Mutational analysis of the major soybean UreF paralogue involved in urease activation

The soybean genome duplicated ∼14 and 45 million years ago and has many paralogous genes, including those in urease activation (emplacement of Ni and CO2 in the active site). Activation requires the UreD and UreF proteins, each encoded by two paralogues. UreG, a third essential activation protein, is encoded by the single-copy Eu3, and eu3 mutants lack activity of both urease isozymes. eu2 has the same urease-negative phenotype, consistent with Eu2 being a single-copy gene, possibly encoding a Ni carrier. Unexpectedly, two eu2 alleles co-segregated with missense mutations in the chromosome 2 UreF paralogue (Ch02UreF), suggesting lack of expression/function of Ch14UreF. However, Ch02UreF and Ch14UreF transcripts accumulate at the same level. Further, it had been shown that expression of the Ch14UreF ORF complemented a fungal ureF mutant. A third, nonsense (Q2*) allelic mutant, eu2-c, exhibited 5- to 10-fold more residual urease activity than missense eu2-a or eu2-b, though eu2-c should lack all Ch02UreF protein. It is hypothesized that low-level activation by Ch14UreF is ‘spoiled’ by the altered missense Ch02UreF proteins (‘epistatic dominant-negative’). In agreement with active ‘spoiling’ by eu2-b-encoded Ch02UreF (G31D), eu2-b/eu2-c heterozygotes had less than half the urease activity of eu2-c/eu2-c siblings. Ch02UreF (G31D) could spoil activation by Chr14UreF because of higher affinity for the activation complex, or because Ch02UreF (G31D) is more abundant than Ch14UreF. Here, the latter is favoured, consistent with a reported in-frame AUG in the 5' leader of Chr14UreF transcript. Translational inhibition could represent a form of ‘functional divergence’ of duplicated genes

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Evolution of enhanced innate immune evasion by SARS-CoV-2

Emergence of SARS-CoV-2 variants of concern (VOCs) suggests viral adaptation to enhance human-to-human transmission1,2. Although much effort has focused on characterisation of spike changes in VOCs, mutations outside spike likely contribute to adaptation. Here we used unbiased abundance proteomics, phosphoproteomics, RNAseq and viral replication assays to show that isolates of the Alpha (B.1.1.7) variant3 more effectively suppress innate immune responses in airway epithelial cells, compared to first wave isolates. We found that Alpha has dramatically increased subgenomic RNA and protein levels of N, Orf9b and Orf6, all known innate immune antagonists. Expression of Orf9b alone suppressed the innate immune response through interaction with TOM70, a mitochondrial protein required for RNA sensing adaptor MAVS activation. Moreover, the activity of Orf9b and its association with TOM70 was regulated by phosphorylation. We propose that more effective innate immune suppression, through enhanced expression of specific viral antagonist proteins, increases the likelihood of successful Alpha transmission, and may increase in vivo replication and duration of infection4. The importance of mutations outside Spike in adaptation of SARS-CoV-2 to humans is underscored by the observation that similar mutations exist in the Delta and Omicron N/Orf9b regulatory regions

Impact of SARS-CoV-2 ORF6 and its variant polymorphisms on host responses and viral pathogenesis

: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes several proteins that inhibit host interferon responses. Among these, ORF6 antagonizes interferon signaling by disrupting nucleocytoplasmic trafficking through interactions with the nuclear pore complex components Nup98-Rae1. However, the roles and contributions of ORF6 during physiological infection remain unexplored. We assessed the role of ORF6 during infection using recombinant viruses carrying a deletion or loss-of-function (LoF) mutation in ORF6. ORF6 plays key roles in interferon antagonism and viral pathogenesis by interfering with nuclear import and specifically the translocation of IRF and STAT transcription factors. Additionally, ORF6 inhibits cellular mRNA export, resulting in the remodeling of the host cell proteome, and regulates viral protein expression. Interestingly, the ORF6:D61L mutation that emerged in the Omicron BA.2 and BA.4 variants exhibits reduced interactions with Nup98-Rae1 and consequently impairs immune evasion. Our findings highlight the role of ORF6 in antagonizing innate immunity and emphasize the importance of studying the immune evasion strategies of SARS-CoV-2

Combining specificity determining and conserved residues improves functional site prediction

<p>Abstract</p> <p>Background</p> <p>Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities.</p> <p>Results</p> <p>Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs), as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples.</p> <p>Conclusion</p> <p>The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.</p