  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.
