PROVAL: A framework for comparison of protein sequence embeddings

Abstract

High throughput sequencing technology leads to a significant increase in the number of generated protein sequences and the anchor database UniProt doubles approximately every two years. This large set of annotated data is used by many bioinformatics algorithms. Searching within these databases, typically without using any annotations, is challenging due to the variable lengths of the entries and the used non-standard comparison measures. A promising strategy to address these issues is to find fixed-length, information-preserving representations of the variable length protein sequences. A systematic algorithmic evaluation of the proposals is however surprisingly missing. In this work, we analyze how different algorithms perform in generating general protein sequence representations and provide a thorough evaluation framework PROVAL. The strategies range from a proximity representation using classical Smith–Waterman algorithm to state-of-the-art embedding techniques by means of transformer networks. The methods are evaluated by, e.g., the molecular function classification, embedding space visualization, computational complexity and the carbon footprint.