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Biomolecular Simulations

Overview of attention for book
Cover of 'Biomolecular Simulations'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Atomistic Force Fields for Proteins
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    Chapter 2 Force Fields for Small Molecules
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    Chapter 3 Improvement of RNA Simulations with Torsional Revisions of the AMBER Force Field
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    Chapter 4 Quantum Chemical and QM/MM Models in Biochemistry
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    Chapter 5 A Practical View of the Martini Force Field.
  7. Altmetric Badge
    Chapter 6 Using SMOG 2 to Simulate Complex Biomolecular Assemblies
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    Chapter 7 Replica-Exchange Methods for Biomolecular Simulations
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    Chapter 8 Metadynamics to Enhance Sampling in Biomolecular Simulations
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    Chapter 9 Protein–Ligand Binding Free Energy Calculations with FEP+
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    Chapter 10 Ligand-Binding Calculations with Metadynamics
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    Chapter 11 The Adaptive Path Collective Variable: A Versatile Biasing Approach to Compute the Average Transition Path and Free Energy of Molecular Transitions
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    Chapter 12 Google-Accelerated Biomolecular Simulations.
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    Chapter 13 A Practical Guide to the Simultaneous Determination of Protein Structure and Dynamics Using Metainference
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    Chapter 14 Inferring Structural Ensembles of Flexible and Dynamic Macromolecules Using Bayesian, Maximum Entropy, and Minimal-Ensemble Refinement Methods
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    Chapter 15 Modeling Biological Complexes Using Integrative Modeling Platform
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    Chapter 16 Coevolutionary Analysis of Protein Sequences for Molecular Modeling
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    Chapter 17 Coarse Graining of a Giant Molecular System: The Chromatin Fiber
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    Chapter 18 Analyzing Biomolecular Ensembles
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    Chapter 19 Using Data-Reduction Techniques to Analyze Biomolecular Trajectories
  21. Altmetric Badge
    Chapter 20 Analysis Libraries for Molecular Trajectories: A Cross-Language Synopsis
  22. Altmetric Badge
    Chapter 21 Analyzing and Biasing Simulations with PLUMED
Attention for Chapter 5: A Practical View of the Martini Force Field.
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Citations

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Chapter title
A Practical View of the Martini Force Field.
Chapter number 5
Book title
Biomolecular Simulations
Published in
Methods in molecular biology, January 2019
DOI 10.1007/978-1-4939-9608-7_5
Pubmed ID
31396901
Book ISBNs
978-1-4939-9607-0, 978-1-4939-9608-7
Authors

Bart M. H. Bruininks, Paulo C. T. Souza, Siewert J. Marrink, Bruininks, Bart M. H., Souza, Paulo C. T., Marrink, Siewert J.

Abstract

Martini is a coarse-grained (CG) force field suitable for molecular dynamics (MD) simulations of (bio)molecular systems. It is based on mapping of two to four heavy atoms to one CG particle. The effective interactions between the CG particles are parametrized to reproduce partitioning free energies of small chemical compounds between polar and apolar phases. In this chapter, a summary of the key elements of this CG force field is presented, followed by an example of practical application: a lipoplex-membrane fusion experiment. Formulated as hands-on practice, this chapter contains guidelines to build CG models of important biological systems, such as asymmetric bilayers and double-stranded DNA. Finally, a series of notes containing useful information, limitations, and tips are described in the last section.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Researcher 10 15%
Student > Master 8 12%
Student > Bachelor 6 9%
Other 5 8%
Other 7 11%
Unknown 15 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 18%
Chemistry 12 18%
Agricultural and Biological Sciences 4 6%
Engineering 4 6%
Materials Science 3 5%
Other 14 22%
Unknown 16 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 August 2019.
All research outputs
#15,332,207
of 23,577,654 outputs
Outputs from Methods in molecular biology
#4,903
of 13,410 outputs
Outputs of similar age
#254,884
of 440,978 outputs
Outputs of similar age from Methods in molecular biology
#28
of 52 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,410 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 440,978 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.

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