Population-based local search for protein folding simulation in the MJ energy model and cubic lattices

L. Kapsokalivas, X. Gan, A. A. Albrecht, K. Steinhoefel

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)


    We present experimental results on benchmark problems in 3D cubic lattice structures with the Miyazawa-Jernigan energy function for two local search procedures that utilise the pull-move set: (i) population-based local search (PLS) that traverses the energy landscape with greedy steps towards (potential) local minima followed by upward steps up to a certain level of the objective function; (ii) simulated annealing with a logarithmic cooling schedule (LSA). The parameter settings for PLS are derived from short LSA-runs executed in pre-processing and the procedure utilises tabu lists generated for each member of the population. In terms of the total number of energy function evaluations both methods perform equally well, however. PLS has the potential of being parallelised with an expected speed-up in the region of the population size. Furthermore, both methods require a significant smaller number of function evaluations when compared to Monte Carlo simulations with kink-jump moves. (C) 2009 Elsevier Ltd. All rights reserved.

    Original languageEnglish
    Pages (from-to)283-294
    Number of pages12
    JournalComputational Biology and Chemistry
    Issue number4
    Publication statusPublished - Aug 2009

    Bibliographical note

    Our new stochastic algorithm for protein folding simulations in the Miyazawa-Jernigan energy model outperforms Monte Carlo simulations with kink-jump moves. Moreover, the method is suitable for parallel implementations. I introduced the key pre-processing step, contributed to the design of computational experiments and co-supervised the data collection.

    ASJC Scopus subject areas

    • Biochemistry
    • Structural Biology
    • Organic Chemistry
    • Computational Mathematics

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