EdtClust: A fast homologous protein sequences clustering method based on edit distance

Homology among protein sequences plays a crucial role in evolutionary and comparative biology, with a key step involving retrieving homologous sequences of a query or clustering homologous protein sequences within a dataset. However, the growing size of known protein sequences due to advances in sequencing technology has made homologous protein sequence clustering an increasingly challenging task. Current feasible solutions employ locality-sensitive hashing to achieve linear time complexity for sequence clustering. Nonetheless, existing methods do not directly apply this hashing technique based on the foundational concept of edit distance, which precisely defines homology. To address this gap, this paper introduces EdtClust, which aims to design a specific instance of locality-sensitive hashing for edit distance, enhancing clustering efficiency while maintaining a foundation in homology’s essence. EdtClust comprises two core steps: first, utilizing the gapped k-mer embedding framework, it transforms the metric space derived from protein sequences and edit distances into a novel metric space containing gapped k-mer sets and L
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distances. Subsequently, a locality-sensitive hashing technique based on the Cauchy distribution is implemented to expedite clustering in this new metric space. Through rigorous theoretical analysis, EdtClust demonstrates enhanced precision and efficiency, while also ensuring a simple and swift implementation. Experimental results using real protein sequences validate that EdtClust can identify more homologous sequence pairs in a shorter time with reduced memory consumption compared to state-of-the-art algorithms like Linclust and ALFATClust, thereby confirming its substantial potential in the field of computational biology.