ReviewQuarterly reviews of biophysics2012
Statistical mechanics and molecular dynamics in evaluating thermodynamic properties of biomolecular recognition.
Review in Quarterly reviews of biophysics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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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.
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Who cites it
66 citing papers in PubMed, 171 citations in OpenAlex.
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- Impact of Varying Velocities and Solvation Boxes on Alchemical Free-Energy Simulations.Journal of chemical information and modeling · 2025Article
- Testing the Simplified Molecular Dynamics Approach to Improve the Reproduction of ECD Spectra and Monitor Aggregation.International journal of molecular sciences · 2024Article
- Exploring Novel Antimalarial Compounds TargetingACS omega · 2024Article
- The effects of RNA.DNA-DNA triple helices on nucleosome structures and dynamics.Biophysical journal · 2023Article
- In Silico Analyses of a Promising Drug Candidate for the Treatment of Amyotrophic Lateral Sclerosis Targeting Superoxide Dismutase I Protein.Pharmaceutics · 2023Article
- Histone variant H2A.Z modulates nucleosome dynamics to promote DNA accessibility.Nature communications · 2023Article
- Electrostatic Contributions to the Binding Free Energy of Nicotine to the Acetylcholine Binding Protein.The journal of physical chemistry. B · 2022Article
- Spectroscopic Fingerprints of Cavity Formation and Solute Insertion as a Measure of Hydration Entropic Loss and Enthalpic Gain.Angewandte Chemie (International ed. in English) · 2022Article
- Comparison of Grand Canonical and Conventional Molecular Dynamics Simulation Methods for Protein-Bound Water Networks.ACS physical chemistry Au · 2022Article
- Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation.ACS omega · 2022Article
- Bacterial 2'-Deoxyguanosine Riboswitch Classes as Potential Targets for Antibiotics: A Structure and Dynamics Study.International journal of molecular sciences · 2022Article
6 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 2 institutions in 1 country.
Funding
Abstract
Molecular recognition plays a central role in biochemical processes. Although well studied, understanding the mechanisms of recognition is inherently difficult due to the range of potential interactions, the molecular rearrangement associated with binding, and the time and length scales involved. Computational methods have the potential for not only complementing experiments that have been performed, but also in guiding future ones through their predictive abilities. In this review, we discuss how molecular dynamics (MD) simulations may be used in advancing our understanding of the thermodynamics that drive biomolecular recognition. We begin with a brief review of the statistical mechanics that form a basis for these methods. This is followed by a description of some of the most commonly used methods: thermodynamic pathways employing alchemical transformations and potential of mean force calculations, along with end-point calculations for free energy differences, and harmonic and quasi-harmonic analysis for entropic calculations. Finally, a few of the fundamental findings that have resulted from these methods are discussed, such as the role of configurational entropy and solvent in intermolecular interactions, along with selected results of the model system T4 lysozyme to illustrate potential and current limitations of these methods.
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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.