ArticleProceedings of the National Academy of Sciences of the United States of America2017
Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Disentangling coevolutionary constraints for modeling protein conformational heterogeneity.Communications chemistry · 2026Article
- Designing modular genetic sensors with TtgR/AcrR family transcriptional regulators by using a statistical and data-driven approach.Nucleic acids research · 2025Article
- Phase-separating fusion proteins drive cancer by upsetting transcription regulation.Genome biology · 2025Article
- Transmembrane Homodimers Interface Identification: Predicting Interface Residues in Alpha-Helical Transmembrane Protein Homodimers Using Sequential and Structural Features.International journal of molecular sciences · 2025Article
- Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- 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
- Protein language model-embedded geometric graphs power inter-protein contact prediction.eLife · 2024Article
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies.Biophysical journal · 2024Article
- Molecular Dynamics and Other HPC Simulations for Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- Chasing long-range evolutionary couplings in the AlphaFold era.Biopolymers · 2023Article
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- Deep graph learning of inter-protein contacts.Bioinformatics (Oxford, England) · 2022Article
- CoCoNet-boosting RNA contact prediction by convolutional neural networks.Nucleic acids research · 2021Article
- Modeling of protein complexes in CASP14 with emphasis on the interaction interface prediction.Proteins · 2021Article
- High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments · 2021Article
- Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.Briefings in bioinformatics · 2021Article
- DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning.Scientific reports · 2021Article
- On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.PLoS computational biology · 2021Article
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6 authors.
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Abstract
Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.
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