Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures

Fan-Rong Meng,Yong Zhou,Ji-Yong An,Xing Chen,Zhu-Hong You

doi:10.3390/molecules22071119

Abstract

Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Highlights

The identification of drug–target interactions (DTI) has recently emerged as an area of intense research activity due to its important role in finding new proteins to target for drug development and discovering new drug candidates [1,2]
In order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets
In order to verify the effectiveness of the proposed method, we carry out the experiment enzyme, ion channel, GPCR, and nuclear receptor datasets through employing five-fold crosson enzyme, ion channel, GPCR, and nuclear receptor datasets through employing five-fold validation tests respectively

Summary

Introduction

The identification of drug–target interactions (DTI) has recently emerged as an area of intense research activity due to its important role in finding new proteins to target for drug development and discovering new drug candidates [1,2]. The target proteins of many drugs are not complete or even not known. Much effort has been devoted to using experimental methods to identify drug–protein interactions. These experimental methods are both time-consuming and expensive. It often costs billions of dollars for developing a successful novel chemistry-based drug and takes nearly a decade for introducing the drug to market. There are only few drug candidates that can be approved to reach the market by Food and Drug Administration (FDA) [3,4,5]

Methods

Results

Conclusion