ArticleThe Journal of chemical physics2013
Multiscale multiphysics and multidomain models--flexibility and rigidity.
Article in The Journal of chemical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Correlated clustering and projection for dimensionality reduction.Machine learning: science and technology · 2026Article
- Predicting protein-nucleic acid flexibility using persistent sheaf Laplacians.Physical chemistry chemical physics : PCCP · 2026Article
- Persistent Sheaf Laplacian Analysis of Protein Flexibility.The journal of physical chemistry. B · 2025Article
- Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- Multiscale Differential Geometry Learning for Protein Flexibility Analysis.Journal of computational chemistry · 2025Article
- Mayer-Homology Learning Prediction of Protein-Ligand Binding Affinities.Journal of computational biophysics and chemistry · 2025Article
- Poisson-Boltzmann-based machine learning model for electrostatic analysis.Biophysical journal · 2024Article
- Preprocessing of Single Cell RNA Sequencing Data Using Correlated Clustering and Projection.Journal of chemical information and modeling · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
- Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE.PloS one · 2024Article
- Systematic review of digital twin technology and applications.Visual computing for industry, biomedicine, and art · 2023Review
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.Chemical reviews · 2022Review
- Geometric algebra generation of molecular surfaces.Journal of the Royal Society, Interface · 2022Article
- AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules.Computers in biology and medicine · 2021Article
- Review of COVID-19 Antibody Therapies.Annual review of biophysics · 2021Article
- Decoding Asymptomatic COVID-19 Infection and Transmission.The journal of physical chemistry letters · 2020Review
- Persistent spectral graph.International journal for numerical methods in biomedical engineering · 2020Article
- Characterizing SARS-CoV-2 mutations in the United States.Research square · 2020Article
- The de Rham-Hodge Analysis and Modeling of Biomolecules.Bulletin of mathematical biology · 2020Article
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3 authors.
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Abstract
The emerging complexity of large macromolecules has led to challenges in their full scale theoretical description and computer simulation. Multiscale multiphysics and multidomain models have been introduced to reduce the number of degrees of freedom while maintaining modeling accuracy and achieving computational efficiency. A total energy functional is constructed to put energies for polar and nonpolar solvation, chemical potential, fluid flow, molecular mechanics, and elastic dynamics on an equal footing. The variational principle is utilized to derive coupled governing equations for the above mentioned multiphysical descriptions. Among these governing equations is the Poisson-Boltzmann equation which describes continuum electrostatics with atomic charges. The present work introduces the theory of continuum elasticity with atomic rigidity (CEWAR). The essence of CEWAR is to formulate the shear modulus as a continuous function of atomic rigidity. As a result, the dynamics complexity of a macromolecular system is separated from its static complexity so that the more time-consuming dynamics is handled with continuum elasticity theory, while the less time-consuming static analysis is pursued with atomic approaches. We propose a simple method, flexibility-rigidity index (FRI), to analyze macromolecular flexibility and rigidity in atomic detail. The construction of FRI relies on the fundamental assumption that protein functions, such as flexibility, rigidity, and energy, are entirely determined by the structure of the protein and its environment, although the structure is in turn determined by all the interactions. As such, the FRI measures the topological connectivity of protein atoms or residues and characterizes the geometric compactness of the protein structure. As a consequence, the FRI does not resort to the interaction Hamiltonian and bypasses matrix diagonalization, which underpins most other flexibility analysis methods. FRI's computational complexity is of O(N(2)) at most, where N is the number of atoms or residues, in contrast to O(N(3)) for Hamiltonian based methods. We demonstrate that the proposed FRI gives rise to accurate prediction of protein B-Factor for a set of 263 proteins. We show that a parameter free FRI is able to achieve about 95% accuracy of the parameter optimized FRI. An interpolation algorithm is developed to construct continuous atomic flexibility functions for visualization and use with CEWAR.
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