Graphical Models of Residue Coupling in Protein Families
Abstract
Many statistical measures and algorithmic techniques have been proposed for studying residue coupling in protein families. Generally speaking, two residue positions are considered coupled if, in the sequence record, some of their amino acid type combinations are significantly more common than others. While the proposed approaches have proven useful in finding and describing coupling, a significant missing component is a formal probabilistic model that explicates and compactly represents the coupling, integrates information about sequence,structure, and function, and supports inferential procedures for analysis, diagnosis, and prediction.We present an approach to learning and using probabilistic graphical models of residue coupling. These models capture significant conservation and coupling constraints observable ina multiply-aligned set of sequences. Our approach can place a structural prior on considered couplings, so that all identified relationships have direct mechanistic explanations. It can also incorporate information about functional classes, and thereby learn a differential graphical model that distinguishes constraints common to all classes from those unique to individual classes. Such differential models separately account for class-specific conservation and family-wide coupling, two different sources of sequence covariation. They are then able to perform interpretable functional classification of new sequences, explaining classification decisions in terms of the underlying conservation and coupling constraints. We apply our approach in studies of both G protein-coupled receptors and PDZ domains, identifying and analyzing family-wide and class-specific constraints, and performing functional classification. The results demonstrate that graphical models of residue coupling provide a powerful tool for uncovering, representing, and utilizing significant sequence structure-function relationships in protein families.
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Within a protein family, structure and function can be inferred from the patterns in sequences.
“Within a protein family, structure and function can be inferred from the patterns in sequences (4, 5).”
From Evolutionary-scale prediction of atomic level protein structure with a language model (2022) · cites this paper as [5]
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- Authors
- Thomas, J., Ramakrishnan, N., & Bailey-Kellogg, C.
- Affiliations
- Dartmouth College (US)
- Virginia Tech Transportation Institute (US)
- ORCID
- Ramakrishnan, N.
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2008-04
- Volume
- 5
- Issue
- 2
- Pages
- 183-197
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W2106694138
- Cited by
- 77
- References
- 53
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 3 · 2022: 3 · 2021: 9 · 2020: 4 · 2019: 1 · 2018: 4 · 2017: 2 · 2016: 3 · 2015: 3 · 2014: 3 · 2013: 6 · 2012: 4
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein families, probabilistic graphical models, conservation restrictions, structural priors, functional classification, G protein-coupled receptors, PDZ domain, sequence-structure-function relationships, multiple sequence alignment
- MeSH terms
- Amino Acid Sequence, Animals, Artificial Intelligence, Cattle, Computer Graphics, Computer Simulation, Humans, Models, Molecular, Proteins, Rhodopsin, Models, Statistical, Likelihood Functions, Sequence Alignment, Receptors, G-Protein-Coupled, PDZ Domains
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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