A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures

doi:10.4208/cicp.OA-2018-0140

Title: A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures

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Abstract

Atomic-level structural characterization of flexible proteins, such as intrinsically disordered proteins and multi-domain proteins connected by flexible linkers, is challenging as they possess distinct conformations in physiological conditions. Significant efforts have been made to develop integrated approaches by combining small angle neutron/X-ray scattering experiments with molecular simulations to reveal the distinct atomic structures and the corresponding populations for these flexible proteins. One widely used method, the basis-set supported ensemble method, classifies the simulation-generated protein conformations into a set of structural basis and then derives the corresponding populations by fitting to the experimental data. This method makes an implicit assumption that protein conformations of similar structures have similar small angle scattering profiles.The present work demonstrates that, for various protein systems ranging from compact globular proteins and flexible multidomain proteins through to intrinsically disordered proteins, this method provides inaccurate assessment of the structural ensemble of the protein molecules due to the breakdown of the assumption made. To alleviate this problem, a two-fold-clustering method is developed to cluster the simulation-generated protein structures using information on both 3D structure and scattering profiles. As benchmarked by both simulation and experimental results, this new method yields much more accurate populations of structural basis of protein molecules.

Authors:

Tan, Pan ^[1]; Fu, Zuyue ^[2];

^[3];

^[4]; You, Delin ^[5]; Wei, Dongqing ^[5]; Li, Jinglai ^[6]; Hong, Liang ^[1]

Shanghai Jiao Tong Univ., Shanghai (China). School of Physics and Astronomy; Shanghai Jiao Tong Univ., Shanghai (China). Inst. of Natural Sciences
Shanghai Jiao Tong Univ., Shanghai (China). Inst. of Natural Sciences; Shanghai Jiao Tong Univ., Shanghai (China). Zhiyuan College
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States). Dept. of Biochemistry, Cellular & Molecular Biology
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Shanghai Jiao Tong Univ., Shanghai (China). School of Life Science and Biotechnology
Shanghai Jiao Tong Univ., Shanghai (China). Inst. of Natural Sciences; Univ. of Liverpool (United Kingdom). Dept. of Mathematical Sciences

Publication Date:: 2018-12-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1495955

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Communications in Computational Physics

Additional Journal Information:: Journal Volume: 25; Journal Issue: 4; Journal ID: ISSN 1815-2406

Publisher:: Global Science Press

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; Protein structures; statistical data analysis; Monte Carlo; cluster analysis

Citation Formats


                    Tan, Pan, Fu, Zuyue, Petridis, Loukas, Qian, Shuo, You, Delin, Wei, Dongqing, Li, Jinglai, and Hong, Liang. A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures.  United States: N. p., 2018. 
        Web.  doi:10.4208/cicp.OA-2018-0140.

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                    Tan, Pan, Fu, Zuyue, Petridis, Loukas, Qian, Shuo, You, Delin, Wei, Dongqing, Li, Jinglai, & Hong, Liang. A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures.  United States.  doi:10.4208/cicp.OA-2018-0140.

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                    Tan, Pan, Fu, Zuyue, Petridis, Loukas, Qian, Shuo, You, Delin, Wei, Dongqing, Li, Jinglai, and Hong, Liang. Sat .  
        "A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures".  United States.  doi:10.4208/cicp.OA-2018-0140.  https://www.osti.gov/servlets/purl/1495955.

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@article{osti_1495955,

  title        = {A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures},

  author       = {Tan, Pan and Fu, Zuyue and Petridis, Loukas and Qian, Shuo and You, Delin and Wei, Dongqing and Li, Jinglai and Hong, Liang},

  abstractNote = {Atomic-level structural characterization of flexible proteins, such as intrinsically disordered proteins and multi-domain proteins connected by flexible linkers, is challenging as they possess distinct conformations in physiological conditions. Significant efforts have been made to develop integrated approaches by combining small angle neutron/X-ray scattering experiments with molecular simulations to reveal the distinct atomic structures and the corresponding populations for these flexible proteins. One widely used method, the basis-set supported ensemble method, classifies the simulation-generated protein conformations into a set of structural basis and then derives the corresponding populations by fitting to the experimental data. This method makes an implicit assumption that protein conformations of similar structures have similar small angle scattering profiles.The present work demonstrates that, for various protein systems ranging from compact globular proteins and flexible multidomain proteins through to intrinsically disordered proteins, this method provides inaccurate assessment of the structural ensemble of the protein molecules due to the breakdown of the assumption made. To alleviate this problem, a two-fold-clustering method is developed to cluster the simulation-generated protein structures using information on both 3D structure and scattering profiles. As benchmarked by both simulation and experimental results, this new method yields much more accurate populations of structural basis of protein molecules.},

  doi          = {10.4208/cicp.OA-2018-0140},

  journal      = {Communications in Computational Physics},

  number       = 4,

  volume       = 25,

  place        = {United States},

  year         = {2018},

  month        = {12}

}

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DOI: 10.4208/cicp.OA-2018-0140

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