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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

Mutations observed in a database of 5811 HIV-1 capsid sequences. Mutations resulting in non-infectious or infectious viruses are shown separately and stratified based on their database frequencies. White boxes represent the percentage of mutations not found in the database.
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viruses-07-02901-f002: Mutations observed in a database of 5811 HIV-1 capsid sequences. Mutations resulting in non-infectious or infectious viruses are shown separately and stratified based on their database frequencies. White boxes represent the percentage of mutations not found in the database.

Mentions: The observation that the majority of observed mutations were not predicted to alter protein stability, in contrast to unobserved mutations, suggests that any mutation found in the database has a high likelihood of being tolerated. To explore this question, we examined the frequency of 184 point mutations with known impact on infectivity in subtype B HIV-1. The mutated residues were scattered throughout the CA, with 70% located in the NTD and the rest in the CTD. Seventy percent of the mutations were random, the other approximately 20% were alanine substitutions and the rest were the most frequently observed mutations [9,11,14,16,35,36,37,38,39]. As the previous studies did not always use the same experimental methods to determine viral infectivity, we used our own threshold for separating infectious and non-infectious mutations. The mutant viruses were considered non-infectious when no viral production was observed or when the reported infectivity was lower than 1% of the wild type virus. Ninety-four (48%) of these mutations resulted in non-infectious viruses defined in this way. Forty of the 94 (42.5%) mutations that destroyed infectivity were not found in the database, while the remainder were found at least once. However, none were found in more than 0.2% (11 of 5811) of the sequences. While the database frequency of inactivating mutations ranged from 0% to 0.2%, the frequency of infectivity-conserving mutations ranged from 0% to 36%. Nineteen out of 90 of the latter mutations (~21%) had not been observed in the HIVDB, while 37 (41%) were present in more than 11 sequences (Figure 2). Overall, inactivating mutations appeared at a significantly lower frequency than tolerated mutations (p = 3.03 × 10−8; Mann–Whitney U test). Using mutation frequency as a predictor for the impact on mutant infectivity, the threshold of 0.2% yielded the best prediction accuracy (64%) (Table 1).


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)

Mutations observed in a database of 5811 HIV-1 capsid sequences. Mutations resulting in non-infectious or infectious viruses are shown separately and stratified based on their database frequencies. White boxes represent the percentage of mutations not found in the database.
© Copyright Policy
Related In: Results  -  Collection

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

viruses-07-02901-f002: Mutations observed in a database of 5811 HIV-1 capsid sequences. Mutations resulting in non-infectious or infectious viruses are shown separately and stratified based on their database frequencies. White boxes represent the percentage of mutations not found in the database.
Mentions: The observation that the majority of observed mutations were not predicted to alter protein stability, in contrast to unobserved mutations, suggests that any mutation found in the database has a high likelihood of being tolerated. To explore this question, we examined the frequency of 184 point mutations with known impact on infectivity in subtype B HIV-1. The mutated residues were scattered throughout the CA, with 70% located in the NTD and the rest in the CTD. Seventy percent of the mutations were random, the other approximately 20% were alanine substitutions and the rest were the most frequently observed mutations [9,11,14,16,35,36,37,38,39]. As the previous studies did not always use the same experimental methods to determine viral infectivity, we used our own threshold for separating infectious and non-infectious mutations. The mutant viruses were considered non-infectious when no viral production was observed or when the reported infectivity was lower than 1% of the wild type virus. Ninety-four (48%) of these mutations resulted in non-infectious viruses defined in this way. Forty of the 94 (42.5%) mutations that destroyed infectivity were not found in the database, while the remainder were found at least once. However, none were found in more than 0.2% (11 of 5811) of the sequences. While the database frequency of inactivating mutations ranged from 0% to 0.2%, the frequency of infectivity-conserving mutations ranged from 0% to 36%. Nineteen out of 90 of the latter mutations (~21%) had not been observed in the HIVDB, while 37 (41%) were present in more than 11 sequences (Figure 2). Overall, inactivating mutations appeared at a significantly lower frequency than tolerated mutations (p = 3.03 × 10−8; Mann–Whitney U test). Using mutation frequency as a predictor for the impact on mutant infectivity, the threshold of 0.2% yielded the best prediction accuracy (64%) (Table 1).

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