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

Abstract : 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.
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Contributor : Alexandre G. de Brevern Connect in order to contact the contributor
Submitted on : Tuesday, February 27, 2007 - 11:01:25 AM
Last modification on : Wednesday, October 27, 2021 - 2:40:02 PM
Long-term archiving on: : Friday, November 25, 2016 - 2:25:28 PM


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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⟩



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