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Source code for the distogram, reference distogram and torsion prediction neural networks, together with the neural network weights and input data for the CASP13 targets are available for research and non-commercial use at -research/tree/master/alphafold_casp13. We make use of several open-source libraries to conduct our experiments, particularly HHblits36, PSI-BLAST37 and the machine-learning framework TensorFlow ( ) along with the TensorFlow library Sonnet ( ), which provides implementations of individual model components50. We also used Rosetta9 under license.

We thank C. Meyer for assistance in preparing the paper; B. Coppin, O. Vinyals, M. Barwinski, R. Sun, C. Elkin, P. Dolan, M. Lai and Y. Li for their contributions and support; O. Ronneberger for reading the paper; the rest of the DeepMind team for their support; the CASP13 organisers and the experimentalists whose structures enabled the assessment.

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R.E., J.J., J.K., L.S., A.W.S., C.Q., T.G., A.., A.B., H.P. and K.S. designed and built the AlphaFold system with advice from D.S., K.K. and D.H. D.T.J. provided advice and guidance on protein structure prediction methodology. S.P. contributed to software engineering. S.C., A.W.R.N., K.K. and D.H. managed the project. J.K., A.W.S., T.G., A.., A.B., R.E., P.K. and J.J. analysed the CASP results for the paper. A.W.S. and J.K. wrote the paper with contributions from J.J., R.E., L.S., T.G., A.B., A.., D.T.J., P.K., K.K. and D.H. A.W.S. led the team.

a, The overall folding system. Feature extraction stages (constructing the MSA using sequence database search and computing MSA-based features) are shown in yellow; the structure-prediction neural network in green; potential construction in red; and structure realization in blue. b, The layers used in one block of the deep residual convolutional network. The dilated convolution is applied to activations of reduced dimension. The output of the block is added to the representation from the previous layer. The bypass connections of the residual network enable gradients to pass back through the network undiminished, permitting the training of very deep networks.

Often protein function can be inferred by finding homologous proteins of known function. Here we show that the FM predictions of AlphaFold give greater accuracy in a structure-based search for homologous domains in the CATH database. For each of the FM or TBM/FM domains, the top-one submission and ground truth are compared to all 30,744 CATH S40 non-redundant domains with TM-align53. For the 36 domains for which there is a good ground-truth match (score > 0.5), we show the percentage of decoys for which a domain with the same CATH code (CATH in red, CA in green; CAT results are close to CATH results) as the top ground-truth match is in the top-k matches with score > 0.5. Curves are shown for AlphaFold and the next-best group (322). AlphaFold predictions determine the matching fold more accurately. Determination of the matching CATH domain can provide insights into the function of a new protein.

The development of computational methods to predict three-dimensional (3D) protein structures from the protein sequence has proceeded along two complementary paths that focus on either the physical interactions or the evolutionary history. The physical interaction programme heavily integrates our understanding of molecular driving forces into either thermodynamic or kinetic simulation of protein physics16 or statistical approximations thereof17. Although theoretically very appealing, this approach has proved highly challenging for even moderate-sized proteins due to the computational intractability of molecular simulation, the context dependence of protein stability and the difficulty of producing sufficiently accurate models of protein physics. The evolutionary programme has provided an alternative in recent years, in which the constraints on protein structure are derived from bioinformatics analysis of the evolutionary history of proteins, homology to solved structures18,19 and pairwise evolutionary correlations20,21,22,23,24. This bioinformatics approach has benefited greatly from the steady growth of experimental protein structures deposited in the Protein Data Bank (PDB)5, the explosion of genomic sequencing and the rapid development of deep learning techniques to interpret these correlations. Despite these advances, contemporary physical and evolutionary-history-based approaches produce predictions that are far short of experimental accuracy in the majority of cases in which a close homologue has not been solved experimentally and this has limited their utility for many biological applications.

We demonstrate in Fig. 2a that the high accuracy that AlphaFold demonstrated in CASP14 extends to a large sample of recently released PDB structures; in this dataset, all structures were deposited in the PDB after our training data cut-off and are analysed as full chains (see Methods, Supplementary Fig. 15 and Supplementary Table 6 for more details). Furthermore, we observe high side-chain accuracy when the backbone prediction is accurate (Fig. 2b) and we show that our confidence measure, the predicted local-distance difference test (pLDDT), reliably predicts the C local-distance difference test (lDDT-C) accuracy of the corresponding prediction (Fig. 2c). We also find that the global superposition metric template modelling score (TM-score)27 can be accurately estimated (Fig. 2d). Overall, these analyses validate that the high accuracy and reliability of AlphaFold on CASP14 proteins also transfers to an uncurated collection of recent PDB submissions, as would be expected (see Supplementary Methods 1.15 and Supplementary Fig. 11 for confirmation that this high accuracy extends to new folds).

AlphaFold greatly improves the accuracy of structure prediction by incorporating novel neural network architectures and training procedures based on the evolutionary, physical and geometric constraints of protein structures. In particular, we demonstrate a new architecture to jointly embed multiple sequence alignments (MSAs) and pairwise features, a new output representation and associated loss that enable accurate end-to-end structure prediction, a new equivariant attention architecture, use of intermediate losses to achieve iterative refinement of predictions, masked MSA loss to jointly train with the structure, learning from unlabelled protein sequences using self-distillation and self-estimates of accuracy.

Predictions of side-chain tag_hash_112 angles as well as the final, per-residue accuracy of the structure (pLDDT) are computed with small per-residue networks on the final activations at the end of the network. The estimate of the TM-score (pTM) is obtained from a pairwise error prediction that is computed as a linear projection from the final pair representation. The final loss (which we term the frame-aligned point error (FAPE) (Fig. 3f)) compares the predicted atom positions to the true positions under many different alignments. For each alignment, defined by aligning the predicted frame (Rk, tk) to the corresponding true frame, we compute the distance of all predicted atom positions xi from the true atom positions. The resulting Nframes Natoms distances are penalized with a clamped L1 loss. This creates a strong bias for atoms to be correct relative to the local frame of each residue and hence correct with respect to its side-chain interactions, as well as providing the main source of chirality for AlphaFold (Supplementary Methods 1.9.3 and Supplementary Fig. 9).

Although AlphaFold has a high accuracy across the vast majority of deposited PDB structures, we note that there are still factors that affect accuracy or limit the applicability of the model. The model uses MSAs and the accuracy decreases substantially when the median alignment depth is less than around 30 sequences (see Fig. 5a for details). We observe a threshold effect where improvements in MSA depth over around 100 sequences lead to small gains. We hypothesize that the MSA information is needed to coarsely find the correct structure within the early stages of the network, but refinement of that prediction into a high-accuracy model does not depend crucially on the MSA information. The other substantial limitation that we have observed is that AlphaFold is much weaker for proteins that have few intra-chain or homotypic contacts compared to the number of heterotypic contacts (further details are provided in a companion paper39). This typically occurs for bridging domains within larger complexes in which the shape of the protein is created almost entirely by interactions with other chains in the complex. Conversely, AlphaFold is often able to give high-accuracy predictions for homomers, even when the chains are substantially intertwined (Fig. 5b). We expect that the ideas of AlphaFold are readily applicable to predicting full hetero-complexes in a future system and that this will remove the difficulty with protein chains that have a large number of hetero-contacts.

The following versions of public datasets were used in this study. Our models were trained on a copy of the PDB5 downloaded on 28 August 2019. For finding template structures at prediction time, we used a copy of the PDB downloaded on 14 May 2020, and the PDB7066 clustering database downloaded on 13 May 2020. For MSA lookup at both training and prediction time, we used Uniref9067 v.2020_01, BFD, Uniclust3036 v.2018_08 and MGnify6 v.2018_12. For sequence distillation, we used Uniclust3036 v.2018_08 to construct a distillation structure dataset. Full details are provided in Supplementary Methods 1.2.

The network is supervised by the FAPE loss and a number of auxiliary losses. First, the final pair representation is linearly projected to a binned distance distribution (distogram) prediction, scored with a cross-entropy loss. Second, we use random masking on the input MSAs and require the network to reconstruct the masked regions from the output MSA representation using a BERT-like loss37. Third, the output single representations of the structure module are used to predict binned per-residue lDDT-C values. Finally, we use an auxiliary side-chain loss during training, and an auxiliary structure violation loss during fine-tuning. Detailed descriptions and weighting are provided in the Supplementary Information. 006ab0faaa