ArticleThe Journal of chemical physics2020
Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding.
Article in The Journal of chemical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 127 citations in OpenAlex.
- Discovery and structural characterization of newcastle disease virus mimetic peptides identified through phage display.Virus research · 2026Article
- Conformational Preferences for N-Glycans at the Surface of CEACAM1-Ig1.ACS chemical biology · 2026Article
- FakeRotLib: Expedient Noncanonical Amino Acid Parametrization in Rosetta.Journal of chemical information and modeling · 2025Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- Recent Developments in Amber Biomolecular Simulations.Journal of chemical information and modeling · 2025Article
- Deep learning-based dipeptidyl peptidase IV inhibitor screening, experimental validation, and GaMD/LiGaMD analysis.BMC biology · 2025Article
- Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies.The journal of physical chemistry. B · 2025Review
- Diverse toxins exhibit a common binding mode to the nicotinic acetylcholine receptors.Biophysical journal · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Mechanisms of Peptide Agonist Dissociation and Deactivation of Adhesion G-Protein-Coupled Receptors.Biochemistry · 2025Article
- Running Gaussian-accelerated Molecular Dynamics Simulations in NAMD [Article v1.0].Living journal of computational molecular science · 2025Article
- Molecular Dynamics Insights into Peptide-Based Tetrodotoxin Delivery Nanostructures.Molecules (Basel, Switzerland) · 2024Article
- Article
- Mechanisms of peptide agonist dissociation and deactivation of adhesion G-protein-coupled receptors.bioRxiv : the preprint server for biology · 2024Article
- Unveiling Allosteric Regulation and Binding Mechanism of BRD9 through Molecular Dynamics Simulations and Markov Modeling.Molecules (Basel, Switzerland) · 2024Article
- Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.Journal of chemical theory and computation · 2024Article
- Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.Molecules (Basel, Switzerland) · 2024Article
- Activation of Polycystin-1 Signaling by Binding of Stalk-derived Peptide Agonists.bioRxiv : the preprint server for biology · 2024Article
- Molecular Mechanisms of the Impaired Heparin Pentasaccharide Interactions in 10 Antithrombin Heparin Binding Site Mutants Revealed by Enhanced Sampling Molecular Dynamics.Biomolecules · 2024Article
- Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning.Molecules (Basel, Switzerland) · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method "Pep-GaMD," which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.
Indexed as
Identifiers
What OpenQuestion holds
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.