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Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality.

Wu NC, Olson CA, Du Y, Le S, Tran K, Remenyi R, Gong D, Al-Mawsawi LQ, Qi H, Wu TT, Sun R - PLoS Genet. (2015)

Bottom Line: We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types.Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone.More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America,; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

No MeSH data available.


Related in: MedlinePlus

Systematic identification of functional residues.(A) Predicted ΔΔG for each point mutation is plotted against the log10 RF index. The horizontal green line represents the RF index cutoff used in this study, RF index = 0.15. For the N-terminal domain, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.20 (P = 1.3e−4). For the C-terminal, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.18 (P = 6.8e−10). (B) The distributions of relative SASA are shown for residues that carried at least one substitutions of interest (RF index < 0.15 and a predicted ΔΔG < 0) and for residues that did not carry any substitutions of interest. (C) This analysis is performed on those solvent exposed residues (relative SASA > 0.2) that carried a deleterious mutation (RF index < 0.15). The pie chart is showing the fraction of residues that carried a substitution of interest (ΔΔG < 0) and those did not (ΔΔG ≥ 0).
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pgen.1005310.g003: Systematic identification of functional residues.(A) Predicted ΔΔG for each point mutation is plotted against the log10 RF index. The horizontal green line represents the RF index cutoff used in this study, RF index = 0.15. For the N-terminal domain, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.20 (P = 1.3e−4). For the C-terminal, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.18 (P = 6.8e−10). (B) The distributions of relative SASA are shown for residues that carried at least one substitutions of interest (RF index < 0.15 and a predicted ΔΔG < 0) and for residues that did not carry any substitutions of interest. (C) This analysis is performed on those solvent exposed residues (relative SASA > 0.2) that carried a deleterious mutation (RF index < 0.15). The pie chart is showing the fraction of residues that carried a substitution of interest (ΔΔG < 0) and those did not (ΔΔG ≥ 0).

Mentions: Using Rosetta software we predicted the effect of individual substitutions on protein stability. We used the parameters from row 16 of Table I in Kellogg et al., which has been shown to give a correlation of 0.69 with experimental data and a stability-classification accuracy of 0.72 [59, 60]. We were able to identify substitutions that had a low RF index, but did not destabilize the protein (Fig 3A). We hypothesized that these residues had large functional constraints with little structural effects to the protein upon substitution. To identify the substitutions of interest, a cutoff was set at an RF index < 0.15 (based on the separation point of silent mutations and nonsense mutations) and a predicted ΔΔG < 0 (not destabilizing). A total of 32 substitutions (22 unique residues) in the PA N-terminal domain and 110 substitutions (81 unique residues) in the PA C-terminal domain satisfied these criteria.


Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality.

Wu NC, Olson CA, Du Y, Le S, Tran K, Remenyi R, Gong D, Al-Mawsawi LQ, Qi H, Wu TT, Sun R - PLoS Genet. (2015)

Systematic identification of functional residues.(A) Predicted ΔΔG for each point mutation is plotted against the log10 RF index. The horizontal green line represents the RF index cutoff used in this study, RF index = 0.15. For the N-terminal domain, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.20 (P = 1.3e−4). For the C-terminal, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.18 (P = 6.8e−10). (B) The distributions of relative SASA are shown for residues that carried at least one substitutions of interest (RF index < 0.15 and a predicted ΔΔG < 0) and for residues that did not carry any substitutions of interest. (C) This analysis is performed on those solvent exposed residues (relative SASA > 0.2) that carried a deleterious mutation (RF index < 0.15). The pie chart is showing the fraction of residues that carried a substitution of interest (ΔΔG < 0) and those did not (ΔΔG ≥ 0).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4489113&req=5

pgen.1005310.g003: Systematic identification of functional residues.(A) Predicted ΔΔG for each point mutation is plotted against the log10 RF index. The horizontal green line represents the RF index cutoff used in this study, RF index = 0.15. For the N-terminal domain, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.20 (P = 1.3e−4). For the C-terminal, the Spearman’s rank correlation between log10 RF index and Predicted ΔΔG is -0.18 (P = 6.8e−10). (B) The distributions of relative SASA are shown for residues that carried at least one substitutions of interest (RF index < 0.15 and a predicted ΔΔG < 0) and for residues that did not carry any substitutions of interest. (C) This analysis is performed on those solvent exposed residues (relative SASA > 0.2) that carried a deleterious mutation (RF index < 0.15). The pie chart is showing the fraction of residues that carried a substitution of interest (ΔΔG < 0) and those did not (ΔΔG ≥ 0).
Mentions: Using Rosetta software we predicted the effect of individual substitutions on protein stability. We used the parameters from row 16 of Table I in Kellogg et al., which has been shown to give a correlation of 0.69 with experimental data and a stability-classification accuracy of 0.72 [59, 60]. We were able to identify substitutions that had a low RF index, but did not destabilize the protein (Fig 3A). We hypothesized that these residues had large functional constraints with little structural effects to the protein upon substitution. To identify the substitutions of interest, a cutoff was set at an RF index < 0.15 (based on the separation point of silent mutations and nonsense mutations) and a predicted ΔΔG < 0 (not destabilizing). A total of 32 substitutions (22 unique residues) in the PA N-terminal domain and 110 substitutions (81 unique residues) in the PA C-terminal domain satisfied these criteria.

Bottom Line: We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types.Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone.More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America,; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

No MeSH data available.


Related in: MedlinePlus