ArticleThe Journal of chemical physics2015
Communication: Capturing protein multiscale thermal fluctuations.
Article in The Journal of chemical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- 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
- Knot data analysis using multiscale Gauss link integral.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- B-factor prediction in proteins using a sequence-based deep learning model.Patterns (New York, N.Y.) · 2023Article
- Multiscale Laplacian Learning.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.Chemical reviews · 2022Review
- EVOLUTIONARY DE RHAM-HODGE METHOD.Discrete and continuous dynamical systems. Series B · 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
- 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
- DG-GL: Differential geometry-based geometric learning of molecular datasets.International journal for numerical methods in biomedical engineering · 2019Article
- Blind prediction of protein B-factor and flexibility.The Journal of chemical physics · 2018Article
- Cytoplasmic dynein binding, run length, and velocity are guided by long-range electrostatic interactions.Scientific reports · 2016Article
- Flexibility-rigidity index for protein-nucleic acid flexibility and fluctuation analysis.Journal of computational chemistry · 2016Article
- Multiscale method for modeling binding phenomena involving large objects: application to kinesin motor domains motion along microtubules.Scientific reports · 2016Article
- Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM).The Journal of chemical physics · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Existing elastic network models are typically parametrized at a given cutoff distance and often fail to properly predict the thermal fluctuation of many macromolecules that involve multiple characteristic length scales. We introduce a multiscale flexibility-rigidity index (mFRI) method to resolve this problem. The proposed mFRI utilizes two or three correlation kernels parametrized at different length scales to capture protein interactions at corresponding scales. It is about 20% more accurate than the Gaussian network model (GNM) in the B-factor prediction of a set of 364 proteins. Additionally, the present method is able to deliver accurate predictions for some large macromolecules on which GNM fails to produce accurate predictions. Finally, for a protein of N residues, mFRI is of linear scaling (O(N)) in computational complexity, in contrast to the order of O(N(3)) for GNM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.