ReviewBiophysical reviews2018
Relaxation mode analysis for molecular dynamics simulations of proteins.
Review in Biophysical reviews, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 24 citations in OpenAlex.
- Application of Anomaly Detection to Identify Important Features of Protein Dynamics.ACS omega · 2025Article
- Markov State Models: To Optimize or Not to Optimize.Journal of chemical theory and computation · 2024Article
- Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation.Journal of chemical information and modeling · 2023Article
- Characteristic structural difference between inactive and active states of orexin 2 receptor determined using molecular dynamics simulations.Biophysical reviews · 2022Review
- Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era.Bioinformatics and biology insights · 2020Review
- Analysis of molecular dynamics simulations of 10-residue peptide, chignolin, using statistical mechanics: Relaxation mode analysis and three-dimensional reference interaction site model theory.Biophysics and physicobiology · 2019Review
- Foreword to 'Multiscale structural biology: biophysical principles and mechanisms underlying the action of bio-nanomachines', a special issue in Honour of Fumio Arisaka's 70th birthday.Biophysical reviews · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular dynamics simulation is a powerful method for investigating the structural stability, dynamics, and function of biopolymers at the atomic level. In recent years, it has become possible to perform simulations on time scales of the order of milliseconds using special hardware. However, it is necessary to derive the important factors contributing to structural change or function from the complicated movements of biopolymers obtained from long simulations. Although some analysis methods for protein systems have been developed using increasing simulation times, many of these methods are static in nature (i.e., no information on time). In recent years, dynamic analysis methods have been developed, such as the Markov state model and relaxation mode analysis (RMA), which was introduced based on spin and homopolymer systems. The RMA method approximately extracts slow relaxation modes and rates from trajectories and decomposes the structural fluctuations into slow relaxation modes, which characterize the slow relaxation dynamics of the system. Recently, this method has been applied to biomolecular systems. In this article, we review RMA and its improved versions for protein systems.
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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.