scholarly journals A N7-guanine RNA cap methyltransferase signature-sequence as a genetic marker of large genome, non-mammalian Tobaniviridae

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
François Ferron ◽  
Humberto J Debat ◽  
Ashleigh Shannon ◽  
Etienne Decroly ◽  
Bruno Canard
Keyword(s):  
Active Site ◽  
Rna Viruses ◽  
Type I ◽  
Diverse Group ◽  
Genome Organisation ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Large Genome ◽  
Multiple Sequence Alignments ◽  
Signature Sequence

Abstract The order Nidovirales is a diverse group of (+)RNA viruses, classified together based on their common genome organisation and conserved replicative enzymes, despite drastic differences in size and complexity. One such difference pertains to the mechanisms and enzymes responsible for generation of the proposed viral 5′ RNA cap. Within the Coronaviridae family, two separate methytransferases (MTase), nsp14 and nsp16, perform the RNA-cap N7-guanine and 2′-OH methylation respectively for generation of the proposed m7GpppNm type I cap structure. For the majority of other families within the Nidovirales order, the presence, structure and key enzymes involved in 5′ capping are far less clear. These viruses either lack completely an RNA MTase signature sequence, or lack an N7-guanine methyltransferase signature sequence, obscuring our understanding about how RNA-caps are N7-methylated for these families. Here, we report the discovery of a putative Rossmann fold RNA methyltransferase in 10 Tobaniviridae members in Orf1a, an unusual genome locus for this gene. Multiple sequence alignments and structural analyses lead us to propose this novel gene as a typical RNA-cap N7-guanine MTase with substrate specificity and active-site organization similar to the canonical eukaryotic RNA-cap N7-guanine MTase.

scholarly journals Identification of aNidoviralesOrf1a N7-guanine cap Methyltransferase signature-sequence as a genetic marker of large genomeTobaniviridae

2019 ◽  
Author(s):  
François Ferron ◽  
Humberto Julio Debat ◽  
Etienne Decroly ◽  
Bruno Canard
Keyword(s):  
Active Site ◽  
Rna Virus ◽  
Salient Feature ◽  
Replication Machinery ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Mrna Transcripts ◽  
Virus World ◽  
Signature Sequence

AbstractMembers of theNidoviralesorder have (+)RNA genomes amongst the largest in size in the RNA virus world. Expression of their genes is promoted through reading of genomic RNA and mRNA transcripts by the ribosome of the infected cell. The 5’-end of these RNAs is supposedly protected by an RNA-cap structure (m7GpppNm) whose most synthesis steps remain elusive. In Eukaryotes, the RNA-cap structure is methylated by RNA methyltransferases (MTases) at the RNA-cap N7-guanine position as well as the 2’-O methyl position of the first transcribed nucleotide. InCoronaviridae, two separate enzymes (nsp14 and nsp16) perform the N7-guanine and the 2’-OH methylation, respectively. One salient feature of theNidoviralesN7-guanine MTase nsp14 is that it is the only example of non-Rossman fold viral MTase known so far. Conversely, all otherNidoviralesnsp16-like MTases have a canonical Rossman fold. ManyNidoviralesmembers lack either any RNA MTase signature sequence (e.g.,Arteriviridae), or lack a N7-guanine MTase signature sequence (e.g.,Tobaniviridae,Euroniviridae,Roniviridae,Medioniviridae). Both nsp14-and nsp16-like enzyme genes are usually located in Orf1b encoding for the replication machinery. Here, we report the discovery of a putative Rossman fold RNA MTase in the Orf1a of tenTobaniviridaemembers. Multiple sequence alignments and structural analyses identify this novel gene as a typical RNA-cap N7-guanine MTase with substrate specificity and active-site organization similar to the canonical eukaryotic RNA-cap N7-guanine MTase.

scholarly journals Positive natural selection in primate genes of the type I interferon response

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elena N. Judd ◽  
Alison R. Gilchrist ◽  
Nicholas R. Meyerson ◽  
Sara L. Sawyer
Keyword(s):  
Natural Selection ◽  
Positive Selection ◽  
Type I Interferon ◽  
Interferon Response ◽  
Type I ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Interferon Stimulated Genes ◽  
Interferon Induction

Abstract Background The Type I interferon response is an important first-line defense against viruses. In turn, viruses antagonize (i.e., degrade, mis-localize, etc.) many proteins in interferon pathways. Thus, hosts and viruses are locked in an evolutionary arms race for dominance of the Type I interferon pathway. As a result, many genes in interferon pathways have experienced positive natural selection in favor of new allelic forms that can better recognize viruses or escape viral antagonists. Here, we performed a holistic analysis of selective pressures acting on genes in the Type I interferon family. We initially hypothesized that the genes responsible for inducing the production of interferon would be antagonized more heavily by viruses than genes that are turned on as a result of interferon. Our logic was that viruses would have greater effect if they worked upstream of the production of interferon molecules because, once interferon is produced, hundreds of interferon-stimulated proteins would activate and the virus would need to counteract them one-by-one. Results We curated multiple sequence alignments of primate orthologs for 131 genes active in interferon production and signaling (herein, “induction” genes), 100 interferon-stimulated genes, and 100 randomly chosen genes. We analyzed each multiple sequence alignment for the signatures of recurrent positive selection. Counter to our hypothesis, we found the interferon-stimulated genes, and not interferon induction genes, are evolving significantly more rapidly than a random set of genes. Interferon induction genes evolve in a way that is indistinguishable from a matched set of random genes (22% and 18% of genes bear signatures of positive selection, respectively). In contrast, interferon-stimulated genes evolve differently, with 33% of genes evolving under positive selection and containing a significantly higher fraction of codons that have experienced selection for recurrent replacement of the encoded amino acid. Conclusion Viruses may antagonize individual products of the interferon response more often than trying to neutralize the system altogether.

scholarly journals DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Farhan Quadir ◽  
Raj S. Roy ◽  
Randal Halfmann ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Tertiary Structure ◽  
Protein Complexes ◽  
Complex Structure ◽  
Great Success ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Residue Contacts ◽  
Evolutionary Features

AbstractDeep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. We name this tool DNCON2_Inter. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of 22.9% for homodimers and 17.0% for higher-order homomultimers. In some instances, especially where interchain contact densities are high, DNCON2_Inter predicted interchain contacts with 100% precision. We also developed Con_Complex, a complex structure reconstruction tool that uses predicted contacts to produce the structure of the complex. Using Con_Complex, we show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

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