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An approach to detect the dominant folds of proteinlike heteropolymers from the statistics of a homopolymeric chain

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Optimization in Computational Chemistry and Molecular Biology

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 40))

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Abstract

Statistical optimization of proteins is interpreted from the standpoint of optimal memory storage in neural networks. This approach results in the concept of an intrinsic learning rule which occurs in place of the usual learning rule in neural networks and incorporates geometric, topological and statistical constraints which make one folded shape kinetically more accessible than another. As a first step to extract this learning rule from the behavior of model chains, we approximate the free energy of proteinlike heteropolymers by an expansion about the free energy of an “equivalent homopolymer”, the coefficients of which determine a potential for heteropolymer sequences. Expansion coefficients are computed for a simple bead chain homopolymer model and the results are compared to a hydrophobic and polar (HP) model of proteins for which the optimal folds are already known.

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Author information

Authors and Affiliations

  1. Center for Advanced Computational Science and Engineering, San Diego Supercomputer Center 0505, University of California at San Diego, La Jolla, CA, 92093, USA

    Erik D. Nelson

  2. School of Chemical Sciences, University of Illinois, Urbana, Illinois, 61801, USA

    Peter G. Wolynes

  3. Department of Physics, University of California at San Diego, La Jolla, CA, 92093, USA

    Jose’ N. Onuchic

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  1. Erik D. Nelson
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  2. Peter G. Wolynes
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  3. Jose’ N. Onuchic
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Editors and Affiliations

  1. Princeton University, USA

    C. A. Floudas

  2. University of Florida, USA

    P. M. Pardalos

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Nelson, E.D., Wolynes, P.G., Onuchic, J.N. (2000). An approach to detect the dominant folds of proteinlike heteropolymers from the statistics of a homopolymeric chain. In: Floudas, C.A., Pardalos, P.M. (eds) Optimization in Computational Chemistry and Molecular Biology. Nonconvex Optimization and Its Applications, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3218-4_7

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  • DOI: https://doi.org/10.1007/978-1-4757-3218-4_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4826-7

  • Online ISBN: 978-1-4757-3218-4

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