W.; Sun, L.; Chen, X.; Chen, Y. Z. J. Chem. Inf. Comput. Sci. 2004, 44, 1630. doi: 10.1021/ci049869h
16. [16]
  (16) Yang, X. G.; Chen, D.; Wang, M.; Xue, Y.; Chen, Y. Z. J. Comput. Chem. 2009, 30, 1202. doi: 10.1002/jcc.v30:8
17. [17]
  (17) Yang, X. G.; Lv, W.; Chen, Y. Z.; Xue, Y. J. Comput. Chem. 2010, 31, 1249.
18. [18]
  (18) Lv, W.; Xue, Y. Eur. J. Med. Chem. 2010, 45, 1167. doi: 10.1016/j.ejmech.2009.12.038
19. [19]
  (19) Cong, Y.; Yang, X.; Lv, W.; Xue, Y. J. Mol. Graph. Model. 2009, 28, 236. doi: 10.1016/j.jmgm.2009.08.001
20. [20]
  (20) Luan, F.; Liu, H.; Ma, W.; Fan, B. Eur. Med. Chem. 2008, 43, 43. doi: 10.1016/j.ejmech.2007.03.002
21. [21]
  (21) Ung, C. Y.; Li, H.; Yap, C. W.; Chen, Y. Z. Mol. Pharmacol. 2007, 71, 158.
22. [22]
  (22) Li, H.; Ung, C.; Yap, C.; Xue, Y.; Li, Z.; Cao, Z.; Chen, Y. Chem. Res. Toxicol. 2005, 18, 1071. doi: 10.1021/tx049652h
23. [23]
  (23) Li, B. K.; Cong, Y.; Tian, Z. Y.; Xue, Y. Acta Phys. -Chim. Sin. 2014, 30 (1), 171. [李秉轲, 丛湧, 田之悦, 薛英. 物理化学学报, 2014, 30 (1), 171.] doi: 10.3866/PKU.WHXB201311041
24. [24]
  (24) Huang, J. J.; Lau, J. Improved Modulators of HEC1 Activity and Methods. CN Patent 103038231.A, 2013-04-10. [Huang, J. J., Lau, J. HEC1活性调节剂及其方法: 中国, CN103038231.A[P]. 2013-04-10.]
25. [25]
  (25) Duda, R. O.; Hart, P. E. Pattern Classification and Scene Analysis; John Wiley & Sons: Hoboken, New Jersey, USA, 1973.
26. [26]
  (26) ChemDraw 7.0.1 ed.; CambridgeSoft Corporation, Cambridge: Massachusetts, USA, 2007.
27. [27]
  (27) Corina 3.4 edn.; Molecular Networks GmbH Computerchemie: Erlangen, Germany, 2006.
28. [28]
  (28) Burges, C. J. Data Min. Knowl. Disc. 1998, 2, 121.
29. [29]
  (29) Vapnik, V. N. The Nature of Statistical Learning Theory; Springer: Berlin & Heidelberg, Germany, 1995.
30. [30]
  (30) Doucet, J. P.; Barbault, F.; Xia, H.; Panaye, A.; Fan, B. Curr. Comput-Aid. Drug. 2007, 3, 263. doi: 10.2174/157340907782799372
31. [31]
  (31) Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. J. Chem. Inf. Comput. Sci. 2003, 43, 1947. doi: 10.1021/ci034160g
32. [32]
  (32) Breiman, L. Mach. Learn. 2001, 45, 5. doi: 10.1023/A: 1010933404324
33. [33]
  (33) Khandelwal, A.; Krasowski, M. D.; Reschly, E. J.; Sinz, M. W.; Swaan, P. W.; Ekins, S. Chem. Res. Toxicol. 2008, 21, 1457. doi: 10.1021/tx800102e
34. [34]
  (34) Breiman, L. Out-of-bag Estimation, 1996, http://citeseerx.ist.psu.edu.sci-hub.org/viewdoc/download?doi=10.1.1.45.3712&rep=rep1&type=pdf (accessed Mar 15, 2015).
35. [35]
  (35) Breiman, L. Wald Lecture II, Looking inside the Black Box, 2005. http://www.stat.berkeley.edu/users/breiman (accessed Mar 15, 2015).
36. [36]
  (36) Breiman, L.; Cutler, A. Random Forests, Version 5.1, 2004. http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm (accessed Mar 15, 2015).
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