Indexing Schemes for Similarity Search In Datasets of Short Protein Fragments

We propose a family of very efficient hierarchical indexing schemes for ungapped, score matrixbased 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, and datasets can have on the order of 60 million objects. Our scheme is based on the internal geometry of the aminoacid 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.