GEMSCORE: A new empirical energy function for protein folding

Yuan Chiu; Jenn Kang Hwang; Jinn-Moon Yang

doi:10.1109/CIBCB.2005.1594933

GEMSCORE: A new empirical energy function for protein folding

Yuan Chiu^*, Jenn Kang Hwang, Jinn-Moon Yang

^*Corresponding author for this work

Department of Biological Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

We have developed a new energy function, termed GEM SCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.

Original language	English
Title of host publication	Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
DOIs	https://doi.org/10.1109/CIBCB.2005.1594933
State	Published - 2005
Event	2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 - La Jolla, CA, United States Duration: 14 Nov 2005 → 15 Nov 2005

Publication series

Name	Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Volume	2005

Conference

Conference	2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Country/Territory	United States
City	La Jolla, CA
Period	14/11/05 → 15/11/05

Access to Document

10.1109/CIBCB.2005.1594933

Cite this

Chiu, Y., Hwang, J. K., & Yang, J.-M. (2005). GEMSCORE: A new empirical energy function for protein folding. In Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 Article 1594933 (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05; Vol. 2005). https://doi.org/10.1109/CIBCB.2005.1594933

Chiu, Yuan ; Hwang, Jenn Kang ; Yang, Jinn-Moon. / GEMSCORE : A new empirical energy function for protein folding. Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. 2005. (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05).

@inproceedings{e6ebdfdf4ad24c999f381dd505a13cd4,

title = "GEMSCORE: A new empirical energy function for protein folding",

abstract = "We have developed a new energy function, termed GEM SCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.",

author = "Yuan Chiu and Hwang, {Jenn Kang} and Jinn-Moon Yang",

year = "2005",

doi = "10.1109/CIBCB.2005.1594933",

language = "English",

isbn = "0780393872",

series = "Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05",

booktitle = "Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05",

note = "2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 ; Conference date: 14-11-2005 Through 15-11-2005",

}

Chiu, Y, Hwang, JK & Yang, J-M 2005, GEMSCORE: A new empirical energy function for protein folding. in Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05., 1594933, Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05, vol. 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05, La Jolla, CA, United States, 14/11/05. https://doi.org/10.1109/CIBCB.2005.1594933

GEMSCORE: A new empirical energy function for protein folding. / Chiu, Yuan; Hwang, Jenn Kang; Yang, Jinn-Moon.
Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. 2005. 1594933 (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - GEMSCORE

T2 - 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05

AU - Chiu, Yuan

AU - Hwang, Jenn Kang

AU - Yang, Jinn-Moon

PY - 2005

Y1 - 2005

N2 - We have developed a new energy function, termed GEM SCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.

AB - We have developed a new energy function, termed GEM SCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.

UR - http://www.scopus.com/inward/record.url?scp=33847228377&partnerID=8YFLogxK

U2 - 10.1109/CIBCB.2005.1594933

DO - 10.1109/CIBCB.2005.1594933

M3 - Conference contribution

AN - SCOPUS:33847228377

SN - 0780393872

SN - 9780780393875

T3 - Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05

BT - Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05

Y2 - 14 November 2005 through 15 November 2005

ER -

Chiu Y, Hwang JK, Yang JM. GEMSCORE: A new empirical energy function for protein folding. In Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. 2005. 1594933. (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05). doi: 10.1109/CIBCB.2005.1594933

GEMSCORE: A new empirical energy function for protein folding

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this