Protein structure prediction aims to determine the three-dimensional shape of a protein from 11 its amino acid sequence1. This problem is of fundamental importance to biology as the struc12 ture of a protein largely determines its function2 but can be hard to determine experimen13 tally. In recent years, considerable progress has been made by leveraging genetic informa14 tion: analysing the co-variation of homologous sequences can allow one to infer which amino 15 acid residues are in contact, which in turn can aid structure prediction3. In this work, we 16 show that we can train a neural network to accurately predict the distances between pairs 17 of residues in a protein which convey more about structure than contact predictions. With 18 this information we construct a potential of mean force4 that can accurately describe the 19 shape of a protein. We find that the resulting potential can be optimised by a simple gradient 20 descent algorithm, to realise structures without the need for complex sampling procedures. 21 The resulting system, named AlphaFold, has been shown to achieve high accuracy, even for 22 sequences with relatively few homologous sequences. In the most recent Critical Assessment 23 of Protein Structure Prediction5 (CASP13), a blind assessment of the state of the field of pro24 tein structure prediction, AlphaFold created high-accuracy structures (with TM-scores† of 25 0.7 or higher) for 24 out of 43 free modelling domains whereas the next best method, using 26 sampling and contact information, achieved such accuracy for only 14 out of 43 domains. 27 AlphaFold represents a significant advance in protein structure prediction. We expect the in28 creased accuracy of structure predictions for proteins to enable insights in understanding the 29 function and malfunction of these proteins, especially in cases where no homologous proteins 30 have been experimentally determined7. 31
分享
引用
