Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions

Lei Wang,Xin Yan,Wei Zhang,Li-Ping Li,Zhu-Hong You

doi:10.1038/s41598-020-62891-2

Abstract

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.

Highlights

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery
Cao et al proposed a new model for predicting DTIs which combines the protein information encoded by physicochemical and biochemical properties with drug molecules structures information encoded by MACCS substructure fingerings[25]
In this work, based on the assumption that the relationship between drugs and targets is largely influenced by the drug molecular structure and protein amino acid sequence, we proposed a novel model to predict DTIs by fusing protein sequence information and molecular fingerprint information

Summary

Introduction

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Xia et al designed semi-supervised model called NetLapRLS which combines the information of the known drug-protein interaction network with genomic sequence data and chemical structure In this model, the final result is predicted by the combination of the classifiers, and the method has achieved good performance because of utilizing the integrate information and unlabeled data[21]. Under the premise of the theory that the interaction among drug and target protein depends largely on the chemical sub-structures of drug compound and the structure of target protein sequence[11,27,28,29], we design a new in silico model to predict DTIs. Compared with the proposed methods, we introduce a protein sequence transformation method which can carry the information of biological evolution.

Methods

Results

Conclusion