MRFalign: Protein Homology Detection through Alignment of Markov Random Fields

By Jianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu. 2014

74 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Probabilistic & Bayesian Methods, Computational Biology & Bioinformatics

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Citations in the corpus

DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “ods were extended to align the profiles of two proteins. Profilesequence alignment method [13] and profile-profile alignment methods based hidden Markov model (HMM) [9] or Markov random fields (MRFs) [14] are more sensitive in recognize proteins that have the same fold, but little sequence similarit”
RaptorX-Property: a web server for protein structure property prediction (2016, background): “A profile is derived from multiple sequence alignment (MSA) of sequence homologs in a protein family (12).”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, background): “The order/disorder state of a residue has a strong relationship with the residue’s substitution and evolution [46–48].”
Improved protein contact predictions with the MetaPSICOV2 server in CASP12 (2017, methodology): “Sequence covariation analysis has emerged as a powerful technique for accurately predicting contacts in protein 3D structures (Marks et al. 2011, Jones et al. 2012, 2015; Kaj (cid:1) an et al. 2014, Ma et al. 2014, See-mayer et al. 2014, Buchan and Jones 2017).”