Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Open accessAbstract
Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at http://plmdca.csc.kth.se/.
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Covarying residues in proteins are frequently found to be close together in the tertiary structure.
“Analysis of amino acid residue covariation in deep multiple sequence alignments (MSAs) has revealed that covarying residues in proteins are frequently found to be close together in the tertiary structure.”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [3]
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- Authors
- Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E.
- Affiliations
- KTH Royal Institute of Technology (SE)
- AlbaNova (SE)
- Tsinghua University (CN)
- Biologie Computationnelle, Quantitative et Synthétique (FR)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2013-01-11
- Volume
- 87
- Issue
- 1
- Pages
- 012707-012707
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2137566700
- Cited by
- 661
- References
- 49
- Citations by year
- 2026: 27 · 2025: 27 · 2024: 54 · 2023: 43 · 2022: 61 · 2021: 73 · 2020: 51 · 2019: 72 · 2018: 58 · 2017: 63 · 2016: 56 · 2015: 41 · 2014: 23 · 2013: 11 · 2012: 1
- FWCI
- 19.54
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review E (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein contact prediction, pseudo-likelihood, Potts model, direct coupling analysis, protein coevolution, indirect interactions
- MeSH terms
- Binding Sites, Computer Simulation, Models, Chemical, Models, Molecular, Protein Binding, Proteins, Models, Statistical, Protein Interaction Mapping
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