Prediction of Protein-Protein Interactions Based on Molecular Interface Features and the Support Vector Machine

Title:Prediction of Protein-Protein Interactions Based on Molecular Interface Features and the Support Vector Machine

Volume: 8 Issue: 1

Author(s): Weiqiang Zhou, Hong Yan, Xiaodan Fan and Quan Hao

Affiliation:

Keywords: Alpha shape, protein-protein interaction, structural alignment, Residue Index, TM-Score, Curvature Index, Support Vector Machine (SVM), Matthews correlation coefficient, False negative, Atom Index

Abstract: Protein-protein interactions play important roles in many biological progresses. Previous studies about proteinprotein interactions were mainly based on sequence analysis. As more 3D structural information can be obtained from protein-protein complexes, structural analysis becomes feasible and useful. In this study, we used structural alignment to predict protein-binding sites and analyzed interface properties using 3D alpha shape. We have developed a method for protein-protein interaction prediction. The result indicates good performance of our method in discriminating proteinbinding structures from non-protein-binding structures. In the experiment, our method shows best Matthews correlation coefficient of 0.204.

Cite this article as:

Zhou Weiqiang, Yan Hong, Fan Xiaodan and Hao Quan, Prediction of Protein-Protein Interactions Based on Molecular Interface Features and the Support Vector Machine, Current Bioinformatics 2013; 8 (1) . https://dx.doi.org/10.2174/1574893611308010003