Protein structure prediction and potential energy landscape analysis using continuous global minimization

Ken A. Dill; Andrew T. Phillips; J. Ben Rosen

doi:10.1145/267521.267537

Protein structure prediction and potential energy landscape analysis using continuous global minimization

Ken A. Dill
, Andrew T. Phillips
, J. Ben Rosen

Laufer Center for Physical and Quantitative Biology

University of California at San Francisco

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

It is shown how one can apply more efficient continuous minimization techniques to the energy minimization problem by using an accurate continuous approximation to the discrete information provided for known protein structures. The results of one particular computational method for protein structure prediction (the CGU algorithm) is shown, which is based on the continuous minimization technique. The method can be used both to accurately determine the global minimum of potential energy function and also to offer a quantitative analysis of all of the local minima on the energy landscape.

Original language	English
Pages	109-117
Number of pages	9
DOIs	https://doi.org/10.1145/267521.267537
State	Published - 1997
Event	Proceedings of the 1997 1st Annual International Conference on Computational Molecular Biology, RECOMB - Santa Fe, NM, USA Duration: Jan 20 1997 → Jan 23 1997

Conference

Conference	Proceedings of the 1997 1st Annual International Conference on Computational Molecular Biology, RECOMB
City	Santa Fe, NM, USA
Period	01/20/97 → 01/23/97

Access to Document

10.1145/267521.267537

Cite this

@conference{897ff29853a048d68275eeb781de31a5,

title = "Protein structure prediction and potential energy landscape analysis using continuous global minimization",

abstract = "It is shown how one can apply more efficient continuous minimization techniques to the energy minimization problem by using an accurate continuous approximation to the discrete information provided for known protein structures. The results of one particular computational method for protein structure prediction (the CGU algorithm) is shown, which is based on the continuous minimization technique. The method can be used both to accurately determine the global minimum of potential energy function and also to offer a quantitative analysis of all of the local minima on the energy landscape.",

author = "Dill, \{Ken A.\} and Phillips, \{Andrew T.\} and Rosen, \{J. Ben\}",

year = "1997",

doi = "10.1145/267521.267537",

language = "English",

pages = "109--117",

note = "Proceedings of the 1997 1st Annual International Conference on Computational Molecular Biology, RECOMB ; Conference date: 20-01-1997 Through 23-01-1997",

}

Protein structure prediction and potential energy landscape analysis using continuous global minimization. / Dill, Ken A.; Phillips, Andrew T.; Rosen, J. Ben.
1997. 109-117 Paper presented at Proceedings of the 1997 1st Annual International Conference on Computational Molecular Biology, RECOMB, Santa Fe, NM, USA.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Protein structure prediction and potential energy landscape analysis using continuous global minimization

AU - Dill, Ken A.

AU - Phillips, Andrew T.

AU - Rosen, J. Ben

PY - 1997

Y1 - 1997

N2 - It is shown how one can apply more efficient continuous minimization techniques to the energy minimization problem by using an accurate continuous approximation to the discrete information provided for known protein structures. The results of one particular computational method for protein structure prediction (the CGU algorithm) is shown, which is based on the continuous minimization technique. The method can be used both to accurately determine the global minimum of potential energy function and also to offer a quantitative analysis of all of the local minima on the energy landscape.

AB - It is shown how one can apply more efficient continuous minimization techniques to the energy minimization problem by using an accurate continuous approximation to the discrete information provided for known protein structures. The results of one particular computational method for protein structure prediction (the CGU algorithm) is shown, which is based on the continuous minimization technique. The method can be used both to accurately determine the global minimum of potential energy function and also to offer a quantitative analysis of all of the local minima on the energy landscape.

UR - https://www.scopus.com/pages/publications/0030679996

U2 - 10.1145/267521.267537

DO - 10.1145/267521.267537

M3 - Paper

AN - SCOPUS:0030679996

SP - 109

EP - 117

T2 - Proceedings of the 1997 1st Annual International Conference on Computational Molecular Biology, RECOMB

Y2 - 20 January 1997 through 23 January 1997

ER -

Protein structure prediction and potential energy landscape analysis using continuous global minimization

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