[8/8] 💻 Resources:
• Training dataset
• 4 pre-trained models (XS → L)
• Code & interactive notebooks
🔗 huggingface.co/collections/...
🔗 github.com/Bitbol-Lab/P...

[7/8] 📊 In conclusion, results show strong performances across species and benchmarks for both PPI prediction and gene essentiality. ProteomeLM makes proteome-wide analysis more practical, easing large-scale studies, including in complex eukaryotic proteomes.

[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.

[3/8] 🧬 Encoding strategy: Instead of positional encoding, ProteomeLM introduces a functional encoding based on orthologous groups. Thus the model can leverage functional encoding and other proteins. This is especially important in eukaryotes, where gene order is less conserved.

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