pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models

Motivation: The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task.Results: We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation. Availability and implementation:pLM-BLAST is accessible via the MPI Bioinformatics Toolkit as a web server for searching precomputed data-bases (https://toolkit.tuebingen.mpg.de/tools/plmblast). It is also available as a standalone tool for building custom databases and performingbatch searches (https://github.com/labstructbioinf/pLM-BLAST).

Automated summary

This study explores the feasibility of using protein language models and the pLM-BLAST tool to detect protein homology through sequence comparison. Benchmark tests show that pLM-BLAST maintains accuracy comparable to HHsearch while being faster, and it is able to compute local alignments, highlighting its potential for discovering previously undiscovered homologous relationships and improving protein annotation.