ArticleProteins2019
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13).
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
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Who cites it
133 citing papers in PubMed, 359 citations in OpenAlex.
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- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Precision Editing of NLRS Improves Effector Recognition for Enhanced Disease Resistance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Computational and Experimental Approaches and Applications of Biological NMR.Advances in experimental medicine and biology · 2026Review
- A machine learning protocol for predicting structural distributions of amyloid-forming proteins from 2D IR spectra.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AlphaFold 3: an unprecedent opportunity for fundamental research and drug development.Precision clinical medicine · 2025Review
- MECP2 Dysfunction in Rett Syndrome: Molecular Mechanisms, Multisystem Pathology, and Emerging Therapeutic Strategies.International journal of molecular sciences · 2025Review
- Enhanced Methodology for Peptide Tertiary Structure Prediction Using GRSA and Bio-Inspired Algorithm.International journal of molecular sciences · 2025Article
- Recapturing Cooperativity of α-Helix Formation and Packing in Coarse-Grained Protein Structure Modeling with Multitorsional Potentials.The journal of physical chemistry. B · 2025Article
- Leveraging protein structural information to improve variant effect prediction.Current opinion in structural biology · 2025Review
- Structural Biology in the AlphaFold Era: How Far Is Artificial Intelligence from Deciphering the Protein Folding Code?Biomolecules · 2025Review
- Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.Biomolecules · 2025Review
- Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.Genome research · 2025Review
73 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free-modeling (FM) methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 FM assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group (an average GDT_TS of 61.4). The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 FM domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.