Acta Phys. -Chim. Sin. ›› 2014, Vol. 30 ›› Issue (1): 171-182.doi: 10.3866/PKU.WHXB201311041

• BIOPHYSICAL CHEMISTRY • Previous Articles     Next Articles

Predicting and Virtually Screening the Selective Inhibitors of MMP-13 over MMP-1 by Molecular Descriptors and Machine Learning Methods

LI Bing-Ke1, CONG Yong1, TIAN Zhi-Yue1, XUE Ying1,2   

  1. 1 College of Chemistry, Key Laboratory of Green Chemistry and Technology, Ministry of Education, Sichuan University, Chengdu 610064, P. R. China;
    2 State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China
  • Received:2013-07-05 Revised:2013-11-04 Published:2014-01-01
  • Contact: XUE Ying E-mail:yxue@scu.edu.cn
  • Supported by:

    The project was supported by the National Natural Science Foundation of China (21173151).

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Abstract:

Matrix metalloproteinase-13 (MMP-13) is an interesting target for the prevention and therapy of osteoarthritis (OA). Interruption of MMP-13 activity with an inhibitor has the potential to affect OA. However, a broad-spectrum inhibitor, which restrains the other members of the MMP family, especially MMP-1, can cause musculoskeletal syndrome. So, the design and discovery of potential and highly selective inhibitors for MMP-13 over MMP-1 are necessary and of great significance for the development of novel therapeutic agents against OA. Two machine-learning (ML) methods, support vector machine and random forest (RF), were explored in this work to develop classification models for predicting selective inhibitors of MMP-13 over MMP-1 from diverse compounds. These ML models achieved promising prediction accuracies. Among the two ML models, RF gave the better performance, i.e., 97.58% for MMP-13 selective inhibitors and 100% for non-inhibitors. We also used different feature selection methods to extract the molecular features most relevant to selective inhibition of MMP-13 over MMP-1 from the two models. In addition, the betterperforming RF model was used to perform virtual screening of MMP-13 selective inhibitors against the "fragment-like" subset of the ZINC database to enrich the potential active agents, thereby obtaining a series of the most potent candidates. Our study suggests that ML methods, particularly RF, are potentially useful for facilitating the discovery of MMP-13 inhibitors and for identifying the molecular descriptors associated with MMP-13 selective inhibitors.

Key words: Matrix metalloproteinase-13, Selective inhibitor, Machine learning method, Support vector machine, Random forest, Virtual screening