About ProtDec-LTR3.0

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 applications (such as drug design). Previous predictors ProtDec-LTR1.0 and ProtDec-LTR2.0 showed that combining different basic ranking methods by using Learning to Rank in a supervised manner is an efficient strategy for protein remote homology detection.

In this study, 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 a feature mapping strategy, and a predictor called ProtDec-LTR3.0 has been proposed. Rigorous tests on a widely used benchmark dataset showed that the ProtDec-LTR3.0 predictor outperformed both ProtDec-LTR1.0 and ProtDec-LTR2.0, and other 12 existing state-of-the-art predictors, indicating that the ProtDec-LTR3.0 predictor is an efficient method for protein remote homology detection, and will become a useful tool for protein sequence analysis.