Machine learning reveals hidden dimensions of functional similarity in proteins
Abstract
Large language models trained on biological sequences, rather than natural language, are transforming biology, from predicting human genetic disease (1 , 2 ) to the design of new-to-nature proteins (3 -5).In this issue of PNAS, Cao et al. (6) extend these applications to detect the molecular underpinnings of phenotypic convergence by decoding patterns invisible to traditional sequence analysis approaches (Fig. 1 ).
Fields of Study & Subjects
Machine Learning in Bioinformatics
Bioinformatics and Genomic Networks
Protein Structure and Dynamics
Publication details
- Journal / source
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Volume
- 123
- Issue
- 1
- Pages
- e2524802122–e2524802122
- Publication date
- 2 January 2026
- Language
- EN