A Space-Efficient Approach towards Distantly Homologous Protein Similarity Searches

TL;DR

This paper introduces a space-efficient heuristic algorithm for protein similarity searches that balances speed, sensitivity, and low memory usage, especially effective for moderately sized databases and short queries.

Contribution

It presents a novel heuristic pair-wise sequence alignment method with constant space complexity, improving efficiency and sensitivity in distantly homologous protein searches.

Findings

Fast and space-efficient for moderate databases

Capable of detecting distantly related proteins

Produces high-quality alignments

Abstract

Protein similarity searches are a routine job for molecular biologists where a query sequence of amino acids needs to be compared and ranked against an ever-growing database of proteins. All available algorithms in this field can be grouped into two categories, either solving the problem using sequence alignment through dynamic programming, or, employing certain heuristic measures to perform an initial screening followed by applying an optimal sequence alignment algorithm to the closest matching candidates. While the first approach suffers from huge time and space demands, the latter approach might miss some protein sequences which are distantly related to the query sequence. In this paper, we propose a heuristic pair-wise sequence alignment algorithm that can be efficiently employed for protein database searches for moderately sized databases. The proposed algorithm is sufficiently fast to be applicable to database searches for short query sequences, has constant auxiliary space requirements, produces good alignments, and is sensitive enough to return even distantly related protein chains that might be of interest.