GEMSCORE: A new empirical energy function for protein folding

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
DOIs
StatePublished - 1 Dec 2005
Event2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 - La Jolla, CA, United States
Duration: 14 Nov 200515 Nov 2005

Publication series

NameProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Volume2005

Conference

Conference2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
CountryUnited States
CityLa Jolla, CA
Period14/11/0515/11/05

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