Novel machine learning approaches revolutionize protein knowledge.

Proteins / chemistry Machine Learning Computational Biology / methods Protein Conformation
AI AlphaFold2 embeddings machine learning pLM protein structure prediction structure alignment

Journal

Trends in biochemical sciences
ISSN: 0968-0004
Titre abrégé: Trends Biochem Sci
Pays: England
ID NLM: 7610674

Informations de publication

Date de publication:
04 2023
Historique:
received: 14 07 2022
revised: 24 10 2022
accepted: 17 11 2022
pubmed: 13 12 2022
medline: 22 3 2023
entrez: 12 12 2022
Statut: ppublish

Résumé

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.

Identifiants

DOI: 10.1016/j.tibs.2022.11.001 PMID: 36504138 PMC: PMC10570143
pubmed: 36504138
pii: S0968-0004(22)00308-5
doi: 10.1016/j.tibs.2022.11.001
pmc: PMC10570143
pii:
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

345-359

Subventions

Organisme : Wellcome Trust
ID : 221327/Z/20/Z
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/T002735/1
Pays : United Kingdom

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests No interests are declared.

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Auteurs

Nicola Bordin (N)

Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK.

Christian Dallago (C)

Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; VantAI, 151 W 42nd Street, New York, NY 10036, USA.

Michael Heinzinger (M)

Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany.

Stephanie Kim (S)

School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.

Maria Littmann (M)

Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.

Clemens Rauer (C)

Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK.

Martin Steinegger (M)

School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.

Burkhard Rost (B)

Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany; TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany.

Christine Orengo (C)

Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK. Electronic address: c.orengo@ucl.ac.uk.

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questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Novel machine learning approaches...
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questionsmedicales.fr › Phénomènes chimiques › Phénomènes biochimiques › Conformation moléculaire › Conformation des protéines › Novel machine learning approaches...