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^*

^*Corresponding author for this work

Biochemistry Molecular Biology

University of Haifa

Research output: Contribution to journal › Comment/debate

1 Scopus citations

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

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Homologues not needed: Structure prediction from a protein language model

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