AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

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Abstract

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 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 years 9 . Despite recent progress 10 – 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. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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UniProt: the universal protein knowledgebase in 2021

(2020)

Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

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Pfam: The protein families database in 2021

Jaina Mistry, Sara Rocío Chuguransky, Lowri Williams … (2020)

Abstract The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.

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Author and article information

Contributors

Mihaly Varadi:

ORCID: https://orcid.org/0000-0002-3687-0839

Stephen Anyango

Mandar Deshpande:

ORCID: https://orcid.org/0000-0002-9043-7665

Sreenath Nair

Cindy Natassia

Galabina Yordanova

David Yuan

Oana Stroe

Gemma Wood

Agata Laydon

Augustin Žídek

Tim Green

Kathryn Tunyasuvunakool

Stig Petersen

John Jumper

Ellen Clancy

Richard Green

Ankur Vora

Mira Lutfi

Michael Figurnov

Andrew Cowie

Nicole Hobbs

Pushmeet Kohli

Gerard Kleywegt

Ewan Birney:

ORCID: https://orcid.org/0000-0001-8314-8497

Demis Hassabis

Sameer Velankar:

ORCID: https://orcid.org/0000-0002-8439-5964

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (publisher-id): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date Collection: 07 January 2022

Publication date (Electronic): 17 November 2021

Publication date PMC-release: 17 November 2021

Volume: 50

Issue: D1

Pages: D439-D444

Affiliations

European Molecular Biology Laboratory, European Bioinformatics Institute , Hinxton, UK

DeepMind , London, UK

European Molecular Biology Laboratory, European Bioinformatics Institute , Hinxton, UK

DeepMind , London, UK

European Molecular Biology Laboratory, European Bioinformatics Institute , Hinxton, UK

Author notes

To whom correspondence should be addressed. Tel: +44 1223 49 4646; Fax: +44 1223 49 4468; Email: sameer@ 123456ebi.ac.uk

Correspondence may also be addressed to Demis Hassabis. Email: dhcontact@ 123456deepmind.com

Author information

Mihaly Varadi https://orcid.org/0000-0002-3687-0839

Mandar Deshpande https://orcid.org/0000-0002-9043-7665

Ewan Birney https://orcid.org/0000-0001-8314-8497

Sameer Velankar https://orcid.org/0000-0002-8439-5964

Article

Publisher ID: gkab1061

DOI: 10.1093/nar/gkab1061

PMC ID: 8728224

PubMed ID: 34791371

SO-VID: aedf36e1-0923-4052-9f78-b2df76409b7f

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date accepted : 19 October 2021

Date revision received : 14 October 2021

Date received : 07 September 2021

Page count

Pages: 6

Funding

Funded by: DeepMind, DOI 10.13039/100017149;

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

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Most cited references 20

Highly accurate protein structure prediction with AlphaFold

UniProt: the universal protein knowledgebase in 2021

Pfam: The protein families database in 2021

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