S2L-PSIBLAST: a supervised two-layer search framework based on PSI-BLAST for protein remote homology detection

Author:

Jin Xiaopeng1,Liao Qing1ORCID,Liu Bin123ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China

2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

3. Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China

Abstract

Abstract Motivation Protein remote homology detection is a challenging task for the studies of protein evolutionary relationships. PSI-BLAST is an important and fundamental search method for detecting homology proteins. Although many improved versions of PSI-BLAST have been proposed, their performance is limited by the search processes of PSI-BLAST. Results For further improving the performance of PSI-BLAST for protein remote homology detection, a supervised two-layer search framework based on PSI-BLAST (S2L-PSIBLAST) is proposed. S2L-PSIBLAST consists of a two-level search: the first-level search provides high-quality search results by using SMI-BLAST framework and double-link strategy to filter the non-homology protein sequences, the second-level search detects more homology proteins by profile-link similarity, and more accurate ranking lists for those detected protein sequences are obtained by learning to rank strategy. Experimental results on the updated version of Structural Classification of Proteins-extended benchmark dataset show that S2L-PSIBLAST not only obviously improves the performance of PSI-BLAST, but also achieves better performance on two improved versions of PSI-BLAST: DELTA-BLAST and PSI-BLASTexB. Availability and implementation http://bliulab.net/S2L-PSIBLAST. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

National Key R&D Program of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference45 articles.

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