Title: Predicting three-dimensional structure of protein fragments from dihedral angle propensities and molecular dynamics

Authors: Jaine K. Blayney, Piyush C. Ojha, Mary Shapcott

Addresses: Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster, Newtownabbey, Northern Ireland, BT37 0QB, UK. ' Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster, Newtownabbey, Northern Ireland, BT37 0QB, UK. ' Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster, Newtownabbey, Northern Ireland, BT37 0QB, UK

Abstract: Incorporating the existing knowledge of protein structural preferences, e.g., amino acid angle frequencies, in structure prediction have proven to be less successful with smaller peptides. In this work, we compare the effectiveness of backbone angle propensity libraries derived from two protein data sets: one consisting of proteins of unrestricted lengths; the second containing proteins ranging in size from 40 to 75 residues. Model structures for 29 target peptides are predicted using a threading algorithm and their stability evaluated using in vacuo molecular dynamics simulations. Structures derived from the data set consisting of smaller proteins outperformed those developed from that unrestricted by protein length.

Keywords: tertiary structure prediction; proteins; amino acid propensities; protein fragments; dihedral angle propensities; molecular dynamics; protein structural preferences; peptides; simulation.

DOI: 10.1504/IJCBDD.2010.035240

International Journal of Computational Biology and Drug Design, 2010 Vol.3 No.2, pp.146 - 163

Available online: 16 Sep 2010 *

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