Amino acid pair- and triplet-wise groupings in the interior of alpha-helical segments in proteins

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Miguel M. de Sousa (Autor) Outra; Cristian R. Munteanu (Autor) Outra; Alejandro Pazos (Autor) Outra; Nuno A. Fonseca (Autor) FCUP; Rui Camacho (Autor) FEUP; A. L. Magalhães (Autor) FCUP

Revista

Título: Journal of Theoretical Biology

Vol. 271 Nº 1

Páginas: 136-144

ISSN: 0022-5193

Editora: Elsevier

Indexação

ISI Web of Knowledge - 7 Citações

Scopus - 8 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Matemática

Outras Informações

ID Authenticus: P-002-V3C

DOI: 10.1016/j.jtbi.2010.11.028

Abstract (EN): A statistical approach has been applied to analyse primary structure patterns at inner positions of alpha-helices in proteins. A systematic survey was carried out in a recent sample of non-redundant proteins selected from the Protein Data Bank, which were used to analyse alpha-helix structures for amino acid pairing patterns. Only residues more than three positions apart from both termini of the alpha-helix were considered as inner. Amino acid pairings i, i+k(k = 1, 2, 3,4, 5), were analysed and the corresponding 20 x 20 matrices of relative global propensities were constructed. An analysis of (i, i+4, i+8) and (i, i+3, i+4) triplet patterns was also performed. These analysis yielded information on a series of amino acid patterns (pairings and triplets) showing either high or low preference for alpha-helical motifs and suggested a novel approach to protein alphabet reduction. In addition, it has been shown that the individual amino acid propensities are not enough to define the statistical distribution of these patterns. Global pair propensities also depend on the type of pattern, its composition and orientation in the protein sequence. The data presented should prove useful to obtain and refine useful predictive rules which can further the development and fine-tuning of protein structure prediction algorithms and tools. (C) 2010 Elsevier Ltd. All rights reserved.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9