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Composite Sequence-Structure Stability Models as Screening Tools for Identifying Vulnerable Targets for HIV Drug and Vaccine Development.

Manocheewa S, Mittler JE, Samudrala R, Mullins JI - Viruses (2015)

Bottom Line: The destabilizing mutations predicted by these models were rarely found in a database of 5811 HIV-1 CA coding sequences, with none being present at a frequency greater than 2%.Furthermore, 90% of variants with the low predicted stability (from a set of 184 CA variants whose replication fitness or infectivity has been studied in vitro) had aberrant capsid structures and reduced viral infectivity.The CA regions enriched with these sites also overlap with peptides shown to induce cellular immune responses associated with lower viral loads in infected individuals.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, University ofWashington, Seattle,WA 98195-8070, USA. manocs@uw.edu.

ABSTRACT
Rapid evolution and high sequence diversity enable Human Immunodeficiency Virus (HIV) populations to acquire mutations to escape antiretroviral drugs and host immune responses, and thus are major obstacles for the control of the pandemic. One strategy to overcome this problem is to focus drugs and vaccines on regions of the viral genome in which mutations are likely to cripple function through destabilization of viral proteins. Studies relying on sequence conservation alone have had only limited success in determining critically important regions. We tested the ability of two structure-based computational models to assign sites in the HIV-1 capsid protein (CA) that would be refractory to mutational change. The destabilizing mutations predicted by these models were rarely found in a database of 5811 HIV-1 CA coding sequences, with none being present at a frequency greater than 2%. Furthermore, 90% of variants with the low predicted stability (from a set of 184 CA variants whose replication fitness or infectivity has been studied in vitro) had aberrant capsid structures and reduced viral infectivity. Based on the predicted stability, we identified 45 CA sites prone to destabilizing mutations. More than half of these sites are targets of one or more known CA inhibitors. The CA regions enriched with these sites also overlap with peptides shown to induce cellular immune responses associated with lower viral loads in infected individuals. Lastly, a joint scoring metric that takes into account both sequence conservation and protein structure stability performed better at identifying deleterious mutations than sequence conservation or structure stability information alone. The computational sequence-structure stability approach proposed here might therefore be useful for identifying immutable sites in a protein for experimental validation as potential targets for drug and vaccine development.

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Receiver operating characteristic curve of HIV-1 subtype B non-infectious mutations predictions. The composite score is the sum of the FOLDEF-stability rank and the mutation-frequency rank.
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viruses-07-02901-f006: Receiver operating characteristic curve of HIV-1 subtype B non-infectious mutations predictions. The composite score is the sum of the FOLDEF-stability rank and the mutation-frequency rank.

Mentions: The second dataset included an additional 128 substitutions with known impact on infectivity in a subtype B virus backbone [9,11,14,16,39]. The stability distributions of infectious and non-infectious mutations resembled those of conical capsid and aberrant capsid conferring mutations (Figure 4). Using FOLDEF with the mature CA hexamer and CTD dimer, mutants predicted to be as stable as the reference structures were associated with both conical capsid shape (p = 5.4 × 10−4; Fisher’s exact test) and infectiousness (p = 2.76 × 10−10). We also observed a moderate correlation between absolute changes in FOLDEF, but not DOPE, stability and viral infectivity (Figure 5). Using the stability level of the reference structures as the threshold, any mutation resulting in a less stable structure was predicted to be non-infectious, with a prediction accuracy of 75% when the CA hexamer and the CTD dimer were used as the template structure, respectively. The accuracy improved slightly to 76% when the CTD of the HOH were used as the template structure for CTD mutations. This combination of template structures yielded the best prediction accuracy (Table S1). Compared to mutation frequency, protein structural stability, as predicted by FOLDEF, performed better in classifying infectious vs. non-infectious mutations (Table 1 and Figure 6).


Composite Sequence-Structure Stability Models as Screening Tools for Identifying Vulnerable Targets for HIV Drug and Vaccine Development.

Manocheewa S, Mittler JE, Samudrala R, Mullins JI - Viruses (2015)

Receiver operating characteristic curve of HIV-1 subtype B non-infectious mutations predictions. The composite score is the sum of the FOLDEF-stability rank and the mutation-frequency rank.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664974&req=5

viruses-07-02901-f006: Receiver operating characteristic curve of HIV-1 subtype B non-infectious mutations predictions. The composite score is the sum of the FOLDEF-stability rank and the mutation-frequency rank.
Mentions: The second dataset included an additional 128 substitutions with known impact on infectivity in a subtype B virus backbone [9,11,14,16,39]. The stability distributions of infectious and non-infectious mutations resembled those of conical capsid and aberrant capsid conferring mutations (Figure 4). Using FOLDEF with the mature CA hexamer and CTD dimer, mutants predicted to be as stable as the reference structures were associated with both conical capsid shape (p = 5.4 × 10−4; Fisher’s exact test) and infectiousness (p = 2.76 × 10−10). We also observed a moderate correlation between absolute changes in FOLDEF, but not DOPE, stability and viral infectivity (Figure 5). Using the stability level of the reference structures as the threshold, any mutation resulting in a less stable structure was predicted to be non-infectious, with a prediction accuracy of 75% when the CA hexamer and the CTD dimer were used as the template structure, respectively. The accuracy improved slightly to 76% when the CTD of the HOH were used as the template structure for CTD mutations. This combination of template structures yielded the best prediction accuracy (Table S1). Compared to mutation frequency, protein structural stability, as predicted by FOLDEF, performed better in classifying infectious vs. non-infectious mutations (Table 1 and Figure 6).

Bottom Line: The destabilizing mutations predicted by these models were rarely found in a database of 5811 HIV-1 CA coding sequences, with none being present at a frequency greater than 2%.Furthermore, 90% of variants with the low predicted stability (from a set of 184 CA variants whose replication fitness or infectivity has been studied in vitro) had aberrant capsid structures and reduced viral infectivity.The CA regions enriched with these sites also overlap with peptides shown to induce cellular immune responses associated with lower viral loads in infected individuals.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, University ofWashington, Seattle,WA 98195-8070, USA. manocs@uw.edu.

ABSTRACT
Rapid evolution and high sequence diversity enable Human Immunodeficiency Virus (HIV) populations to acquire mutations to escape antiretroviral drugs and host immune responses, and thus are major obstacles for the control of the pandemic. One strategy to overcome this problem is to focus drugs and vaccines on regions of the viral genome in which mutations are likely to cripple function through destabilization of viral proteins. Studies relying on sequence conservation alone have had only limited success in determining critically important regions. We tested the ability of two structure-based computational models to assign sites in the HIV-1 capsid protein (CA) that would be refractory to mutational change. The destabilizing mutations predicted by these models were rarely found in a database of 5811 HIV-1 CA coding sequences, with none being present at a frequency greater than 2%. Furthermore, 90% of variants with the low predicted stability (from a set of 184 CA variants whose replication fitness or infectivity has been studied in vitro) had aberrant capsid structures and reduced viral infectivity. Based on the predicted stability, we identified 45 CA sites prone to destabilizing mutations. More than half of these sites are targets of one or more known CA inhibitors. The CA regions enriched with these sites also overlap with peptides shown to induce cellular immune responses associated with lower viral loads in infected individuals. Lastly, a joint scoring metric that takes into account both sequence conservation and protein structure stability performed better at identifying deleterious mutations than sequence conservation or structure stability information alone. The computational sequence-structure stability approach proposed here might therefore be useful for identifying immutable sites in a protein for experimental validation as potential targets for drug and vaccine development.

Show MeSH
Related in: MedlinePlus