ArticleProceedings of the National Academy of Sciences of the United States of America2009
Protein elastic network models and the ranges of cooperativity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
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101 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Fast prediction of protein flexibility.Bioinformatics (Oxford, England) · 2026Article
- Efficient sampling of large-scale transition pathways and intermediate conformations in sub-mesoscopic protein complexes.Nature communications · 2026Article
- GenUnfold: Rapidly Predict Protein Mechanical Unfolding Trajectory via a Physics-Guided Diffusion Model.Proceedings of machine learning research · 2026Article
- Optimizing a coarse-grained model for large-scale membrane protein simulation.Biophysical reports · 2025Article
- Enhanced Exploration of Protein Conformational Space through Integration of Ultra-Coarse-Grained Models to Multiscale Workflows.The journal of physical chemistry. B · 2025Article
- Dynamic energy conversion in protein catalysis: From brownian motion to enzymatic function.Computational and structural biotechnology journal · 2025Review
- Structural Analysis of theBiomolecules · 2024Article
- Anisotropic Network Analysis of Open/Closed HCN4 Channel Advocates Asymmetric Subunit Cooperativity in cAMP Modulation of Gating.Journal of chemical information and modeling · 2024Article
- Insight into the Nucleotide Based Modulation of the Grp94 Molecular Chaperone Using Multiscale Dynamics.The journal of physical chemistry. B · 2023Article
- Biotite: new tools for a versatile Python bioinformatics library.BMC bioinformatics · 2023Article
- Mapping allosteric pathway in NIa-Pro using computational approach.Quantitative biology (Beijing, China) · 2023Article
- Statistical learning of protein elastic network from positional covariance matrix.Computational and structural biotechnology journal · 2023Article
- Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation.PLoS computational biology · 2022Article
- Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier.Bioinformatics (Oxford, England) · 2022Article
- Probing the competitive inhibitor efficacy of frog-skin alpha helical AMPs identified against ACE2 binding to SARS-CoV-2 S1 spike protein as therapeutic scaffold to prevent COVID-19.Journal of molecular modeling · 2022Article
- Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0.Acta crystallographica. Section D, Structural biology · 2022Review
- The Inherent Coupling of Intrinsically Disordered Regions in the Multidomain Receptor Tyrosine Kinase KIT.International journal of molecular sciences · 2022Article
41 more citing papers are in PubMed but not listed here.
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Abstract
Elastic network models (ENMs) are entropic models that have demonstrated in many previous studies their abilities to capture overall the important internal motions, with comparisons having been made against crystallographic B-factors and NMR conformational variabilities. ENMs have become an increasingly important tool and have been widely used to comprehend protein dynamics, function, and even conformational changes. However, reliance upon an arbitrary cutoff distance to delimit the range of interactions has presented a drawback for these models, because the optimal cutoff values can differ somewhat from protein to protein and can lead to quirks such as some shuffling in the order of the normal modes when applied to structures that differ only slightly. Here, we have replaced the requirement for a cutoff distance and introduced the more physical concept of inverse power dependence for the interactions, with a set of elastic network models that are parameter-free, with the distance cutoff removed. For small fluctuations about the native forms, the power dependence is the inverse square, but for larger deformations, the power dependence may become inverse 6th or 7th power. These models maintain and enhance the simplicity and generality of the original ENMs, and at the same time yield better predictions of crystallographic B-factors (both isotropic and anisotropic) and of the directions of conformational transitions. Thus, these parameter-free ENMs can be models of choice whenever elastic network models are used.
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