title: Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity.
abstract: Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence.
authors.affiliation: Equipe de Bioinformatique Génomique et Moléculaire, INSERM U726, Université Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France. camproux@ebgm.jussieu.fr
authors.forename: A C
authors.initials: AC
authors.lastname: Camproux
authors.forename: P
authors.initials: P
authors.lastname: Tufféry
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D006801:Humans; D008390:Markov Chains; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins