Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories
Molecular dynamics simulation is commonly employed to explore protein dynamics. Despite the disparate timescales between functional mechanisms and molecular dynamics (MD) trajectories, functional differences are often inferred from differences in conformational ensembles between two proteins in stru...
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MDPI AG
2017-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/19/12/646 |
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| author | Jenny Farmer Fareeha Kanwal Nikita Nikulsin Matthew C. B. Tsilimigras Donald J. Jacobs |
| author_facet | Jenny Farmer Fareeha Kanwal Nikita Nikulsin Matthew C. B. Tsilimigras Donald J. Jacobs |
| author_sort | Jenny Farmer |
| collection | DOAJ |
| description | Molecular dynamics simulation is commonly employed to explore protein dynamics. Despite the disparate timescales between functional mechanisms and molecular dynamics (MD) trajectories, functional differences are often inferred from differences in conformational ensembles between two proteins in structure-function studies that investigate the effect of mutations. A common measure to quantify differences in dynamics is the root mean square fluctuation (RMSF) about the average position of residues defined by C α -atoms. Using six MD trajectories describing three native/mutant pairs of beta-lactamase, we make comparisons with additional measures that include Jensen-Shannon, modifications of Kullback-Leibler divergence, and local p-values from 1-sample Kolmogorov-Smirnov tests. These additional measures require knowing a probability density function, which we estimate by using a nonparametric maximum entropy method that quantifies rare events well. The same measures are applied to distance fluctuations between C α -atom pairs. Results from several implementations for quantitative comparison of a pair of MD trajectories are made based on fluctuations for on-residue and residue-residue local dynamics. We conclude that there is almost always a statistically significant difference between pairs of 100 ns all-atom simulations on moderate-sized proteins as evident from extraordinarily low p-values. |
| first_indexed | 2024-04-11T14:06:39Z |
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| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T14:06:39Z |
| publishDate | 2017-11-01 |
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| spelling | doaj.art-1f241b928ed047d2b15a414928a835fc2022-12-22T04:19:52ZengMDPI AGEntropy1099-43002017-11-01191264610.3390/e19120646e19120646Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation TrajectoriesJenny Farmer0Fareeha Kanwal1Nikita Nikulsin2Matthew C. B. Tsilimigras3Donald J. Jacobs4Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USAMolecular dynamics simulation is commonly employed to explore protein dynamics. Despite the disparate timescales between functional mechanisms and molecular dynamics (MD) trajectories, functional differences are often inferred from differences in conformational ensembles between two proteins in structure-function studies that investigate the effect of mutations. A common measure to quantify differences in dynamics is the root mean square fluctuation (RMSF) about the average position of residues defined by C α -atoms. Using six MD trajectories describing three native/mutant pairs of beta-lactamase, we make comparisons with additional measures that include Jensen-Shannon, modifications of Kullback-Leibler divergence, and local p-values from 1-sample Kolmogorov-Smirnov tests. These additional measures require knowing a probability density function, which we estimate by using a nonparametric maximum entropy method that quantifies rare events well. The same measures are applied to distance fluctuations between C α -atom pairs. Results from several implementations for quantitative comparison of a pair of MD trajectories are made based on fluctuations for on-residue and residue-residue local dynamics. We conclude that there is almost always a statistically significant difference between pairs of 100 ns all-atom simulations on moderate-sized proteins as evident from extraordinarily low p-values.https://www.mdpi.com/1099-4300/19/12/646molecular dynamicsconformational fluctuationsconformational similarity measuresp-valuesstatistical significancebeta-lactamasesite directed mutations |
| spellingShingle | Jenny Farmer Fareeha Kanwal Nikita Nikulsin Matthew C. B. Tsilimigras Donald J. Jacobs Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories Entropy molecular dynamics conformational fluctuations conformational similarity measures p-values statistical significance beta-lactamase site directed mutations |
| title | Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories |
| title_full | Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories |
| title_fullStr | Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories |
| title_full_unstemmed | Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories |
| title_short | Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories |
| title_sort | statistical measures to quantify similarity between molecular dynamics simulation trajectories |
| topic | molecular dynamics conformational fluctuations conformational similarity measures p-values statistical significance beta-lactamase site directed mutations |
| url | https://www.mdpi.com/1099-4300/19/12/646 |
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