Volume 34, No 9, Sep 2024

ISSN: 1001-0602 
EISSN: 1748-7838 2018 
impact factor 17.848* 
(Clarivate Analytics, 2019)

Volume 34 Issue 9, September 2024: 597-598

RESEARCH HIGHLIGHTS

Synergizing sequence and structure representations to predict protein variants

Tong Chen¹ , Pranam Chatterjee^1,2,3,*

¹Department of Biomedical Engineering, Duke University, Durham, NC, USA
²Department of Computer Science, Duke University, Durham, NC, USA
³Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
Correspondence: Pranam Chatterjee(pranam.chatterjee@duke.edu)

In a new study published in Cell Research, Chinese researchers have trained ProMEP, a multimodal protein representation model that enables accurate, zero-shot prediction of mutation effects in proteins. Without utilizing multiple sequence alignments, the model uniquely integrates sequence and structural information to both model and predict mutational consequences and prioritize beneficial mutations to improve protein activity, motivating its usage for diverse protein engineering and biotechnology applications.

https://doi.org/10.1038/s41422-024-01010-6

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