title: A reduced amino acid alphabet for understanding and designing protein adaptation to mutation.
abstract: Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Protein Blocks (PBs). This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analyzed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the alpha-helix Ncap). Some similar associations are found in other reduced AAAs, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.
authors.affiliation: Equipe de Bioinformatique Génomique et Moléculaire (EBGM), INSERM UMR-S 726, Université Denis DIDEROT, Paris 7, case 7113, 2, place Jussieu, 75251, Paris, France.
authors.forename: C
authors.initials: C
authors.lastname: Etchebest
authors.forename: C
authors.initials: C
authors.lastname: Benros
authors.forename: A
authors.initials: A
authors.lastname: Bornot
authors.forename: A-C
authors.initials: AC
authors.lastname: Camproux
authors.forename: A G
authors.initials: AG
authors.lastname: de Brevern
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D009154:Mutation / Q000502:physiology*; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*
chemical_list: D000596:Amino Acids; D011506:Proteins