scholarly journals High-Resolution, Multidimensional Phylogenetic Metrics Identify Class I Aminoacyl-tRNA Synthetase Evolutionary Mosaicity and Inter-modular Coupling

2021 ◽  
Author(s):  
Charles Carter ◽  
Alex Popinga ◽  
Remco Bouckaert ◽  
Peter R Wills
Keyword(s):  
Rna World ◽  
Trna Synthetase ◽  
Class I ◽  
Sequence Alignments ◽  
Gene Encoding ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Canonical Class ◽  
Genetic Coding ◽  
Molecular Tree

The provenance of the aminoacyl-tRNA synthetases (aaRS) poses challenging questions because of their role in the emergence and evolution of genetic coding. We investigate evidence about their ancestry from curated structure-based multiple sequence alignments of a structurally invariant “scaffold” shared by all 10 canonical Class I aaRS. Three uncorrelated phylogenetic metrics—residue-by-residue conservation, its variance, and row-by-row cladistic congruence—imply that the Class I scaffold is a mosaic assembled from distinct, successive genetic sources. These data are especially significant in light of: (i) experimental fragmentations of the Class I scaffold into three partitions that retain catalytic activities in proportion to their length; and (ii) evidence that two of these partitions arose from an ancestral Class I aaRS gene encoding a Class II ancestor in frame on the opposite strand. Phylogenetic metrics of different modules vary in accordance with their presumed functionality. A 46-residue Class I “protozyme” roots the Class I molecular tree prior to the adaptive radiation of the Rossmann dinucleotide binding fold that refined substrate discrimination. Such rooting is consistent with near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved long after the genetic code had been implemented in an RNA world. Further, pinpointing discontinuous enhancements of aaRS fidelity establishes a timeline for the growth of coding from a binary amino acid alphabet.

scholarly journals High-Resolution, Multidimensional Phylogenetic Metrics Identify Class I Aminoacyl-tRNA Synthetase Evolutionary Mosaicity and Inter-modular ‘Coupling

2020 ◽  
Author(s):  
Charles W. Carter ◽  
Alex Popinga ◽  
Remco Bouckaert ◽  
Peter R. Wills
Keyword(s):  
Amino Acid ◽  
Trna Synthetase ◽  
Class I ◽  
Evolutionary Divergence ◽  
Aminoacyl Trna Synthetase ◽  
Insertion Element ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Residue Conservation ◽  
Genetic Coding

AbstractThe provenance of the aminoacyl-tRNA synthetases (aaRS) poses unusually challenging questions because of their role in the emergence and evolution of genetic coding. We investigate evidence about their ancestry from highly curated structure-based multiple sequence alignments of a small “scaffold” that is structurally invariant in all 10 canonical Class I aaRS. Statistically different values of two uncorrelated phylogenetic metrics—residue by residue conservation derived from Clustal and row-by-row cladistic congruence derived from BEAST2—suggest that the Class I scaffold is a mosaic assembled from distinct, successive genetic sources. These data are especially significant in light of: (i) experimental fragmentations of the Class I scaffold into three partitions that retain catalytic activities in proportion to their length; and (ii) multiple sources of evidence that two of these partitions arose from an ancestral Class I aaRS gene encoding a Class II ancestor in frame on the opposite strand. Two additional metrics output by BEAST2 vary in accordance with the presumed functionality endowed by the various modules. The new evidence supplements previous aaRS phylogenies. It identifies a previously characterized 46-residue Class I “protozyme” as preceding the adaptive radiation of the superfamily containing variations of the Rossmann dinucleotide binding fold related to amino acid discrimination, and thus as root of that molecular tree. Such a rooting is consistent with near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved long after the genetic code had been implemented in an RNA world. Further, it establishes a timeline for the growth of coding from a binary amino acid alphabet by pinpointing discontinuous enhancements of aaRS fidelity.Author SummaryPhylogenetic analysis uncovers evolutionary connections between different protein superfamily members. We describe complementary, uncorrelated, phylogenetic metrics that support multiple evolutionary histories for different segments within members of the Class I aminoacyl-tRNA synthetase superfamily. Using a carefully curated 3D crystal structure superposition as the primary source of the multiple sequence alignment substantially reduced dependence of these metrics on empirical amino acid substitution matrices. Two metrics are derived from the amino acid distribution observed in each successive position. A third depends on how individual sequences distribute into phylogenetic tree branches for each of the ten amino acids activated by the superfamily. All metrics confirm that a segment previously identified as an inserted element is, indeed, a more recent acquisition, despite its structural conservation. The residue-by-residue conservation metrics reveal significant co-variation of mutational frequencies between a core segment that forms the amino acid binding site and a neighboring segment derived from the more recent insertion element. We attribute that covariation to the differentiation of superfamily members as evolutionary divergence enhanced amino acid specificity. Finally, evidence that the insertion element is a recent acquisition implies a new branching order for much of the proteome.

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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