Application of learning to rank to protein remote homology detection

Bioinformatics. 2015 Nov 1;31(21):3492-8. doi: 10.1093/bioinformatics/btv413. Epub 2015 Jul 10.


Motivation: Protein remote homology detection is one of the fundamental problems in computational biology, aiming to find protein sequences in a database of known structures that are evolutionarily related to a given query protein. Some computational methods treat this problem as a ranking problem and achieve the state-of-the-art performance, such as PSI-BLAST, HHblits and ProtEmbed. This raises the possibility to combine these methods to improve the predictive performance. In this regard, we are to propose a new computational method called ProtDec-LTR for protein remote homology detection, which is able to combine various ranking methods in a supervised manner via using the Learning to Rank (LTR) algorithm derived from natural language processing.

Results: Experimental results on a widely used benchmark dataset showed that ProtDec-LTR can achieve an ROC1 score of 0.8442 and an ROC50 score of 0.9023 outperforming all the individual predictors and some state-of-the-art methods. These results indicate that it is correct to treat protein remote homology detection as a ranking problem, and predictive performance improvement can be achieved by combining different ranking approaches in a supervised manner via using LTR.

Availability and implementation: For users' convenience, the software tools of three basic ranking predictors and Learning to Rank algorithm were provided at


Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods
  • Databases, Protein
  • Sequence Analysis, Protein / methods*
  • Sequence Homology, Amino Acid*
  • Software