The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing.


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.

Submission Details


Submitter: Connie Wang

Submission Date: Oct. 22, 2018, 10:46 a.m.

Version: 1

Publication Details
Mann JK;Barton JP;Ferguson AL;Omarjee S;Walker BD;Chakraborty A;Ndung'u T,PLoS Comput Biol (2014) The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing. PMID:25102049
Additional Information

Study Summary

Number of data points 38
Proteins Gag-Pol polyprotein
Unique complexes 38
Assays/Quantities/Protocols Experimental Assay: Replication capacity ratio
Libraries Supplmental Table 1: Replication Capacity of Gag p24

Structure view and single mutant data analysis

Study data

No weblogo for data of varying length.
Colors: D E R H K S T N Q A V I L M F Y W C G P

Data Distribution

Studies with similar sequences (approximate matches)

Correlation with other assays (exact sequence matches)

Relevant UniProtKB Entries

Percent Identity Matching Chains Protein Accession Entry Name
100.0 Gag-Pol polyprotein P12497 POL_HV1N5
97.0 Gag-Pol polyprotein P35963 POL_HV1Y2
95.5 Gag-Pol polyprotein P04585 POL_HV1H2
95.5 Gag-Pol polyprotein P0C6F2 POL_HV1LW
95.3 Gag-Pol polyprotein P20892 POL_HV1OY
96.2 Gag-Pol polyprotein Q73368 POL_HV1B9
95.0 Gag-Pol polyprotein P03367 POL_HV1BR
94.5 Gag-Pol polyprotein P03366 POL_HV1B1
96.1 Gag-Pol polyprotein P03369 POL_HV1A2
94.4 Gag-Pol polyprotein P20875 POL_HV1JR
94.4 Gag-Pol polyprotein P04587 POL_HV1B5
94.6 Gag-Pol polyprotein P05959 POL_HV1RH
93.9 Gag-Pol polyprotein P12499 POL_HV1Z2
93.7 Gag-Pol polyprotein P18802 POL_HV1ND
93.2 Gag-Pol polyprotein P05961 POL_HV1MN
92.8 Gag-Pol polyprotein P04589 POL_HV1EL
94.4 Gag-Pol polyprotein P05960 POL_HV1C4
93.8 Gag-Pol polyprotein P12498 POL_HV1J3
92.2 Gag-Pol polyprotein P12493 GAG_HV1N5
93.1 Gag-Pol polyprotein P05889 GAG_HV1W2
97.3 Gag-Pol polyprotein P04586 POL_HV1Z6