ArticleBulletin of mathematical biology2013
Coarse grained normal mode analysis vs. refined Gaussian Network Model for protein residue-level structural fluctuations.
Article in Bulletin of mathematical biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.Proteins · 2026Article
- Predicting protein-nucleic acid flexibility using persistent sheaf Laplacians.Physical chemistry chemical physics : PCCP · 2026Article
- Machine learning predictions from unpredictable chaos.Journal of the Royal Society, Interface · 2025Article
- Persistent Sheaf Laplacian Analysis of Protein Flexibility.The journal of physical chemistry. B · 2025Article
- Multiscale Differential Geometry Learning for Protein Flexibility Analysis.Journal of computational chemistry · 2025Article
- Multi-scale Jones polynomial and persistent Jones polynomial for knot data analysis.AIMS mathematics · 2025Article
- Knot data analysis using multiscale Gauss link integral.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Comparative Analysis of Structural and Dynamical Features of Ribosome Upon Association With mRNA Reveals Potential Role of Ribosomal Proteins.Frontiers in molecular biosciences · 2021Article
- Evolutionary homology on coupled dynamical systems with applications to protein flexibility analysis.Journal of applied and computational topology · 2020Article
- Persistent spectral graph.International journal for numerical methods in biomedical engineering · 2020Article
- A review of mathematical representations of biomolecular data.Physical chemistry chemical physics : PCCP · 2020Review
- MathDL: mathematical deep learning for D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Atom-specific persistent homology and its application to protein flexibility analysis.Computational and mathematical biophysics · 2020Article
- AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.Journal of chemical information and modeling · 2019Article
- Interpreting the Dynamics of Binding Interactions of snRNA and U1A Using a Coarse-Grained Model.Biophysical journal · 2019Article
- Blind prediction of protein B-factor and flexibility.The Journal of chemical physics · 2018Article
- Flexibility-rigidity index for protein-nucleic acid flexibility and fluctuation analysis.Journal of computational chemistry · 2016Article
- Are protein-protein interfaces special regions on a protein's surface?The Journal of chemical physics · 2015Article
- Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM).The Journal of chemical physics · 2015Article
- Communication: Capturing protein multiscale thermal fluctuations.The Journal of chemical physics · 2015Article
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3 authors.
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Abstract
We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms C(α), C, N, and C(β) are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.
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