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 |
|
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