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Richard A. Friesner, Ilya Prigogine, Stuart A. Rice's Advances in Chemical Physics, Computational Methods for PDF

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

ISBN-10: 0471209554

ISBN-13: 9780471209553

ISBN-10: 0471465232

ISBN-13: 9780471465232

Because the first makes an attempt to version proteins on a working laptop or computer all started nearly thirty years in the past, our figuring out of protein constitution and dynamics has dramatically elevated. Spectroscopic dimension innovations proceed to enhance in solution and sensitivity, permitting a wealth of data to be bought with reference to the kinetics of protein folding and unfolding, and complementing the designated structural photo of the folded kingdom. at the same time, algorithms, software program, and computational have stepped forward to the purpose the place either structural and kinetic difficulties will be studied with a good measure of realism. regardless of those advances, many significant demanding situations stay in realizing protein folding at either the conceptual and functional degrees. Computational tools for Protein Folding seeks to light up fresh advances in computational modeling of protein folding in a manner that would be valuable to physicists, chemists, and chemical physicists. protecting a huge spectrum of computational equipment and practices culled from various examine fields, the editors current an entire diversity of versions that, jointly, offer a radical and present description of all points of protein folding. A helpful source for either scholars and pros within the box, the booklet should be of price either as a state of the art review of present details and as a catalyst for uplifting new experiences. Computational tools for Protein Folding is the a hundred and twentieth quantity within the acclaimed sequence Advances in Chemical Physics, a compilation of scholarly works devoted to the dissemination of up to date advances in chemical physics, edited by means of Nobel Prize-winner Ilya Prigogine.

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Extra resources for Advances in Chemical Physics, Computational Methods for Protein Folding (Volume 120)

Example text

Pair energies, 120–126 Threading protocols: comparison of potentials, 91–92 linear programming (LP) protocol, 101–102 protein recognition, 78–79 sequence-structure-function prediction, 134–138, 147 biochemical function prediction, 175–178 first-pass threading, 148–149 Fischer database applications, 149–151 genome-scale iterative threading, 152–153 iterative threading, 151–152 orientation-dependent pair potential, PROSPECTOR extension, 153 tertiary contacts, 153–155 tertiary protein structure, search space limitation, knowledge-based prediction, 210–211 local threading and fragment lists, 211–212 THOM2 model, HL test, 114–118 Three-descriptor models, protein folding kinetic statistical analysis, 22–24 Time-dependent probabilities, protein folding, potential energy surfaces, 399–400 Time-evolution of quantities, protein folding, coil-to-helix transition, 380–382 Topological frustration, protein folding mechanisms, 52–53 Torsional energy expressioin, oligopeptide structure prediction, potential energy models, 289–291 Torsion angle dynamics (TAD), sparse restraints, structural refinement, 338–359 algorithmic steps, 345–347 global optimization, 356–359 molecular dynamics, 341–345 vs.

1. Linear Correlations The first column of statistics given in Table I contains the Pearson linear correlation coefficients between the descriptor values (x) and log kf ðrx;log kf Þ. This is the statistical measure used by Plaxco et al. in their analysis of a subset of the descriptors considered here [12,14]. Consistent with their results, the two coefficients with the largest magnitudes are associated with the contact order (c and c=n). Several descriptors not examined by Plaxco et al. [12,14] exhibit jrx;log kf j > 0:5 as well: the a-helix content and propensity (h and ph ), total helix content (a), and b-sheet content (e).

INTRODUCTION Experimental and theoretical studies have led to the emergence of a unified general mechanism for protein folding that serves as a framework for the design and interpretation of research in this area [1]. This is not to suggest that the details of the folding process are the same for all proteins. Indeed, one of the most striking computational results is that a single model can yield qualitatively different behavior depending on the choice of parameters [1–3]. Consequently, it remains to determine the behavior of individual sequences under given environmental conditions and to identify the specific factors that lead to the manifestation of one folding scenario rather than another.

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Advances in Chemical Physics, Computational Methods for Protein Folding (Volume 120) by Richard A. Friesner, Ilya Prigogine, Stuart A. Rice


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