title: MRFalign: protein homology detection through alignment of Markov random fields.
abstract: Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.
authors.forename: Jianzhu
authors.initials: J
authors.lastname: Ma
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.
authors.forename: Sheng
authors.initials: S
authors.lastname: Wang
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D003201:Computers; D021621:Imaging, Three-Dimensional; D008390:Markov Chains; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins