Using distances between Top-n-gram and residue pairs for protein remote homology detection

Qingcai Chen,Quan Zou,Bin Liu,Ruifeng Xu,Xiaolong Wang,Jinghao Xu

doi:10.1186/1471-2105-15-s2-s3

Qingcai Chen, Quan Zou + Show 4 more

Open Access

https://doi.org/10.1186/1471-2105-15-s2-s3

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Abstract

BackgroundProtein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Currently, discriminative methods based on Support Vector Machines (SVMs) achieve the state-of-the-art performance. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods.ResultsTwo new methods for protein remote homology detection were proposed, called SVM-DR and SVM-DT. SVM-DR is a sequence-based method, in which the feature vector representation for protein is based on the distances between residue pairs. SVM-DT is a profile-based method, which considers the distances between Top-n-gram pairs. Top-n-gram can be viewed as a profile-based building block of proteins, which is calculated from the frequency profiles. These two methods are position dependent approaches incorporating the sequence-order information of protein sequences. Various experiments were conducted on a benchmark dataset containing 54 families and 23 superfamilies. Experimental results showed that these two new methods are very promising. Compared with the position independent methods, the performance improvement is obvious. Furthermore, the proposed methods can also provide useful insights for studying the features of protein families.ConclusionThe better performance of the proposed methods demonstrates that the position dependant approaches are efficient for protein remote homology detection. Another advantage of our methods arises from the explicit feature space representation, which can be used to analyze the characteristic features of protein families. The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp

Highlights

Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application
The impact of dMAX value on the performance of Support Vector Machine (SVM)-Distance-based Top-n-gram (DT) and SVM-Distance-based Residue approach (DR) There is a parameter dMAX in the proposed methods, which would impact on the predictive performance of the proposed methods SVM-DT and SVM-DR. dmax can be any integer between 0 and the length of the longest protein sequence in the dataset
In this study, we proposed two methods SVM-DT and SVM-DR for protein remote homology detection, in which the feature vectors were constructed based on the occurrences of Top-n-gram pairs or residue pairs at distances shorter than a distance threshold dMAX

Summary

Introduction

Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods. Protein remote homology detection is still a changing problem in bioinformatics and accurately and efficiently computational approaches are needed. During the past two decades, some computational methods have been proposed for protein remote homology detection, which can be mainly divided into two major categories: generative methods and discriminative algorithms. Solutions of protein remote homology detection were generative methods, which trained a model to represent a protein family and evaluated a query sequence according to this model. Some online servers are available, including FORTE [7], RANKPOOP [8], webPRC [9], PHYRE [10], GenThreader [11], COMA [12], and, Bioshell [13]

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Conclusion