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Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling
Wang, C; Wei, Y; Zhang, HC; Kong, LP; Sun, SW; Zheng, WM; Bu, DB
2019
Conference Name17th Asia Pacific Bioinformatics Conference (APBC)
Source PublicationBMC BIOINFORMATICS
Volume20
Pages135
Conference DateJAN 14-16, 2019
Conference PlaceWuhan, PEOPLES R CHINA
Publication PlaceLONDON
ISSN1471-2105
PublisherBMC
AbstractBackgroundThe ab initio approaches to protein structure prediction usually employ the Monte Carlo technique to search the structural conformation that has the lowest energy. However, the widely-used energy functions are usually ineffective for conformation search. How to construct an effective energy function remains a challenging task.ResultsHere, we present a framework to construct effective energy functions for protein structure prediction. Unlike existing energy functions only requiring the native structure to be the lowest one, we attempt to maximize the attraction-basin where the native structure lies in the energy landscape. The underlying rationale is that each energy function determines a specific energy landscape together with a native attraction-basin, and the larger the attraction-basin is, the more likely for the Monte Carlo search procedure to find the native structure. Following this rationale, we constructed effective energy functions as follows: i) To explore the native attraction-basin determined by a certain energy function, we performed reverse Monte Carlo sampling starting from the native structure, identifying the structural conformations on the edge of attraction-basin. ii) To broaden the native attraction-basin, we smoothened the edge points of attraction-basin through tuning weights of energy terms, thus acquiring an improved energy function. Our framework alternates the broadening attraction-basin and reverse sampling steps (thus called BARS) until the native attraction-basin is sufficiently large. We present extensive experimental results to show that using the BARS framework, the constructed energy functions could greatly facilitate protein structure prediction in improving the quality of predicted structures and speeding up conformation search.ConclusionUsing the BARS framework, we constructed effective energy functions for protein structure prediction, which could improve the quality of predicted structures and speed up conformation search as well.
KeywordTERTIARY STRUCTURES FRAGMENTS
DOI10.1186/s12859-019-2652-5
URL查看原文
Indexed ByCPCI
Language英语
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
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Document Type会议论文
Identifierhttp://ir.itp.ac.cn/handle/311006/23548
CollectionSCI会议论文
Affiliation1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, 19-1 Yuquan Rd, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Theoret Phys, 55 Zhongguancun East Rd, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, C,Wei, Y,Zhang, HC,et al. Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling[C]. LONDON:BMC,2019:135.
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