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    Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

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    Type
    Article
    Authors
    Najibi, Seyed Morteza
    Maadooliat, Mehdi
    Zhou, Lan
    Huang, Jianhua Z.
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    URF/1/1976-04
    Date
    2017-02-08
    Online Publication Date
    2017-02-08
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/622856
    
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    Abstract
    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.
    Citation
    Najibi SM, Maadooliat M, Zhou L, Huang JZ, Gao X (2017) Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions. Computational and Structural Biotechnology Journal. Available: http://dx.doi.org/10.1016/j.csbj.2017.01.011.
    Sponsors
    We are grateful to Professor Roland L. Dunbrack for providing the data set for the neighbor-dependent Ramachandran distribution application, and to Amelie Stein for help with the implementation of Rosetta. The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST)Office of Sponsored Research (OSR) under Award No. URF/1/1976-04.
    Publisher
    Elsevier BV
    Journal
    Computational and Structural Biotechnology Journal
    DOI
    10.1016/j.csbj.2017.01.011
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S2001037016300885
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.csbj.2017.01.011
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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