ArticlePLoS computational biology2014
MRFalign: protein homology detection through alignment of Markov random fields.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 99 citations in OpenAlex.
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- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.Proteins · 2026Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
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- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields.Molecules (Basel, Switzerland) · 2022Article
- ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.Proteins · 2021Article
- Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.Microbial genomics · 2021Article
- PPalign: optimal alignment of Potts models representing proteins with direct coupling information.BMC bioinformatics · 2021Article
- Deep template-based protein structure prediction.PLoS computational biology · 2021Article
- Master Blaster: an approach to sensitive identification of remotely related proteins.Scientific reports · 2021Article
- Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.Frontiers in molecular biosciences · 2021Review
- Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading.Scientific reports · 2020Article
- Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.BMC bioinformatics · 2019Article
- Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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.
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