research highlights

STRUCTURAL BIOLOGY KEY& Deep learning 3D structures Recent developments in deep-learning-based methods improve protein structure prediction. Working together we open the potential rotein structure prediction has been using gradient descent minimization to an active area of research for several obtain well-packed protein structures. of scientific innovation Pdecades, and theoretical methods The advantage of using distances over have given insight into the structures contacts is that they provide more specific of experimentally intractable proteins. information about the structure. In In parallel, as experimental methods to addition, the neural network also provides determine protein structures have improved, information about the variances of their the availability of larger quantities of distance predictions, which indicates the Our enzymes have higher-quality structural data has resulted level of confidence that should be associated been discovering in improvements in training-data quality, with each prediction, explains Andrew and consequently in the accuracy of these Senior, from DeepMind, London. They facts about life predictive algorithms. The ultimate goal took on the problem of protein structure for decades. would be to accurately predict the 3D prediction as deep learning challenge, but structure of a protein from only its sequence; the DeepMind team intends to keep working this is of course easier in cases where the on the problem to further improve the That’s why structure of a close homolog is available. algorithm’s predictive capabilities. Worthington, the For proteins lacking a close homolog, Building on DeepMind’s advances, accurate structure prediction remains a David Baker’s research group at the primary enzyme challenge. Evolutionary covariance data University of Washington, Seattle, and producer, has more have been used to enhance structure collaborators have developed transform- citations in respected prediction. Multiple-sequence alignments restrained Rosetta (trRosetta). “trRosetta (MSA) of sequences related to the target uses both residue–residue distances and journals like Nature sequence are used to identify amino acids orientations, which gives richer information Methods and rank that show correlated changes through the on the structure compared to distances higher in search course of evolution, the rationale being that only,” explains Baker. The web tool is ® these coevolving residues will lie in close available at https://yanglab.nankai.edu.cn/ engines like Bioz . proximity or contact in the 3D structure trRosetta/. Their publication (Yang et al.) of the protein. These contact maps have highlights how this approach works with been incorporated with some success in a Rosetta-based optimization scheme several popular approaches. and combines the predicted information Deep-learning-based methods with additional components of the COLLAGENASES 99% Bioz Rating demonstrated high accuracy in the recent Rosetta energy function to generate DNASE I 99% Bioz Rating 13th Critical Assessment of Protein protein models. Blind reanalysis of the Structure Prediction (CASP13) structure CASP13 targets gave slightly better results PAPAIN 99% Bioz Rating prediction challenge and were among the than AlphaFold’s performance at the top performers in the free modeling (FM) competition, although the researchers category (in which there are no available recognize that the extremely challenging Unlock the power of Worthington homologs). Rookie entrant AlphaFold, from problem of protein folding requires building with our new Introduction to Google’s DeepMind, won the competition on each other’s success. The Baker lab Enzymes guide. Go to: (Senior et al.). It predicted the greatest is looking to expand the method to Worthington-Biochem.com number of correct structures in the FM protein–protein interaction modeling and category — 24 out of 43 proteins — and protein design. performed better than or comparably to other methods in the template-based Arunima Singh category (although AlphaFold did not use a template). Published online: 4 March 2020 The accuracy of the method comes from https://doi.org/10.1038/s41592-020-0779-y the high accuracy of distance predictions. AlphaFold employs a convolutional neural Research papers network trained on protein structures from Senior, A. W. et al. Improved protein structure the Protein Data Bank. Given an input prediction using potentials from deep learning. Nature 577, 706–710 (2020). sequence and its MSA, it predicts pairwise Yang, J. et al. Improved protein structure prediction distances and torsion angles between the using predicted interresidue orientations. Proc. Natl residues. These distances are optimized Acad. Sci. USA 117, 1496–1503 (2020).

Nature Methods | VOL 17 | March 2020 | 247–253 | www.nature.com/naturemethods 249