ArticleNature communications2021
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Accurate prediction of virulence factors using pre-train protein language model and ensemble learning.BMC genomics · 2025Article
- LightRoseTTA: High-Efficient and Accurate Protein Structure Prediction Using a Light-Weight Deep Graph Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- Assessing the functional roles of coevolving PHD finger residues.Protein science : a publication of the Protein Society · 2024Article
- Foundation models in molecular biology.Biophysics reports · 2024Article
- Fast multiple sequence alignment via multi-armed bandits.Bioinformatics (Oxford, England) · 2024Article
- Protein language model-embedded geometric graphs power inter-protein contact prediction.eLife · 2024Article
- Computational drug development for membrane protein targets.Nature biotechnology · 2024Review
- MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.Briefings in bioinformatics · 2024Article
- Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production.International journal of molecular sciences · 2023Review
- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes.Nature communications · 2023Article
- Accurate and efficient protein sequence design through learning concise local environment of residues.Bioinformatics (Oxford, England) · 2023Article
- Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.Current opinion in structural biology · 2023Review
- UPP2: fast and accurate alignment of datasets with fragmentary sequences.Bioinformatics (Oxford, England) · 2023Article
- GeoPacker: A novel deep learning framework for protein side-chain modeling.Protein science : a publication of the Protein Society · 2022Article
- learnMSA: learning and aligning large protein families.GigaScience · 2022Article
- Improved AlphaFold modeling with implicit experimental information.Nature methods · 2022Article
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7 authors.
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Abstract
Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA. The key elements of CopulaNet include: (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures.
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