Volume 25, Issue 4
A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures

Pan Tan, Zuyue Fu, Loukas Petridis, Shuo Qian, Delin You, Dongqing Wei, Jinglai Li & Liang Hong

Commun. Comput. Phys., 25 (2019), pp. 1010-1023.

Published online: 2018-12

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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.


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  • Keywords

Protein structures statistical data analysis Monte Carlo cluster analysis.

  • AMS Subject Headings

62-07

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-25-1010, author = {Pan Tan, Zuyue Fu, Loukas Petridis, Shuo Qian, Delin You, Dongqing Wei, Jinglai Li and Liang Hong}, title = {A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures}, journal = {Communications in Computational Physics}, year = {2018}, volume = {25}, number = {4}, pages = {1010--1023}, 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.


Supporting Information


}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2018-0140}, url = {http://global-sci.org/intro/article_detail/cicp/12888.html} }
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