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By Richard A. Friesner, Ilya Prigogine, Stuart A. Rice

ISBN-10: 0471209554

ISBN-13: 9780471209553

Because the first makes an attempt to version proteins on a working laptop or computer started nearly thirty years in the past, our figuring out of protein constitution and dynamics has dramatically elevated. Spectroscopic size innovations proceed to enhance in answer and sensitivity, permitting a wealth of data to be acquired with reference to the kinetics of protein folding and unfolding, and complementing the certain structural photograph of the folded nation. simultaneously, algorithms, software program, and computational have improved to the purpose the place either structural and kinetic difficulties might be studied with a good measure of realism. regardless of those advances, many significant demanding situations stay in figuring out protein folding at either the conceptual and functional degrees.

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Extra info for Advances in Chemical Physics, Computational Methods for Protein Folding

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In the context of protein folding, lattice models were first introduced by G"o and co-workers [49]. The insights brought by Dill and Chan in the late 1980s have had a great influence on the development of LM for understanding proteinfolding kinetics [50]. Dill and co-workers argued that protein folding can be studied using short enough chains so that exact enumeration of all allowed conformations becomes possible. Exact enumeration enables precise computations of thermodynamic characteristics. Monte Carlo (MC) simulations, based on physically motivated move sets, can be used to monitor folding kinetics.

HOMOLOGOUS PROTEINS Information about the transition state of a protein can be obtained from protein engineering experiments in which one compares the effects of mutations on the folding rate to their effects on the overall stability (f values). Several proteins have been mutated extensively, and their kinetics have been measured. The fact that proteins with related structures but low sequence homologies are found to have similar transition states has been taken to support the relation between native structure and folding behavior; this is the case for the transition states of the src [57] and a-spectrin [58] SH3 domains, which have 36% sequence homology.

T. Manallack and D. J. Livingstone, Neural networks in drug discovery: Have they lived up to their promise? Eur. J. Med. Chem. 34, 195–208 (1999). 27. M. F. Møller, A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1993). 28. D. E. , Evolutionary Algorithms in Molecular Design, Wiley-VCH, Cambridge, 2000. 29. E. I. Shakhnovich and A. M. Gutin, Engineering of stable and fast-folding sequences of model proteins. Proc. Natl. Acad. Sci. USA 90, 7195–7199 (1993).

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Advances in Chemical Physics, Computational Methods for Protein Folding by Richard A. Friesner, Ilya Prigogine, Stuart A. Rice

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