Prediction of potential drug targets based on simple sequence properties

Luhua Lai,Qingliang Li

doi:10.1186/1471-2105-8-353

Luhua Lai, Qingliang Li

Open Access

https://doi.org/10.1186/1471-2105-8-353

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Abstract

BackgroundDuring the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.ResultsBased on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research.ConclusionWe have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

Highlights

During the past decades, research and development in drug discovery have attracted much attention and efforts
Kernel function selection Three commonly used kernels, linear, polynomial and radial basis function (RBF) were tested in order to find the best performance support vector machine (SVM) model. 10-fold cross-validation was done to evaluate the performance of each kernel functions
SVM with RBF kernel outperformed the other two, which gave an overall accuracy of 83%, sensitivity of 85% and specificity of 80%, respectively

Summary

Introduction

Research and development in drug discovery have attracted much attention and efforts. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Great efforts have been exerted on drug research and development during the past decades, only about 500 drug targets have been identified for clinically using drugs to date[1]. This number has been revised to be 324[2], which indicates that current pharmaceutical industry relies on only a small pool of drug targets, compared to the large number of proteins available in human genome[3]. Han et al [16] used machine learning methods to build a model with 1,484 clinical and research drug targets from TTD database[9], and predicted druggable proteins among different organisms

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