Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Issue 12 (11th November 2019)

• Record Type: Journal Article
• Authors: Senior, Andrew W.; Evans, Richard; Jumper, John; Kirkpatrick, James; Sifre, Laurent; Green, Tim; Qin, Chongli; Žídek, Augustin; Nelson, Alexander W. R.; Bridgland, Alex; Penedones, Hugo; Petersen, Stig; Simonyan, Karen; Crossan, Steve; Kohli, Pushmeet; Jones, David T.; Silver, David; Kavukcuoglu, Koray; Hassabis, Demis
• Is Part Of: Proteins. Volume 87:Issue 12(2019)
• Page Start: 1141
• Page End: 1148
• Publication Date: 2019-11-11
• Subjects: CASP -- deep learning -- machine learning -- protein structure prediction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.25834
• Languages: English
• ISSNs: 0887-3585

Abstract

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.