PROTDEC-LTR3.0: Protein remote homology detection by incorporating profile-based features into learning to rank

Bin Liu*, Yulin Zhu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

Protein remote homology detection is one of the most challenging problems in the field of protein sequence analysis, which is an important step for both theoretical research (such as the understanding of structures and functions of proteins) and drug design. Previous studies have shown that combining different ranking methods via learning to the rank algorithm is an effective strategy for remote protein homology detection, and the performance can be further improved by the protein similarity networks. In this paper, we improved the ProtDec-LTR1.0 and ProtDec-LTR2.0 predictors by incorporating three profile-based features (Top-1-gram, Top-2-gram, and ACC) into the framework of learning to rank via feature mapping strategies. The predictive performance was further refined by the pagerank (PR) algorithm and hyperlink-induced topic search (HITS) algorithm. Finally, a predictor called ProtDec-LTR3.0 was proposed. Rigorous tests on two widely used benchmark datasets showed that the ProtDec-LTR3.0 predictor outperformed both ProtDec-LTR1.0 and ProtDec-LTR2.0, and other nine existing state-of-the-art predictors, indicating that the ProtDec-LTR3.0 is an efficient method for protein remote homology detection, and will become a useful tool for protein sequence analysis. A user-friendly web server of the ProtDec-LTR3.0 predictor was established for the convenience of users, which can be accessed at http://bliulab.net/ProtDec-LTR3.0/.

Original languageEnglish
Article number8765711
Pages (from-to)102499-102507
Number of pages9
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Feature mapping strategy
  • Hyperlink-induced topic search
  • Learning to rank
  • Pagerank
  • Profile-based features
  • Protein remote homology detection

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