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

Tel Aviv University, University of Haifa

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

All Science Journal Classification (ASJC) codes

Structural Biology
Molecular Biology

Access to Document

10.1016/j.str.2022.07.002

Cite this

@article{f1c16e0f6d9b4d4ca1b1b8aa5ff1be56,

title = "Homologues not needed: Structure prediction from a protein language model",

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",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

year = "2022",

month = aug,

day = "4",

doi = "10.1016/j.str.2022.07.002",

language = "الإنجليزيّة",

volume = "30",

pages = "1047--1049",

journal = "Structure",

issn = "0969-2126",

publisher = "Cell Press",

number = "8",

}

TY - JOUR

T1 - Homologues not needed

T2 - Structure prediction from a protein language model

AU - Ben-Tal, Nir

AU - Kolodny, Rachel

PY - 2022/8/4

Y1 - 2022/8/4

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85135531492&partnerID=8YFLogxK

U2 - 10.1016/j.str.2022.07.002

DO - 10.1016/j.str.2022.07.002

M3 - تعليقَ / نقاش

C2 - 35931059

SN - 0969-2126

VL - 30

SP - 1047

EP - 1049

JO - Structure

JF - Structure

IS - 8

ER -

Homologues not needed: Structure prediction from a protein language model

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this