ReviewBiophysical reviews2012
Assessing and refining molecular dynamics simulations of proteins with nuclear magnetic resonance data.
Review in Biophysical reviews, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 9 citations in OpenAlex.
- Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0].Living journal of computational molecular science · 2025Article
- Nanobody Paratope Ensembles in Solution Characterized by MD Simulations and NMR.International journal of molecular sciences · 2022Article
- Combining Experimental Data and Computational Methods for the Non-Computer Specialist.Molecules (Basel, Switzerland) · 2020Review
- NMR Methods to Study Dynamic Allostery.PLoS computational biology · 2016Review
- Force field-dependent solution properties of glycine oligomers.Journal of computational chemistry · 2015Article
- Information flow and protein dynamics: the interplay between nuclear magnetic resonance spectroscopy and molecular dynamics simulations.Frontiers in plant science · 2015Review
- Ensemble-based interpretations of NMR structural data to describe protein internal dynamics.Molecules (Basel, Switzerland) · 2013Review
- The structure of neuronal calcium sensor-1 in solution revealed by molecular dynamics simulations.PloS one · 2013Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The sophistication of the force fields, algorithms and hardware used for molecular dynamics (MD) simulations of proteins is continuously increasing. No matter how advanced the methodology, however, it is essential to evaluate the appropriateness of the structures sampled in a simulation by comparison with quantitative experimental data. Solution nuclear magnetic resonance (NMR) data are particularly useful for checking the quality of protein simulations, as they provide both structural and dynamic information on a variety of temporal and spatial scales. Here, various features and implications of using NMR data to validate and bias MD simulations are outlined, including an overview of the different types of NMR data that report directly on structural properties and of relevant simulation techniques. The focus throughout is on how to properly account for conformational averaging, particularly within the context of the assumptions inherent in the relationships that link NMR data to structural properties.
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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.