Pattern recognition on random trees associated to protein functionality families

TL;DR

This paper introduces a novel metric-based method for analyzing protein sequences by representing them as trees, enabling effective clustering and classification of proteins into functional families without alignment.

Contribution

It proposes a new similarity measure on tree representations of protein sequences and demonstrates its effectiveness for both supervised and unsupervised protein family analysis.

Findings

High classification accuracy comparable to existing VLMC methods

Alignment-free clustering method for protein families

Potential for versatile clustering and classification applications

Abstract

In this paper, we address the problem of identifying protein functionality using the information contained in its aminoacid sequence. We propose a method to define sequence similarity relationships that can be used as input for classification and clustering via well known metric based statistical methods. In our examples, we specifically address two problems of supervised and unsupervised learning in structural genomics via simple metric based techniques on the space of trees 1)Unsupervised detection of functionality families via K means clustering in the space of trees, 2)Classification of new proteins into known families via k nearest neighbour trees. We found evidence that the similarity measure induced by our approach concentrates information for discrimination. Classification has the same high performance than others VLMC approaches. Clustering is a harder task, though, but our approach for clustering is alignment free and automatic, and may lead to many interesting variations by choosing other clustering or classification procedures that are based on pre-computed similarity information, as the ones that performs clustering using flow simulation, see (Yona et al 2000, Enright et al, 2003).