Homologues not needed: Structure prediction from a protein language model

Nir Ben-Tal; Rachel Kolodny

doi:10.1016/j.str.2022.07.002

Homologues not needed: Structure prediction from a protein language model

Nir Ben-Tal, 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

Funding Information:
We acknowledge the support of grants 450/16 and 1764/21 of the Israeli Science Foundation (ISF). R.K is supported in part by the DSRC in the University of Haifa . N.B.-T.’s research is supported in part by the Abraham E. Kazan Chair in Structural Biology, Tel Aviv University .

Publisher Copyright:
© 2022 Elsevier Ltd

ASJC Scopus subject areas

Structural Biology
Molecular Biology

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10.1016/j.str.2022.07.002

Cite this

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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.",

author = "Nir Ben-Tal and Rachel Kolodny",

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AU - Kolodny, Rachel

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PY - 2022/8/4

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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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Homologues not needed: Structure prediction from a protein language model

Abstract

Bibliographical note

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