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Computational Methods for Protein Fold Prediction: an Ab-initio Topological Approach

  • G. Ceci
  • A. Mucherino
  • M. D’Apuzzo
  • D. Di Serafino
  • S. Costantini
  • A. Facchiano
  • G. Colonna
Part of the Springer Optimization and Its Applications book series (SOIA, volume 7)

Abstract

The prediction of protein native conformations is still a big challenge in science, although a strong research activity has been carried out on this topic in the last decades. In this chapter we focus on ab-initio computational methods for protein fold predictions that do not rely heavily on comparisons with known protein structures and hence appear to be the most promising methods for determining conformations not yet been observed experimentally. To identify main trends in the research concerning protein fold predictions, we briefly review several ab-initio methods, including a recent topological approach that models the protein conformation as a tube having maximum thickness without any self-contacts. This representation leads to a constrained global optimization problem. We introduce a modification in the tube model to increase the compactness of the computed conformations, and present results of computational experiments devoted to simulating α-helices and all-α proteins. A Metropolis Monte Carlo Simulated Annealing algorithm is used to search the protein conformational space.

Key words

Protein fold prediction Ab-initio methods Native state topology Tube thickness Global optimization Simulated annealing 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • G. Ceci
    • 1
    • 5
  • A. Mucherino
    • 1
    • 5
  • M. D’Apuzzo
    • 1
    • 4
    • 5
  • D. Di Serafino
    • 1
    • 4
    • 5
  • S. Costantini
    • 2
    • 3
    • 5
  • A. Facchiano
    • 3
    • 4
    • 5
  • G. Colonna
    • 2
    • 4
    • 5
  1. 1.Department of MathematicsSecond University of NaplesCasertaItaly
  2. 2.Department of Biochemistry and BiophysicsSecond University of NaplesNaplesItaly
  3. 3.Institute of Food ScienceCNRAvellinoItaly
  4. 4.Research Center of Computational and Biotechnological Sciences (CRISCEB)Second University of NaplesNaplesItaly
  5. 5.Computational Biology DoctorateSecond University of NaplesItaly

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