Statistical learning of protein elastic network from positional covariance matrix
Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A l...
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023001320 |
| _version_ | 1797384146221268992 |
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| author | Chieh Cheng Yu Nixon Raj Jhih-Wei Chu |
| author_facet | Chieh Cheng Yu Nixon Raj Jhih-Wei Chu |
| author_sort | Chieh Cheng Yu |
| collection | DOAJ |
| description | Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data. |
| first_indexed | 2024-03-08T21:31:16Z |
| format | Article |
| id | doaj.art-7360c1d27fd54cd6995bfd1d0c50afc2 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:16Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
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| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-7360c1d27fd54cd6995bfd1d0c50afc22023-12-21T07:31:18ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012125242535Statistical learning of protein elastic network from positional covariance matrixChieh Cheng Yu0Nixon Raj1Jhih-Wei Chu2Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROCInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROCInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROC; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROC; Institute of Molecular Medicine and Bioengineering, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROC; Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROC; Corresponding author at: Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan, ROC.Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.http://www.sciencedirect.com/science/article/pii/S2001037023001320Statistical learningElastic network modelPositional covariance matrixAll-atom molecular dynamics simulationHomologous structureSerine protease |
| spellingShingle | Chieh Cheng Yu Nixon Raj Jhih-Wei Chu Statistical learning of protein elastic network from positional covariance matrix Computational and Structural Biotechnology Journal Statistical learning Elastic network model Positional covariance matrix All-atom molecular dynamics simulation Homologous structure Serine protease |
| title | Statistical learning of protein elastic network from positional covariance matrix |
| title_full | Statistical learning of protein elastic network from positional covariance matrix |
| title_fullStr | Statistical learning of protein elastic network from positional covariance matrix |
| title_full_unstemmed | Statistical learning of protein elastic network from positional covariance matrix |
| title_short | Statistical learning of protein elastic network from positional covariance matrix |
| title_sort | statistical learning of protein elastic network from positional covariance matrix |
| topic | Statistical learning Elastic network model Positional covariance matrix All-atom molecular dynamics simulation Homologous structure Serine protease |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023001320 |
| work_keys_str_mv | AT chiehchengyu statisticallearningofproteinelasticnetworkfrompositionalcovariancematrix AT nixonraj statisticallearningofproteinelasticnetworkfrompositionalcovariancematrix AT jhihweichu statisticallearningofproteinelasticnetworkfrompositionalcovariancematrix |