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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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Related in: MedlinePlus

Distribution of capsid protein mutant stabilities based on flexible-backbone models of the mature capsid (CA) hexamer. The stability bin reflects the structural stability from higher (left) to Revise the asterisks into Palatino linotype. lower (right) levels. * indicates the bin in which reference structures were found. All mutations predicted by Discrete optimized protein energy (DOPE) (A,C) and FoldX energy function (FOLDEF) (B,D) were classified into three groups based on their frequency in the HIV sequence database. Only results from five higher, five lower and the reference model bins are shown, as together they accounted for more than 98% of all models.
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viruses-07-02901-f001: Distribution of capsid protein mutant stabilities based on flexible-backbone models of the mature capsid (CA) hexamer. The stability bin reflects the structural stability from higher (left) to Revise the asterisks into Palatino linotype. lower (right) levels. * indicates the bin in which reference structures were found. All mutations predicted by Discrete optimized protein energy (DOPE) (A,C) and FoldX energy function (FOLDEF) (B,D) were classified into three groups based on their frequency in the HIV sequence database. Only results from five higher, five lower and the reference model bins are shown, as together they accounted for more than 98% of all models.

Mentions: Using the DOPE scoring function, the predicted stabilities of mutant models had a normal distribution, with the peak being the same bin representing the structural stability of the reference protein. About one-fifth of the flexible-backbone models were predicted to be as stable as the reference structures and roughly equal numbers of the remaining mutants were predicted to be more or less stable (Figure 1A and Figure S3). In contrast, using the FOLDEF scoring function, almost half of the mutant models were predicted to be as stable as the reference models. The other half were predicted to have lower stability and only ~2% were predicted to be more stable (Figure 1B). Similar predicted stability distributions were observed using fixed-backbone modes, notwithstanding a larger variation in FOLDEF stabilities for the reference models (Figure S3).


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)

Distribution of capsid protein mutant stabilities based on flexible-backbone models of the mature capsid (CA) hexamer. The stability bin reflects the structural stability from higher (left) to Revise the asterisks into Palatino linotype. lower (right) levels. * indicates the bin in which reference structures were found. All mutations predicted by Discrete optimized protein energy (DOPE) (A,C) and FoldX energy function (FOLDEF) (B,D) were classified into three groups based on their frequency in the HIV sequence database. Only results from five higher, five lower and the reference model bins are shown, as together they accounted for more than 98% of all models.
© Copyright Policy
Related In: Results  -  Collection

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

viruses-07-02901-f001: Distribution of capsid protein mutant stabilities based on flexible-backbone models of the mature capsid (CA) hexamer. The stability bin reflects the structural stability from higher (left) to Revise the asterisks into Palatino linotype. lower (right) levels. * indicates the bin in which reference structures were found. All mutations predicted by Discrete optimized protein energy (DOPE) (A,C) and FoldX energy function (FOLDEF) (B,D) were classified into three groups based on their frequency in the HIV sequence database. Only results from five higher, five lower and the reference model bins are shown, as together they accounted for more than 98% of all models.
Mentions: Using the DOPE scoring function, the predicted stabilities of mutant models had a normal distribution, with the peak being the same bin representing the structural stability of the reference protein. About one-fifth of the flexible-backbone models were predicted to be as stable as the reference structures and roughly equal numbers of the remaining mutants were predicted to be more or less stable (Figure 1A and Figure S3). In contrast, using the FOLDEF scoring function, almost half of the mutant models were predicted to be as stable as the reference models. The other half were predicted to have lower stability and only ~2% were predicted to be more stable (Figure 1B). Similar predicted stability distributions were observed using fixed-backbone modes, notwithstanding a larger variation in FOLDEF stabilities for the reference models (Figure S3).

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