Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.

Hui Liu,Yimin Yimin,Dongmei Guo,Xiaomiao Shi,Zuowei Zhao

doi:10.1155/2015/263586

Abstract

It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

Highlights

Acquired immune deficiency syndrome (AIDS) is a severe disease which mostly causes patient’s death during its terminal period
HIV-1 protease inhibitor is a small molecule that can tightly bind to HIV-1 protease at the active cleavage sites, so that substrates which should normally be cleaved cannot bind to the protease
The inherently contained characteristics of amino acids can provide useful help for us to understand the specificity of HIV-1 protease [16]

Summary

Introduction

Acquired immune deficiency syndrome (AIDS) is a severe disease which mostly causes patient’s death during its terminal period. HIV-1 protease is an enzyme which plays an important role in the replication progress. It cleaves proteins to smaller peptides, and these peptides are used to make up some important proteins that are essential for the replication of HIV-1 [1]. Inhibition of this protease is a reliable method to interfere the virus reproduction. A good concept of which residues play more important roles in the cleavage progress is necessary. It is too costly and almost impossible to achieve these targets through experiments in laboratory. Machine learning methods can be used here to predict whether octapeptides are cleavable for the protease

Methods

Results

Conclusion