Homologues not needed: Structure prediction from a protein language model

Nir Ben-Tal, Rachel Kolodny

Research output: Contribution to journalComment/Debate


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 languageEnglish
Pages (from-to)1047-1049
Number of pages3
Issue number8
StatePublished - 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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