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Improved protein structure prediction using potentials from deep learning

Nature volume 577, pages 706–710 (2020)Cite this article

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Abstract

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7.

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Fig. 1: The performance of AlphaFold in the CASP13 assessment.
Fig. 2: The folding process illustrated for CASP13 target T0986s2.
Fig. 3: Predicted distance distributions compared with true distances.
Fig. 4: TM scores versus the accuracy of the distogram, and the dependency of the TM score on different components of the potential.

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Data availability

Our training, validation and test data splits (CATH domain codes) are available from https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13. The following versions of public datasets were used in this study: PDB 2018-03-15; CATH 2018-03-16; Uniclust30 2017-10; and PSI-BLAST nr dataset (as of 15 December 2017).

Code availability

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 https://github.com/deepmind/deepmind-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 (https://github.com/tensorflow/tensorflow) along with the TensorFlow library Sonnet (https://github.com/deepmind/sonnet), which provides implementations of individual model components50. We also used Rosetta9 under license.

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Acknowledgements

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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Author notes

  1. These authors contributed equally: Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre

Authors and Affiliations

  1. DeepMind, London, UK

    Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David Silver, Koray Kavukcuoglu & Demis Hassabis

  2. The Francis Crick Institute, London, UK

    David T. Jones

  3. University College London, London, UK

    David T. Jones

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Contributions

R.E., J.J., J.K., L.S., A.W.S., C.Q., T.G., A.Ž., 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.Ž., 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.Ž., D.T.J., P.K., K.K. and D.H. A.W.S. led the team.

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Correspondence to Andrew W. Senior.

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Competing interests

A.W.S., J.K., T.G., J.J., L.S., R.E., H.P., C.Q., K.S., A.Ž. and A.B. have filed provisional patent applications relating to machine learning for predicting protein structures. The remaining authors declare no competing interests.

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Peer review information Nature thanks Mohammed AlQuraishi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Schematics of the folding system and neural network.

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.

Extended Data Fig. 2 CASP13 contact precisions.

a, Precisions (as shown in Fig. 1c) for long-range contact prediction in CASP13 for the most probable L, L/2 or L/5 contacts, where L is the length of the domain. The distance distributions used by AlphaFold (AF) in CASP13, thresholded to contact predictions, are compared with submissions by the two best-ranked contact prediction methods in CASP13: 498 (RaptorX-Contact26) and 032 (TripletRes32), on ‘all groups’ targets, with updated domain definitions for T0953s2. b, c, True distances (b) and modes of the predicted distogram (c) for CASP13 target T0990. CASP divides this chain into three domains as shown (D3 is inserted in D2) for which there are 39, 36 and 42 HHblits alignments, respectively (from the CASP website).

Extended Data Fig. 3 Analysis of structure accuracies.

a, lDDT12 versus distogram lDDT12 (see Methods, ‘Accuracy’). The distogram accuracy predicts the lDDT of the realized structure well (particularly for medium- and long-range residue pairs, as well as the TM score as shown in Fig. 4a) for both CASP13 (n = 500: 5 decoys for domains excluding T0999) and test (n = 377) datasets. Data are shown with Pearson’s correlation coefficients. b, DLDDT12 against the effective number of sequences in the MSA (Neff) normalized by sequence length (n = 377). The number of effective sequences correlates with this measure of distogram accuracy (r = 0.634). c, Structure accuracy measures, computed on the test set (n = 377), for gradient descent optimization of different forms of the potential. Top, removing terms in the potential, and showing the effect of following optimization with Rosetta relax. ‘P’ shows the significance of the potential giving different results from ‘Full’, for a two-tailed paired data t-test. ‘Bins’ shows the number of bins fitted by the spline before extrapolation and the number in the full distribution. In CASP13, splines were fitted to the first 51 of 64 bins. Bottom, reducing the resolution of the distogram distributions. The original 64-bin distogram predictions are repeatedly downsampled by a factor of 2 by summing adjacent bins, in each case with constant extrapolation beyond 18 Å (the last quarter of the bins). The two-level potential in the final row, which was designed to compare with contact predictions, is constructed by summing the probability mass below 8 Å and between 8 and 14 Å, with constant extrapolation beyond 14 Å. The TM scores in this table are plotted in Fig. 4b.

Extended Data Fig. 4 TM score versus per-target computation time computed as an average over the test set.

Structure realization requires a modest computation budget, which can be parallelized over multiple machines. Full optimization with noisy restarts (orange) is compared with initialization from sampled torsions (blue). Computation is measured as the product of the number of (CPU-based) machines and time elapsed and can be largely parallelized. Longer targets take longer to optimize. Figure 2e shows how the TM score increases with the number of repeats of gradient descent. n = 377.

Extended Data Fig. 5 AlphaFold CASP13 results.

a, The TM score for each of the five AlphaFold CASP13 submissions are shown. Simulated annealing with fragment assembly entries are shown in blue. Gradient-descent entries are shown in yellow. Gradient descent was only used for targets T0975 and later, so to the left of the black line we also show the results for a single ‘back-fill’ run of gradient descent for each earlier target using the deployed system. T0999 (1,589 residues) was manually segmented based on HHpred51 homology matching. b, Average TM scores of the AlphaFold CASP13 submissions (n = 104 domains), comparing the first model submitted, the best-of-five model (submission with highest GDT_TS), a single run of full-chain gradient descent (a CASP13 run for T0975 and later, back-fill for earlier targets) and a single CASP13 run of fragment assembly with domain segmentation (using a gradient descent submission for T0999). c, The formula-standardized (z) scores of the assessors for GDT TS + QCS52, best-of-five for CASP FM (n = 31) and FM/TBM (n = 12) domains comparing AlphaFold with the closest competitor (group 322), coloured by domain category. AlphaFold performs better (P = 0.0032, one-tailed paired statistic t-test).

Extended Data Fig. 6 Correct fold identification by structural search in CATH.

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.

Extended Data Fig. 7 Accuracy of predictions for interfaces.

Protein–protein interaction is an important domain for understanding protein function that has hitherto largely been limited to template-based models because of the need for high-accuracy predictions, although there has been moderate success54 in docking with predicted structures up to 6 Å r.m.s.d. This figure shows that the predictions by AlphaFold improve accuracy in the interface regions of chains in hetero-dimer structures and are probably better candidates for docking, although docking did not form part of the AlphaFold system and all submissions were for isolated chains rather than complexes. For the five all-groups heterodimer CASP13 targets, the full-atom r.m.s.d. values of the interface residues (residues with a ground-truth inter-chain heavy-atom distance <10 Å) are computed for the chain submissions of all groups (green), relative to the target complex. Results >8 Å are not shown. AlphaFold (blue) achieves consistently high accuracy interface regions and, for 4 out of 5 targets, predicts interfaces below <5 Å for both chains.

Extended Data Fig. 8 Ligand pocket visualizations for T1011.

T1011 (PDB 6M9T) is the EP3 receptor bound to misoprostol-FA55. a, The native structure showing the ligand in a pocket. b, c, Submission 5 (78.0 GDT TS) by AlphaFold (b), made without knowledge of the ligand, shows a pocket more similar to the true pocket than that of the best other submission (322, model 3, 68.7 GDT TS) (c). Both submissions are aligned to the native protein using the same subset of residues from the helices close to the ligand pocket and visualized with the interior pocket together with the native ligand position.

Extended Data Fig. 9 Attribution map of distogram network.

The contact probability map of T0986s2, and the summed absolute value of the Integrated Gradient, ∑c|SI,Ji,j,c|, of the input two-dimensional features with respect to the expected distance between five different pairs of residues (I,J): (1) a helix self-contact, (2) a long-range strand–strand contact, (3) a medium-range strand–strand contact, (4) a non-contact and (5) a very long-range strand–strand contact. Each pair is shown as two red dots on the diagrams. Darker colours indicate a higher attribution weight.

Extended Data Fig. 10 Attribution shown on predicted structure.

For T0986s2 (TM score 0.8), the top 10 input pairs, including self-pairs, with the highest attribution weight for each of the five output pairs shown in Extended Data Fig. 9, are shown as lines (or spheres for self-pairs) coloured by sensitivity, lighter green colours indicate more sensitive, and the output pair is shown as a blue line.

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This PDF file contains nine equations (for potentials, Distogram lDDT and Integrated gradients) referenced by the Methods section of the paper.

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Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020). https://doi.org/10.1038/s41586-019-1923-7

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