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Premium Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)
Author(s)
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
Publication year2019
Publication title
proteins: structure, function, and bioinformatics
Resource typeJournals
PublisherJohn Wiley & Sons
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.
Subject(s)algorithm , artificial intelligence , artificial neural network , biochemistry , casp , chemistry , computer science , domain (mathematical analysis) , gradient descent , machine learning , mathematical analysis , mathematics , network structure , pattern recognition (psychology) , protein structure , protein structure prediction , ranking (information retrieval) , segmentation
Language(s)English
SCImago Journal Rank1.699
H-Index191
eISSN1097-0134
pISSN0887-3585
DOI10.1002/prot.25834

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