Indexing Schemes for Similarity Search In Datasets of Short Protein Fragments

TL;DR

This paper introduces efficient hierarchical indexing schemes for rapid similarity search in large datasets of short protein fragments, significantly reducing search time while maintaining accuracy.

Contribution

The authors develop a novel indexing method leveraging amino acid geometry, enabling fast, scalable similarity searches in datasets of up to 60 million protein fragments.

Findings

Achieves search of 100 nearest neighbors with less than 1% dataset scanning

Performs exceptionally well on datasets of 4-12 amino acid fragments

Offers a building block for complex algorithms and biological investigations

Abstract

We propose a family of very efficient hierarchical indexing schemes for ungapped, score matrix-based similarity search in large datasets of short (4-12 amino acid) protein fragments. This type of similarity search has importance in both providing a building block to more complex algorithms and for possible use in direct biological investigations where datasets are of the order of 60 million objects. Our scheme is based on the internal geometry of the amino acid alphabet and performs exceptionally well, for example outputting 100 nearest neighbours to any possible fragment of length 10 after scanning on average less than one per cent of the entire dataset.