Homologues not needed: Structure prediction from a protein language model

Rachel Kolodny

doi:10.1016/j.str.2022.07.002

Homologues not needed: Structure prediction from a protein language model

Rachel Kolodny

Department of Computer Science

Research output: Contribution to journal › Comment/Debate

Abstract

Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a protein structure from its amino acid sequence only. This approach should enable the analysis of mutation effects.

Original language	English
Pages (from-to)	1047-1049
Number of pages	3
Journal	Structure
Volume	30
Issue number	8
DOIs	https://doi.org/10.1016/j.str.2022.07.002
State	Published - 4 Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

ASJC Scopus subject areas

Structural Biology
Molecular Biology

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10.1016/j.str.2022.07.002

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abstract = "Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Wei{\ss}enow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a protein structure from its amino acid sequence only. This approach should enable the analysis of mutation effects.",

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AB - Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a protein structure from its amino acid sequence only. This approach should enable the analysis of mutation effects.

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M3 - Comment/Debate

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Homologues not needed: Structure prediction from a protein language model

Abstract

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