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Free Energy Predictions of Ligand Binding to an α‑Helix Using Steered Molecular Dynamics and Umbrella Sampling Simulations

Overview of attention for article published in Journal of Chemical Information and Modeling, July 2014
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Article details
Title
Free Energy Predictions of Ligand Binding to an α‑Helix Using Steered Molecular Dynamics and Umbrella Sampling Simulations
Published in
Journal of Chemical Information and Modeling, July 2014
DOI 10.1021/ci500164q
Pubmed ID
Authors
Abstract

Free energy prediction of ligand binding to macromolecules using explicit solvent molecular dynamics (MD) simulations is computationally very expensive. Recently, we reported a linear correlation between the binding free energy obtained via umbrella sampling (US) versus the rupture force from steered molecular dynamics (SMD) simulations for epigallocatechin-3-gallate (EGCG) binding to α-helical rich keratin. This linear correlation suggests a potential route for fast free-energy predictions using SMD alone. In this work, the generality of the linear correlation is further tested for several ligands interacting with the α-helical motif of keratin. These molecules have significantly varying properties i.e. octanol/water partition coefficient (logP), and/or overall charges (oleic acid, catechin, Fe(2+), citric acid, hydrogen citrate, dihydrogen citrate and citrate). Using the constant loading rate of our previous study of the keratin-EGCG system, we observe that the linear correlation for keratin-EGCG can be extended to other uncharged molecules where interactions are governed by hydrogen bonds and/or a combination of hydrogen bonds and hydrophobic forces. For molecules where interactions with the keratin helix are governed primarily by electrostatics between charged molecules, a second, alternative linear correlation model is derived. Whilst further investigations are needed to expand the molecular space and build a fully predictive model, the current approach represents a promising methodology for fast free energy predictions based on short SMD simulations (requiring picoseconds to nanoseconds of sampling) for defined biomolecular systems.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Iran, Islamic Republic of 1 2%
Germany 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 18 31%
Researcher 12 21%
Student > Doctoral Student 4 7%
Student > Bachelor 4 7%
Professor 4 7%
Other 7 12%
Unknown 9 16%
Readers by discipline
Readers by discipline Count As %
Chemistry 14 24%
Biochemistry, Genetics and Molecular Biology 7 12%
Agricultural and Biological Sciences 7 12%
Chemical Engineering 5 9%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Other 10 17%
Unknown 11 19%
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 28 July 2016.
All research outputs
#18,784,918
of 27,265,997 outputs
Outputs from Journal of Chemical Information and Modeling
#5,252
of 6,270 outputs
Outputs of similar age
#142,442
of 229,991 outputs
Outputs of similar age from Journal of Chemical Information and Modeling
#33
of 70 outputs
Altmetric has tracked 27,265,997 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,270 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 229,991 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.