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Landscape approaches for determining the ensemble of folding transition states: Success and failure hinge on the degree of frustration
Author(s) -
Hugh Nymeyer,
Nicholas D. Socci,
José N. Onuchic
Publication year - 2000
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.97.2.634
Subject(s) - frustration , statistical physics , protein folding , folding (dsp implementation) , hinge , stability (learning theory) , physics , mathematics , chemistry , computer science , classical mechanics , condensed matter physics , nuclear magnetic resonance , machine learning , electrical engineering , engineering
We present a method for determining structural properties of the ensemble of folding transition states from protein simulations. This method relies on thermodynamic quantities (free energies as a function of global reaction coordinates, such as the percentage of native contacts) and not on “kinetic” measurements (rates, transmission coefficients, complete trajectories); consequently, it requires fewer computational resources compared with other approaches, making it more suited to large and complex models. We explain the theoretical framework that underlies this method and use it to clarify the connection between the experimentally determined Φ value, a quantity determined by the ratio of rate and stability changes due to point mutations, and the average structure of the transition state ensemble. To determine the accuracy of this thermodynamic approach, we apply it to minimalist protein models and compare these results with the ones obtained by using the standard experimental procedure for determining Φ values. We show that the accuracy of both methods depends sensitively on the amount of frustration. In particular, the results are similar when applied to models with minimal amounts of frustration, characteristic of rapid-folding, single-domain globular proteins.

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