'Hybrid protein model' for optimally defining 3D protein structure fragments.

Alexandre de Brevern (1) , Serge A. Hazout (1)

1 Modélisation mathématique et statistique en biologie et médecine

MOTIVATION: 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.

Domaines: Biochimie, Biologie Moléculaire; Bio-Informatique, Biologie Systémique [q-bio.QM]

Alexandre de Brevern, Serge A. Hazout. 'Hybrid protein model' for optimally defining 3D protein structure fragments.. Bioinformatics, 2003, 19 (3), pp.345-53. ⟨10.1093/bioinformatics/btf859⟩. ⟨inserm-00133632⟩