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231222s2003 xx ||||| 00| ||eng c |
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|a eng
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|a Das, B
|e verfasserin
|4 aut
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|a Performance of hybrid methods for large-scale unconstrained optimization as applied to models of proteins
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|c 2003
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|a Text
|b txt
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|a ohne Hilfsmittel zu benutzen
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|2 rdamedia
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|2 rdacarrier
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|a Date Completed 11.02.2004
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|a Date Revised 14.11.2007
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|a published: Print
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|a Citation Status MEDLINE
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|a Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 1222-1231, 2003
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|a Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large-scale unconstrained optimization that interlace iterations of the limited-memory BFGS method (L-BFGS) and the Hessian-free Newton method (Computat Opt Appl 2002, 21, 143-154). We test the performance of this approach as compared to those of the L-BFGS algorithm of Liu and Nocedal and the truncated Newton (TN) with automatic preconditioner of Nash, as applied to the protein bovine pancreatic trypsin inhibitor (BPTI) and a loop of the protein ribonuclease A. These systems are described by the all-atom AMBER force field with a dielectric constant epsilon = 1 and a distance-dependent dielectric function epsilon = 2r, where r is the distance between two atoms. It is shown that for the optimal parameters the hybrid approach is typically two times more efficient in terms of CPU time and function/gradient calculations than the two other methods. The advantage of the hybrid approach increases as the electrostatic interactions become stronger, that is, in going from epsilon = 2r to epsilon = 1, which leads to a more rugged and probably more nonlinear potential energy surface. However, no general rule that defines the optimal parameters has been found and their determination requires a relatively large number of trial-and-error calculations for each problem
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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|a Research Support, U.S. Gov't, P.H.S.
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|a Peptides
|2 NLM
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|a Proteins
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|a Aprotinin
|2 NLM
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|a 9087-70-1
|2 NLM
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|a Ribonuclease, Pancreatic
|2 NLM
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| 650 |
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|a EC 3.1.27.5
|2 NLM
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|a Meirovitch, H
|e verfasserin
|4 aut
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|a Navon, I M
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 24(2003), 10 vom: 30. Juli, Seite 1222-31
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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|g volume:24
|g year:2003
|g number:10
|g day:30
|g month:07
|g pages:1222-31
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