A successive sub-grouping method for multiple sequence alignments analysis

TL;DR

This paper introduces a deterministic, property-based sub-grouping method for multiple sequence alignment that clusters amino acids based on chemical or structural properties, offering an alternative to substitution matrix approaches.

Contribution

It presents a novel successive sub-grouping approach for amino acids in sequence alignment, using user-defined or default property schemes, implemented in Python and tested on biological cases.

Findings

Method provides a composite score based on amino acid similarity at each position.

Different property schemes can be used for amino acid clustering.

The approach has been tested and benchmarked on biological data.

Abstract

A novel approach to protein multiple sequence alignment is discussed: substantially this method counterparts with substitution matrix based methods (like Blosum or PAM based methods), and implies a more deterministic approach to chemical/physical sub-grouping of amino acids . Amino acids (aa) are divided into sub-groups with successive derivations, that result in a clustering based on the considered property. The properties can be user defined or chosen between default schemes, like those used in the analysis described here. Starting from an initial set of the 20 naturally occurring amino acids, they are successively divided on the basis of their polarity/hydrophobic index, with increasing resolution up to four level of subdivision. Other schemes of subdivision are possible: in this thesis work it was employed also a scheme based on physical/structural properties (solvent exposure, lateral chain mobility and secondary structure tendency), that have been compared to the chemical scheme with testing purposes. In the method described in this chapter, the total score for each position in the alignment accounts for different degree of similarity between amino acids. The scoring value result form the contribution of each level of selectivity for every individual property considered. Simply the method (called M_Al) analyse the n sequence alignment position per position and assigns a score which have contributes by aa identity plus a composed valuation of the chemical or of the structural affinity between the n aligned amino acids. This method has been implemented in a series of programs written in python language; these programs have been tested in some biological cases, with benchmark purposes.