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Advances in Physical Chemistry
Volume 2016 (2016), Article ID 3240674, 10 pages
Research Article

Energy Landscape of Pentapeptides in a Higher-Order Conformational Subspace

1York Centre for Complex Systems Analysis (YCCSA), University of York, York YO10 5GE, UK
2Department of Chemistry, College of Science, Qassim University, Buraydah 52571, Saudi Arabia

Received 8 February 2016; Accepted 4 April 2016

Academic Editor: Dennis Salahub

Copyright © 2016 Karim M. ElSawy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The potential energy landscape of pentapeptides was mapped in a collective coordinate principal conformational subspace derived from principal component analysis of a nonredundant representative set of protein structures from the PDB. Three pentapeptide sequences that are known to be distinct in terms of their secondary structure characteristics, (Ala)5, (Gly)5, and Val.Asn.Thr.Phe.Val, were considered. Partitioning the landscapes into different energy valleys allowed for calculation of the relative propensities of the peptide secondary structures in a statistical mechanical framework. The distribution of the observed conformations of pentapeptide data showed good correspondence to the topology of the energy landscape of the (Ala)5 sequence where, in accord with reported trends, the α-helix showed a predominant propensity at 298 K. The topography of the landscapes indicates that the stabilization of the α-helix in the (Ala)5 sequence is enthalpic in nature while entropic factors are important for stabilization of the β-sheet in the Val.Asn.Thr.Phe.Val sequence. The results indicate that local interactions within small pentapeptide segments can lead to conformational preference of one secondary structure over the other where account of conformational entropy is important in order to reveal such preference. The method, therefore, can provide critical structural information for ab initio protein folding methods.