The transformation of protein 1D-sequence to protein 3D-structure is one of the main difficulties of structural biology. A structural alphabet has been previously defined from dihedral angles describing the protein backbone as structural information by using an unsupervised classifier. The 16 protein blocks (PBs), basis element of the structural alphabet, allows a correct 3D structure approximation. Local prediction had been estimated by a Bayesian approach and shown that sequence information induces strongly the local fold, but stays coarse (prediction rate of 40.7% with one PB, 75,8% with the four most probable PBs). The Hybrid Protein Model presented in this study learns both the sequence and structure of the proteins. The analysis made along the hybrid protein has permitted to appreciate more precisely the spatial location of some types of amino acid residues in the secondary structures and their flanking regions. This study leads to a fuzzy model of dependence between sequence and structure.
['A. G. de Brevern', 'S. Hazout']
Hybrid Protein Model (HPM): a method to compact protein 3D-structure information and physicochemical properties