2019/10/11 by Andrew Senior, Richard Evans, John Jumper +16 · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · #Protein Structure and Dynamics #Machine Learning in Bioinformatics #Enzyme Structure and Function
paper · pdf · doi:10.1002/prot.25834
openalex publication_date 2019/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
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 GDTTS. 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 GDTTS of 61.4). The system produced high-accuracy structures (with GDTTS 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.