Detecting remote homology with speed and sensitivity is crucial for tasks like function annotation and structure prediction. We introduce a novel approach using contrastive learning to convert protein language model embeddings into a new 20-letter alphabet, The Embedded Alphabet (TEA), enabling highly efficient large-scale protein homology searches.

Searching with our alphabet performs on par with and complements structure-based methods without requiring any structural information, and with the speed of sequence search. This provides a significant advantage for proteins that lack structural data and enables searches at the scale of available sequence data numbering over a billion. The inbuilt entropy metric provides a measure of confidence offering a valuable estimate of prediction reliability for downstream analysis. This work has broad implications, significantly accelerating and broadening the scope of protein sequence analysis. The ability to detect remote homologs more effectively enhances functional annotation transfer and drug discovery efforts. We also provide a user-friendly web-server at https://pickybinders.org/tea enabling conversion of amino acid sequences to TEA sequences and searches on the UniRef50 and Foldseek AlphaFold Clusters databases.

Ultimately, we bring the exciting advances in protein language model representation learning to the plethora of sequence bioinformatics algorithms developed over the past century, offering a powerful new tool for biological discovery.

Co-authors: Gabriel Studer, Lorenzo Pantolini, Laura Engist, Florian Pommerening, Ieva Pudžiuvelytė, Andrew M. Waterhouse, Stefan Bienert, Gerardo Tauriello, Martin Steinegger, Torsten Schwede