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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

Fitness profiling of PA influenza virus polymerase subunit.(A) Correlations of log10 relative frequency of individual point mutations between replicates are shown. Relative frequencymutationi = (Occurrence frequencymutationi)/(Occurrence frequencyWT) (B) Log10 RF indices for silent mutations, nonsense mutations, and missense mutations are shown as histograms. Point mutations located at the 5 terminal 400 bp and 3 terminal 400 bp regions are not included in this analysis to avoid complication by the vRNA packaging signal [93, 94]. (C) The locations of the PA C-terminal domain and the PA N-terminal domain are shown as white boxes. The locations of the mutated regions in each mutant library are shown as green boxes. Log10 RF indices for individual point mutations are plotted across the PA gene. Each point mutation is colored coded as in panel B. Purple: silent mutations; Cyan: nonsense mutations; Brown: missense mutations. A smooth curve was fitted by loess and plotted for each point mutation type.
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pgen.1005310.g002: Fitness profiling of PA influenza virus polymerase subunit.(A) Correlations of log10 relative frequency of individual point mutations between replicates are shown. Relative frequencymutationi = (Occurrence frequencymutationi)/(Occurrence frequencyWT) (B) Log10 RF indices for silent mutations, nonsense mutations, and missense mutations are shown as histograms. Point mutations located at the 5 terminal 400 bp and 3 terminal 400 bp regions are not included in this analysis to avoid complication by the vRNA packaging signal [93, 94]. (C) The locations of the PA C-terminal domain and the PA N-terminal domain are shown as white boxes. The locations of the mutated regions in each mutant library are shown as green boxes. Log10 RF indices for individual point mutations are plotted across the PA gene. Each point mutation is colored coded as in panel B. Purple: silent mutations; Cyan: nonsense mutations; Brown: missense mutations. A smooth curve was fitted by loess and plotted for each point mutation type.

Mentions: Comparison of the relative frequency of individual point mutations between replicates was performed to assess the reproducibility of our “small library” high-throughput genetic platform (see Materials and Methods for the calculation of relative frequency). A Pearson’s correlation of 0.95 was obtained for the technical replicate of DNA library, 0.76 for the biological replicate of transfection, and 0.96 for the biological replicate of infection (Fig 2A). The strong correlations between replicates validated the design of our high-throughput genetic platform. Only those point mutations with an occurrence frequency of ≥ 0.03% in the DNA library were included in the downstream analysis, which covered 42% of all possible point mutations on the PA gene, to avoid fitness calculations being obscured by sequencing errors. The relative fitness index (RF index) was used as a proxy to estimate the fitness effect for each point mutation.


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)

Fitness profiling of PA influenza virus polymerase subunit.(A) Correlations of log10 relative frequency of individual point mutations between replicates are shown. Relative frequencymutationi = (Occurrence frequencymutationi)/(Occurrence frequencyWT) (B) Log10 RF indices for silent mutations, nonsense mutations, and missense mutations are shown as histograms. Point mutations located at the 5 terminal 400 bp and 3 terminal 400 bp regions are not included in this analysis to avoid complication by the vRNA packaging signal [93, 94]. (C) The locations of the PA C-terminal domain and the PA N-terminal domain are shown as white boxes. The locations of the mutated regions in each mutant library are shown as green boxes. Log10 RF indices for individual point mutations are plotted across the PA gene. Each point mutation is colored coded as in panel B. Purple: silent mutations; Cyan: nonsense mutations; Brown: missense mutations. A smooth curve was fitted by loess and plotted for each point mutation type.
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pgen.1005310.g002: Fitness profiling of PA influenza virus polymerase subunit.(A) Correlations of log10 relative frequency of individual point mutations between replicates are shown. Relative frequencymutationi = (Occurrence frequencymutationi)/(Occurrence frequencyWT) (B) Log10 RF indices for silent mutations, nonsense mutations, and missense mutations are shown as histograms. Point mutations located at the 5 terminal 400 bp and 3 terminal 400 bp regions are not included in this analysis to avoid complication by the vRNA packaging signal [93, 94]. (C) The locations of the PA C-terminal domain and the PA N-terminal domain are shown as white boxes. The locations of the mutated regions in each mutant library are shown as green boxes. Log10 RF indices for individual point mutations are plotted across the PA gene. Each point mutation is colored coded as in panel B. Purple: silent mutations; Cyan: nonsense mutations; Brown: missense mutations. A smooth curve was fitted by loess and plotted for each point mutation type.
Mentions: Comparison of the relative frequency of individual point mutations between replicates was performed to assess the reproducibility of our “small library” high-throughput genetic platform (see Materials and Methods for the calculation of relative frequency). A Pearson’s correlation of 0.95 was obtained for the technical replicate of DNA library, 0.76 for the biological replicate of transfection, and 0.96 for the biological replicate of infection (Fig 2A). The strong correlations between replicates validated the design of our high-throughput genetic platform. Only those point mutations with an occurrence frequency of ≥ 0.03% in the DNA library were included in the downstream analysis, which covered 42% of all possible point mutations on the PA gene, to avoid fitness calculations being obscured by sequencing errors. The relative fitness index (RF index) was used as a proxy to estimate the fitness effect for each point mutation.

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