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Novel machine learning approaches revolutionize protein knowledge

Bordin, Nicola; Dallago, Christian; Heinzinger, Michael; Kim, Stephanie; Littmann, Maria; Rauer, Clemens; Steinegger, Martin; ... Orengo, Christine; + view all (2022) Novel machine learning approaches revolutionize protein knowledge. Trends in Biochemical Sciences 10.1016/j.tibs.2022.11.001. (In press). Green open access

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Abstract

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 Appraisal Skills Program (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.

Type: Article
Title: Novel machine learning approaches revolutionize protein knowledge
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.tibs.2022.11.001
Publisher version: https://doi.org/10.1016/j.tibs.2022.11.001
Language: English
Additional information: Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: AI, AlphaFold2, embeddings, machine learning, pLM, protein structure prediction, structure alignment
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI: https://discovery.ucl.ac.uk/id/eprint/10162092
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