Stochastic model for protein flexibility analysis
Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
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2016
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| Online Access: | https://hdl.handle.net/10356/82116 http://hdl.handle.net/10220/41117 |
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| author | Xia, Kelin Wei, Guo-Wei |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Xia, Kelin Wei, Guo-Wei |
| author_sort | Xia, Kelin |
| collection | NTU |
| description | Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis are based on Hamiltonian mechanics. We introduce a stochastic model to study protein flexibility. The essential idea is to analyze the free induction decay of a perturbed protein structural probability, which satisfies the master equation. The transition probability matrix is constructed by using probability density estimators including monotonically decreasing radial basis functions. We show that the proposed stochastic model gives rise to some of the best predictions of Debye-Waller factors or B factors for three sets of protein data introduced in the literature. |
| first_indexed | 2024-10-01T07:43:34Z |
| format | Journal Article |
| id | ntu-10356/82116 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:43:34Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/821162023-02-28T19:32:23Z Stochastic model for protein flexibility analysis Xia, Kelin Wei, Guo-Wei School of Physical and Mathematical Sciences Science Physics Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis are based on Hamiltonian mechanics. We introduce a stochastic model to study protein flexibility. The essential idea is to analyze the free induction decay of a perturbed protein structural probability, which satisfies the master equation. The transition probability matrix is constructed by using probability density estimators including monotonically decreasing radial basis functions. We show that the proposed stochastic model gives rise to some of the best predictions of Debye-Waller factors or B factors for three sets of protein data introduced in the literature. Published version 2016-08-10T06:56:03Z 2019-12-06T14:46:57Z 2016-08-10T06:56:03Z 2019-12-06T14:46:57Z 2013 Journal Article Xia, K., & Wei, G.-W. (2013). Stochastic model for protein flexibility analysis. Physical Review E, 88, 062709-. 1539-3755 https://hdl.handle.net/10356/82116 http://hdl.handle.net/10220/41117 10.1103/PhysRevE.88.062709 en Physical Review E © 2013 American Physical Society. This paper was published in Physical Review E and is made available as an electronic reprint (preprint) with permission of American Physical Society. The published version is available at: [http://dx.doi.org/10.1103/PhysRevE.88.062709]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 8 p. application/pdf |
| spellingShingle | Science Physics Xia, Kelin Wei, Guo-Wei Stochastic model for protein flexibility analysis |
| title | Stochastic model for protein flexibility analysis |
| title_full | Stochastic model for protein flexibility analysis |
| title_fullStr | Stochastic model for protein flexibility analysis |
| title_full_unstemmed | Stochastic model for protein flexibility analysis |
| title_short | Stochastic model for protein flexibility analysis |
| title_sort | stochastic model for protein flexibility analysis |
| topic | Science Physics |
| url | https://hdl.handle.net/10356/82116 http://hdl.handle.net/10220/41117 |
| work_keys_str_mv | AT xiakelin stochasticmodelforproteinflexibilityanalysis AT weiguowei stochasticmodelforproteinflexibilityanalysis |