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Ambiguity in genetic codes exists in cases where certain stop codons are alternatively used to encode non-canonical amino acids. In selenoprotein transcripts, the UGA codon may either represent a translation termination signal or a selenocysteine (Sec) codon. Translating UGA to Sec requires selenium and specialized Sec incorporation machinery such as the interaction between the SECIS element and SBP2 protein, but how these factors quantitatively affect alternative assignments of UGA has not been fully investigated. We developed a model simulating the UGA decoding process. Our model is based on the following assumptions: (1) charged Sec-specific tRNAs (Sec-tRNASec) and release factors compete for a UGA site, (2) Sec-tRNASec abundance is limited by the concentrations of selenium and Sec-specific tRNA (tRNASec) precursors, and (3) all synthesis reactions follow first-order kinetics. We demonstrated that this model captured two prominent characteristics observed from experimental data. First, UGA to Sec decoding increases with elevated selenium availability, but saturates under high selenium supply. Second, the efficiency of Sec incorporation is reduced with increasing selenoprotein synthesis. We measured the expressions of four selenoprotein constructs and estimated their model parameters. Their inferred Sec incorporation efficiencies did not correlate well with their SECIS-SBP2 binding affinities, suggesting the existence of additional factors determining the hierarchy of selenoprotein synthesis under selenium deficiency. This model provides a framework to systematically study the interplay of factors affecting the dual definitions of a genetic codon.

Domain architectures and catalytic functions of enzymes constitute the centerpieces of a metabolic network. These types of information are formulated as a two-layered network consisting of domains, proteins, and reactions-a domain-protein-reaction (DPR) network. We propose an algorithm to reconstruct the evolutionary history of DPR networks across multiple species and categorize the mechanisms of metabolic systems evolution in terms of network changes. The reconstructed history reveals distinct patterns of evolutionary mechanisms between prokaryotic and eukaryotic networks. Although the evolutionary mechanisms in early ancestors of prokaryotes and eukaryotes are quite similar, more novel and duplicated domain compositions with identical catalytic functions arise along the eukaryotic lineage. In contrast, prokaryotic enzymes become more versatile by catalyzing multiple reactions with similar chemical operations. Moreover, different metabolic pathways are enriched with distinct network evolution mechanisms. For instance, although the pathways of steroid biosynthesis, protein kinases, and glycosaminoglycan biosynthesis all constitute prominent features of animal-specific physiology, their evolution of domain architectures and catalytic functions follows distinct patterns. Steroid biosynthesis is enriched with reaction creations but retains a relatively conserved repertoire of domain compositions and proteins. Protein kinases retain conserved reactions but possess many novel domains and proteins. In contrast, glycosaminoglycan biosynthesis has high rates of reaction/protein creations and domain recruitments. Finally, we elicit and validate two general principles underlying the evolution of DPR networks: 1) duplicated enzyme proteins possess similar catalytic functions and 2) the majority of novel domains arise to catalyze novel reactions. These results shed new lights on the evolution of metabolic systems.

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level.

Protein termini are determinants of protein stability. Proteins bearing degradation signals, or degrons, at their amino‐ or carboxyl‐termini are eliminated by the N‐ or C‐degron pathways, respectively. We aimed to elucidate the function of C‐degron pathways and to unveil how normal proteomes are exempt from C‐degron pathway‐mediated destruction. Our data reveal that C‐degron pathways remove mislocalized cellular proteins and cleavage products of deubiquitinating enzymes. Furthermore, the C‐degron and N‐degron pathways cooperate in protein removal. Proteome analysis revealed a shortfall in normal proteins targeted by C‐degron pathways, but not of defective proteins, suggesting proteolysis‐based immunity as a constraint for protein evolution/selection. Our work highlights the importance of protein termini for protein quality surveillance, and the relationship between the functional proteome and protein degradation pathways. SYNOPSIS: Proteins with degradation signals (degron) at their amino‐ or carboxyl‐termini are eliminated by the N‐ or C‐degron pathways, respectively. Proteome‐wide analyses suggest functions of human C‐degrons signals in protein quality surveillance, as well as interplay with N‐degron‐dependent mechanisms. The Global Protein Stability (GPS) random peptide platform enables context‐independent characterization of degron features.The activity of C‐degrons is tuned by their surrounding sequences.Gly/C‐degron shortage is limited to functional eukaryotic proteomes accessible to C‐degron pathways.C‐degron pathways clear products of proteases and mislocalized cellular proteins.Mitochondrial localization of MIC19 is dually safeguarded by the N‐ and C‐degron pathways.Coronaviral diGly‐ending proteins evade C‐degron pathway‐mediated degradation. [ABSTRACT FROM AUTHOR]