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Fast Calculation of Protein–Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model

Overview of attention for article published in Journal of Chemical Theory and Computation, December 2017
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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9 X users
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Article details
Title
Fast Calculation of Protein–Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model
Published in
Journal of Chemical Theory and Computation, December 2017
DOI 10.1021/acs.jctc.7b00660
Pubmed ID
Authors
Abstract

Determination of protein-protein binding affinity values is key to understanding various underlying biological phenomena, such as how mutations change protein-protein binding. Most existing non-rigorous (fast) and rigorous (slow) methods that rely on all-atom representation of the proteins force the user to choose between speed and accuracy. In an attempt to achieve balance between speed and accuracy, we have combined rigorous umbrella sampling molecular dynamics simulation with a coarse-grained protein model. We predicted the effect of mutations on binding affinity by selecting three protein-protein systems and comparing results to empirical relative binding affinity values, and to non-rigorous modeling approaches. We obtained significant improvement both in our ability to discern stabilizing from destabilizing mutations and in the correlation between predicted and experimental values compared to non-rigorous approaches. Overall our results suggest that using a rigorous affinity calculation method with coarse-grained protein models could offer fast and reliable predictions of protein-protein binding free energies.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 123 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 %
Unknown 123 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 27 22%
Researcher 22 18%
Student > Master 12 10%
Student > Bachelor 11 9%
Professor > Associate Professor 7 6%
Other 14 11%
Unknown 30 24%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 22%
Chemistry 20 16%
Agricultural and Biological Sciences 11 9%
Chemical Engineering 8 7%
Pharmacology, Toxicology and Pharmaceutical Science 5 4%
Other 17 14%
Unknown 35 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 03 June 2020.
All research outputs
#10,101,407
of 34,185,767 outputs
Outputs from Journal of Chemical Theory and Computation
#2,318
of 8,635 outputs
Outputs of similar age
#143,974
of 494,846 outputs
Outputs of similar age from Journal of Chemical Theory and Computation
#51
of 179 outputs
Altmetric has tracked 34,185,767 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 8,635 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 72% 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 494,846 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 179 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.