Paper Info
Title
PHR-search: a search framework for protein remote homology detection based on the predicted protein hierarchical relationships
Abstract
Protein remote homology detection is one of the most fundamental research tool for protein structure and function prediction. Most search methods for protein remote homology detection are evaluated based on the Structural Classification of Proteins-extended (SCOPe) benchmark, but the diverse hierarchical structure relationships between the query protein and candidate proteins are ignored by these methods. In order to further improve the predictive performance for protein remote homology detection, a search framework based on the predicted protein hierarchical relationships (PHR-search) is proposed. In the PHR-search framework, the superfamily level prediction information is obtained by extracting the local and global features of the Hidden Markov Model (HMM) profile through a convolution neural network and it is converted to the fold level and class level prediction information according to the hierarchical relationships of SCOPe. Based on these predicted protein hierarchical relationships, filtering strategy and re-ranking strategy are used to construct the two-level search of PHR-search. Experimental results show that the PHR-search framework achieves the state-of-the-art performance by employing five basic search methods, including HHblits, JackHMMER, PSI-BLAST, DELTA-BLAST and PSI-BLASTexB. Furthermore, the web server of PHR-search is established, which can be accessed at http://bliulab.net/PHR-search.
Year
DOI
Venue
2022
10.1093/bib/bbab609
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
Protein remote homology detection, Predicted protein hierarchical relationships, Hidden Markov Model (HMM) profile
Journal
23
Issue
ISSN
Citations 
2
1467-5463
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Xiaopeng Jin111.36
Xiaoling Luo200.34
Bin Liu341933.30