[6/8] 🎯 Beyond PPIs: ProteomeLM predicts gene essentiality across diverse taxa (e.g. E. coli, yeast, minimal cells), highlighting its potential for broad downstream applications.

[5/8] ⚡ This allows unsupervised and supervised PPI prediction at proteome scale in minutes, several orders of magnitude faster than coevolution-based methods such as DCA.
Try it here: github.com/Bitbol-Lab/P...

[4/8] 🎯 Key finding: Attention heads spontaneously encode protein–protein interaction networks. Some heads can reach an AUC of 0.92 in discriminating interacting vs non-interacting pairs.

[2/8] 🧬 Training objective: ProteomeLM uses a custom masked language modeling task, predicting masked ESM-C representations of proteins within the proteome.