'Hybrid protein model' for optimally defining 3D protein structure fragments.
BACKGROUND
Our aim is to develop a process that automatically defines a repertory of contiguous 3D protein structure fragments and can be used in homology modeling. We present here improvements to the method we introduced previously: the 'hybrid protein model' (de Brevern and Hazout, THEOR: Chem. Acc., 106, 36-47, (2001)) The hybrid protein learns a non-redundant databank encoded in a structural alphabet composed of 16 Protein Blocks (PBs; de Brevern et al., Proteins, 41, 271-287, (2000)). Every local fold is learned by looking for the most similar pattern present in the hybrid protein and modifying it slightly. Finally each position corresponds to a cluster of similar 3D local folds.


RESULTS
In this paper, we describe improvements to our method for building an optimal hybrid protein: (i) 'baby training,' which is defined as the introduction of large structure fragments and the progressive reduction in the size of training fragments; and (ii) the deletion of the redundant parts of the hybrid protein. This repertory of contiguous 3D protein structure fragments should be a useful tool for molecular modeling.
de Brevern
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AG
Equipe de Bioinformatique Génomique et Moléculaire, INSERM U436, Université Paris 7, case 7113, 2, place Jussieu, 75251 Paris cedex 05, France. debrevern@urbb.jussieu.fr
Hazout
S
S
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D030562:Databases, Protein; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010446:Peptide Fragments / Q000737:chemistry; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D011786:Quality Control
D010446:Peptide Fragments; D011506:Proteins