ArticleProceedings of the National Academy of Sciences of the United States of America2019
Distance-based protein folding powered by deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers.
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181 citing papers in PubMed.
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
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- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search.Briefings in bioinformatics · 2026Article
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- AI-Driven Protein Research: From Prediction to Design.Methods in molecular biology (Clifton, N.J.) · 2026Review
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16.Communications biology · 2025Article
- A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.Briefings in bioinformatics · 2025Article
- Decoding the limits of deep learning in molecular docking for drug discovery.Chemical science · 2025Article
- Comprehensive Molecular Profiling of AcrAB-TolC Efflux Pump Genes in Salmonella typhi Isolates from Typhoid Infected Patients.Current microbiology · 2025Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.Journal of nanobiotechnology · 2025Review
- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Cross-regulations of two connected domains form a mechanical circuit for steady force transmission during clathrin-mediated endocytosis.Cell reports · 2024Article
- Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins.Biomolecules · 2024Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- Foundation models in molecular biology.Biophysics reports · 2024Article
121 more citing papers are in PubMed but not listed here.
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Abstract
Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs, even coupled with time-consuming conformation sampling with fragments. We show that we can accurately predict interresidue distance distribution of a protein by deep learning, even for proteins with ∼60 sequence homologs. Using only the geometric constraints given by the resulting distance matrix we may construct 3D models without involving extensive conformation sampling. Our method successfully folded 21 of the 37 CASP12 hard targets with a median family size of 58 effective sequence homologs within 4 h on a Linux computer of 20 central processing units. In contrast, DCA-predicted contacts cannot be used to fold any of these hard targets in the absence of extensive conformation sampling, and the best CASP12 group folded only 11 of them by integrating DCA-predicted contacts into fragment-based conformation sampling. Rigorous experimental validation in CASP13 shows that our distance-based folding server successfully folded 17 of 32 hard targets (with a median family size of 36 sequence homologs) and obtained 70% precision on the top L/5 long-range predicted contacts. The latest experimental validation in CAMEO shows that our server predicted correct folds for 2 membrane proteins while all of the other servers failed. These results demonstrate that it is now feasible to predict correct fold for many more proteins lack of similar structures in the Protein Data Bank even on a personal computer.
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