Generalized pattern search algorithm for peptide structure prediction

Giuseppe Nicosia, Giovanni Stracquadanio*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα.

Original languageEnglish
Pages (from-to)4988-4999
Number of pages12
JournalBiophysical Journal
Volume95
Issue number10
DOIs
Publication statusPublished - 15 Nov 2008

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