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The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003776
Pubmed ID
Authors

Jaclyn K. Mann, John P. Barton, Andrew L. Ferguson, Saleha Omarjee, Bruce D. Walker, Arup Chakraborty, Thumbi Ndung'u

Abstract

Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = -0.74, p = 3.6×10-6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = -0.83, p = 3.7×10-12). Performance of the Potts model (r = -0.73, p = 9.7×10-9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design.

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The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Saudi Arabia 1 <1%
United States 1 <1%
Unknown 100 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 25%
Researcher 24 23%
Student > Master 14 13%
Student > Bachelor 7 7%
Professor > Associate Professor 7 7%
Other 14 13%
Unknown 13 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 20%
Physics and Astronomy 15 14%
Biochemistry, Genetics and Molecular Biology 10 10%
Engineering 10 10%
Medicine and Dentistry 7 7%
Other 23 22%
Unknown 19 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 November 2016.
All research outputs
#16,345,315
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#7,027
of 9,043 outputs
Outputs of similar age
#130,738
of 242,411 outputs
Outputs of similar age from PLoS Computational Biology
#110
of 163 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% 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 242,411 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 163 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.