Highly accurate protein structure prediction with AlphaFold
Authors: Demis Hassabis
Ellen Clancy
Martin Steinegger
Alexander Pritzel
Augustin Žídek
Clemens Meyer
Stanislav Nikolov
Oriol Vinyals
David Silver
Sebastian Bodenstein
Andrew W Senior
Andrew J Ballard
Kathryn Tunyasuvunakool
Rishub Jain
Michael Figurnov
Koray Kavukcuoglu
Simon A A Kohl
Anna Potapenko
Olaf Ronneberger
Michalina Pacholska
Jonas Adler
Alex Bridgland
Richard Evans
Stig Petersen
Trevor Back
Pushmeet Kohli
Tim Green
John Jumper
Andrew Cowie
Michal Zielinski
David Reiman
Russ Bates
Tamas Berghammer
Bernardino Romera-Paredes
Journal: Nature (2021)
Discipline: Multidisciplinary
Keywords: Proteins; Structure; Function; Protein folding problem; Computational approaches
Affiliations: DeepMind, London, UK. ., DeepMind, London, UK., School of Biological Sciences, Seoul National University, Seoul, South Korea., Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.

Abstract:  Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1-4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'8-has been an important open research problem for more than 50 years9. Despite recent progress10-14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. 

Retracted: No

PubPeer comments:
 Readers should become aware of potential limitations of AlphaFold as described in this recent preprint AlphaFold2 fails to predict protein fold switching https: www.biorxiv.org content 10.1101 2022.03.08.483439v1 From the Abstract: AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model protein folds with atomic-level accuracy. Nevertheless, previous work has shown that these predictions tend to be inaccurate for structurally heterogeneous proteins. To systematically assess factors that contribute to this inaccuracy, we tested AlphaFold2 s performance on 98 fold-switching proteins, which assume at least two distinct-yet-stable secondary and tertiary structures. Topological similarities were quantified between five predicted and two experimentally determined structures of each fold-switching protein. Overall, 94% of AlphaFold2 predictions captured one experimentally determined conformation but not the other. Despite these biased results, AlphaFold2 s estimated confidences were moderate-to-high for 74% of fold-switching residues, a result that contrasts with overall low confidences for intrinsically disordered proteins, which are also structurally heterogeneous. To investigate factors contributing to this disparity, we quantified sequence variation within the multiple sequence alignments used to generate AlphaFold2 s predictions of fold-switching and intrinsically disordered proteins. Unlike intrinsically disordered regions, whose sequence alignments show low conservation, fold-switching regions had conservation rates statistically similar to canonical single-fold proteins. Furthermore, intrinsically disordered regions had systematically lower prediction confidences than either fold-switching or single-fold proteins, regardless of sequence conservation. **These results suggest that AlphaFold2 s high prediction confidences for fold switchers stem from the assumption that proteins assume one dominant fold. Our results emphasize the need to look at protein structure as an ensemble and suggest that systematic examination of fold-switching sequences may reveal propensities for multiple stable secondary and tertiary structures.** ,