Enhancing HMM-based protein profile-profile alignment with structural features and evolutionary coupling information
1 LexisNexis | Risk Solutions | Healthcare, Orlando, FL 32811, USA
2 Computer Science Department, Informatics Institute, C. Bond Life Science Center, University of Missouri-Columbia, Columbia, MO 65211, USA
BMC Bioinformatics 2014, 15:252 doi:10.1186/1471-2105-15-252Published: 25 July 2014
Protein sequence profile-profile alignment is an important approach to recognizing remote homologs and generating accurate pairwise alignments. It plays an important role in protein sequence database search, protein structure prediction, protein function prediction, and phylogenetic analysis.
In this work, we integrate predicted solvent accessibility, torsion angles and evolutionary residue coupling information with the pairwise Hidden Markov Model (HMM) based profile alignment method to improve profile-profile alignments. The evaluation results demonstrate that adding predicted relative solvent accessibility and torsion angle information improves the accuracy of profile-profile alignments. The evolutionary residue coupling information is helpful in some cases, but its contribution to the improvement is not consistent.
Incorporating the new structural information such as predicted solvent accessibility and torsion angles into the profile-profile alignment is a useful way to improve pairwise profile-profile alignment methods.