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Pred-binding: large-scale protein-ligand binding affinity prediction.

Authors
  • Shar, Piar Ali1
  • Tao, Weiyang1
  • Gao, Shuo1
  • Huang, Chao1
  • Li, Bohui1
  • Zhang, Wenjuan1
  • Shahen, Mohamed1
  • Zheng, Chunli1
  • Bai, Yaofei1
  • Wang, Yonghua1
Type
Published Article
Journal
Journal of Enzyme Inhibition and Medicinal Chemistry
Publisher
Informa UK (Taylor & Francis)
Publication Date
December 2016
Volume
31
Issue
6
Pages
1443–1450
Identifiers
DOI: 10.3109/14756366.2016.1144594
PMID: 26888050
Source
Medline
Keywords
License
Unknown

Abstract

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

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