ReviewCold Spring Harbor perspectives in biology2024
Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.
Review in Cold Spring Harbor perspectives in biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Protein stability is determined by single-site bias rather than pairwise covariance.Nature chemical biology · 2026Article
- Predicting side chain conformations in folded proteins by AlphaFold: Perspective and challenges.Biophysical journal · 2025Article
- Considering Metabolic Context in Enzyme Evolution and Design.Biochemistry · 2025Review
- Using AlphaFold2 to Predict the Conformations of Side Chains in Folded Proteins.bioRxiv : the preprint server for biology · 2025Article
- Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large multiple sequence alignment. A central premise is that covariance between amino acid positions reflects coevolution due to a shared functional or biophysical constraint. In this review, we describe the implementation and discuss the advantages, limitations, and recent progress on two coevolution-based modeling approaches: (1) Potts models of protein sequence (direct coupling analysis [DCA]-like), and (2) the statistical coupling analysis (SCA). Each approach detects interesting features of protein sequence and structure-the former emphasizes local physical contacts throughout the structure, while the latter identifies larger evolutionarily coupled networks of residues. Recent advances in large-scale gene synthesis and high-throughput functional selection now motivate additional work to benchmark model performance across quantitative function prediction and de novo design tasks.
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Registered trials
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