Protein Sequence Variation Research Articles

Longitudinal variation in SARS-CoV-2 antibody levels and emergence of viral variants: a serological analysis

BackgroundSerological assays are being used to monitor antibody responses in individuals who had SARS-CoV-2 infection and those who received a COVID-19 vaccine. We aimed to determine whether such assays can predict neutralising antibody titres as antibody levels wane and viral variants emerge.MethodsWe measured antibody levels in serum samples from a cohort of 112 participants with SARS-CoV-2 infection using ten high-throughput serological tests and functional neutralisation assays. Serum samples were taken at baseline and at up to four subsequent visits. We assessed the effects of time and spike protein sequence variation on the performance and predictive value of the various assays. We did correlation analyses for individual timepoints using non-parametric Spearman correlation, and differences between timepoints were determined by use of a two-tailed Wilcoxon matched-pairs signed rank test.FindingsNeutralising antibody titres decreased over the first few months post-infection but stabilised thereafter, at about 30% of the level observed shortly after infection. Serological assays commonly used to measure antibodies against SARS-CoV-2 displayed a range of sensitivities that declined to varying extents over time. Quantitative measurements generated by serological assays based on the spike protein were better at predicting neutralising antibody titres than those based on nucleocapsid, but performance was variable, and manufacturer positivity thresholds were not able to predict the presence or absence of detectable neutralising activity. Although we observed some deterioration in correlation between serological measurements and functional neutralisation activity, some assays maintained an ability to predict neutralising titres, even against variants of concern.InterpretationThe ability of high-throughput serological assays to predict neutralising antibody titres is likely to be crucial for evaluation of immunity at the population scale. These data can facilitate the selection of the most suitable assays as surrogates of functional neutralising activity and suggest that such measurements might be useful in clinical practice.FundingUS National Institutes of Health and National Health Service Research Scotland BioResource.

Human herpesvirus diversity is altered in HLA class I binding peptides

Herpesviruses are ubiquitous, genetically diverse DNA viruses, with long-term presence in humans associated with infrequent but significant pathology. Human leukocyte antigen (HLA) class I presents intracellularly derived peptide fragments from infected tissue cells to CD8+ T and natural killer cells, thereby directing antiviral immunity. Allotypes of highly polymorphic HLA class I are distinguished by their peptide binding repertoires. Because this HLA class I variation is a major determinant of herpesvirus disease, we examined if sequence diversity of virus proteins reflects evasion of HLA presentation. Using population genomic data from Epstein–Barr virus (EBV), human cytomegalovirus (HCMV), and Varicella–Zoster virus, we tested whether diversity differed between the regions of herpesvirus proteins that can be recognized, or not, by HLA class I. Herpesviruses exhibit lytic and latent infection stages, with the latter better enabling immune evasion. Whereas HLA binding peptides of lytic proteins are conserved, we found that EBV and HCMV proteins expressed during latency have increased peptide sequence diversity. Similarly, latent, but not lytic, herpesvirus proteins have greater population structure in HLA binding than nonbinding peptides. Finally, we found patterns consistent with EBV adaption to the local HLA environment, with less efficient recognition of EBV isolates by high-frequency HLA class I allotypes. Here, the frequency of CD8+ T cell epitopes inversely correlated with the frequency of HLA class I recognition. Previous analyses have shown that pathogen-mediated natural selection maintains exceptional polymorphism in HLA residues that determine peptide recognition. Here, we show that HLA class I peptide recognition impacts diversity of globally widespread pathogens.

PSIII-12 Identification and Classification of Novel Animal Associated Viruses from the Human Skin Virome of Pet Owners and Agricultural Workers

Abstract Viruses are the most abundant biological entity on earth. Despite this, viral diversity in current reference databases is poorly described. With limited number of studies investigating environmental viromes, our understanding of the virosphere composition and diversity is limited. Improvement in metagenomic sequencing methodologies has allowed for identification of novel genomic sequences, especially for new and emerging viral taxa, and improvement in existing ambiguous viral phylogenetic groupings. Here we describe a viral discovery-based study that evaluated viral metagenomes collected from the hands and scalps of 42 human subjects (n = 642) for identification of environmentally contracted DNA viruses. Due to direct interaction between humans and animals either through owning pets or through vocation, it is hypothesized that surveillance of the human skin virome will contain animal associated viruses contracted through environmental contact that can then potentially become zoonotic pathogens. To investigate this, viral genome assemblies from the metagenomes were bioinformatically predicted using model and reference-based tools. A gene and protein sequence variation and binning approach was then employed to resolve viral dark matter. Evaluation of animal associated viruses resulted in the identification of a feline papillomavirus from a subject who reportedly owned a cat. Phylogenetic analysis suggests this isolate is related to feline papillomavirus type 2 sharing approximately 75% sequence synteny thus making it a novel papillomavirus isolate. Additionally, a new grouping of porcine associated Cressdnaviricota viruses were identified through protein sequence similarity network clustering of conserved Rep genes and all other NCBI reported Rep gene sequences. This grouping contained both novel viral genomes collected from the human skin virome dataset and unclassified Cressdnaviruses all collected from porcine samples obtained from NCBI. Further phylogenetic analysis showed similarity to this novel cluster to that of porcine circovirus (PCV). This discovery and phylogenetic based investigation of novel viral genome assemblies demonstrates that animal associated viruses can be identified from the skin of humans and thus studying human viromes can provide information into the spread and emergence of novel viruses.

Venom Gene Sequence Diversity and Expression Jointly Shape Diet Adaptation in Pitvipers.

Understanding the joint roles of protein sequence variation and differential expression during adaptive evolution is a fundamental, yet largely unrealized goal of evolutionary biology. Here, we use phylogenetic path analysis to analyze a comprehensive venom-gland transcriptome dataset spanning three genera of pitvipers to identify the functional genetic basis of a key adaptation (venom complexity) linked to diet breadth (DB). The analysis of gene-family-specific patterns reveals that, for genes encoding two of the most important venom proteins (snake venom metalloproteases and snake venom serine proteases), there are direct, positive relationships between sequence diversity (SD), expression diversity (ED), and increased DB. Further analysis of gene-family diversification for these proteins showed no constraint on how individual lineages achieved toxin gene SD in terms of the patterns of paralog diversification. In contrast, another major venom protein family (PLA2s) showed no relationship between venom molecular diversity and DB. Additional analyses suggest that other molecular mechanisms—such as higher absolute levels of expression—are responsible for diet adaptation involving these venom proteins. Broadly, our findings argue that functional diversity generated through sequence and expression variations jointly determine adaptation in the key components of pitviper venoms, which mediate complex molecular interactions between the snakes and their prey.

Sequence determinants of human-cell entry identified in ACE2-independent bat sarbecoviruses: A combined laboratory and computational network science approach.

SummaryBackgroundThe sarbecovirus subgenus of betacoronaviruses is widely distributed throughout bats and other mammals globally and includes human pathogens, SARS-CoV and SARS-CoV-2. The most studied sarbecoviruses use the host protein, ACE2, to infect cells. Curiously, the majority of sarbecoviruses identified to date do not use ACE2 and cannot readily acquire ACE2 binding through point mutations. We previously screened a broad panel of sarbecovirus spikes for cell entry and observed bat-derived viruses that could infect human cells, independent of ACE2. Here we further investigate the sequence determinants of cell entry for ACE2-independent bat sarbecoviruses.MethodsWe employed a network science-based approach to visualize sequence and entry phenotype similarities across the diversity of sarbecovirus spike protein sequences. We then verified these computational results and mapped determinants of viral entry into human cells using recombinant chimeric spike proteins within an established viral pseudotype assay.FindingsWe show ACE2-independent viruses that can infect human and bat cells in culture have a similar putative receptor binding motif, which can impart human cell entry into other bat sarbecovirus spikes that cannot otherwise infect human cells. These sequence determinants of human cell entry map to a surface-exposed protrusion from the predicted bat sarbecovirus spike receptor binding domain structure.InterpretationOur findings provide further evidence of a group of bat-derived sarbecoviruses with zoonotic potential and demonstrate the utility in applying network science to phenotypic mapping and prediction.Funding statementThis work was supported by Washington State University and the Paul G. Allen School for Global Health.

Tandem repeats in precursor protein stabilize transcript levels and production levels of the fungal ribosomally synthesized and post-translationally modified peptide ustiloxin B

Ustiloxin B is a ribosomally synthesized and post-translationally modified peptide (RiPP) first reported in Ascomycetes. Its biosynthetic pathway was recently identified in the filamentous fungus Aspergillus flavus. The precursor protein of ustiloxin B, UstA, has a signal peptide to the endoplasmic reticulum at its N-terminal and a subsequent tandemly highly repeated segment cleaved at Lys-Arg dipeptides by Kex2 protease; such proteins are called Kex2-processed repeat proteins (KEPs). RiPP biosynthetic pathways using KEPs as precursor proteins are widely distributed in the Fungi kingdom, with high diversity of precursor protein sequences. UstA in A. flavus has a 16-fold tandemly repeated segment containing the core peptide Tyr-Ala-Ile-Gly, which forms the ustiloxin B backbone structure, but it is unknown why such a costly-to-maintain highly repeated sequence is retained. Here, we replaced ustA, the gene encoding the ustiloxin B precursor protein, with synthetic genes encoding 1-, 3-, 5-, 7-, and 11-fold tandem-repeat segments in A. flavus, to investigate the relationship between the repeat number and ustiloxin B production. Ustiloxin B production increased quadratically with increasing repeat number in ustA variants, although it dropped in a previously constructed ustA variant that had a substituted synthetic gene encoding a 16-fold repeat segment probably because of the presence of the many rare codons in the sequence. We also examined the transcript levels of substituted synthetic genes in ustA variants, and surprisingly we found that the transcript levels of the synthetic genes increased linearly with increasing repeat number. This result implies that an unknown mechanism stabilizes ustA transcripts via the highly repeated structure in a feedback manner. We also constructed a transformant without the intron in native ustA, but no effect of intron removal was observed on either ustiloxin B production or the precursor gene transcript level. The costly-to-maintain highly repeated sequence in KEPs probably serves the purpose of maintaining stable transcripts and thus increasing the amount of substrate.

Crystal Structures of Wolbachia CidA and CidB Reveal Determinants of Bacteria-induced Cytoplasmic Incompatibility and Rescue

Cytoplasmic incompatibility (CI) results when Wolbachia bacteria-infected male insects mate with uninfected females, leading to embryonic lethality. “Rescue” of viability occurs if the female harbors the same Wolbachia strain. CI is caused by linked pairs of Wolbachia genes called CI factors (CifA and CifB). The co-evolution of CifA-CifB pairs may account in part for the incompatibility patterns documented in insects infected with different Wolbachia strains, but the molecular mechanisms remain elusive. Here, we use X-ray crystallography and AlphaFold to analyze the CI factors from Wolbachia strain wMel called CidAwMel and CidBwMel. Substituting CidAwMel interface residues with those from CidAwPip (from strain wPip) enables the mutant protein to bind CidBwPip and rescue CidBwPip-induced yeast growth defects, supporting the importance of CifA-CifB interaction in CI rescue. Sequence divergence in CidAwPip and CidBwPip proteins affects their pairwise interactions, which may help explain the complex incompatibility patterns of mosquitoes infected with different wPip strains.

Structural and biochemical analyses of selectivity determinants in chimeric Streptococcus Class A sortase enzymes.

Sequence variation in related proteins is an important characteristic that modulates activity and selectivity. An example of a protein family with a large degree of sequence variation is that of bacterial sortases, which are cysteine transpeptidases on the surface of gram-positive bacteria. Class A sortases are responsible for attachment of diverse proteins to the cell wall to facilitate environmental adaption and interaction. These enzymes are also used in protein engineering applications for sortase-mediated ligations (SML) or sortagging of protein targets. We previously investigated SrtA from Streptococcus pneumoniae, identifying a number of putative β7-β8 loop-mediated interactions that affected in vitro enzyme function. We identified residues that contributed to the ability of S. pneumoniae SrtA to recognize several amino acids at the P1' position of the substrate motif, underlined in LPXTG, in contrast to the strict P1' Gly recognition of SrtA from Staphylococcus aureus. However, motivated by the lack of a structural model for the active, monomeric form of S. pneumoniae SrtA, here, we expanded our studies to other Streptococcus SrtA proteins. We solved the first monomeric structure of S. agalactiae SrtA which includes the C-terminus, and three others of β7-β8 loop chimeras from S. pyogenes and S. agalactiae SrtA. These structures and accompanying biochemical data support our previously identified β7-β8 loop-mediated interactions and provide additional insight into their role in Class A sortase substrate selectivity. A greater understanding of individual SrtA sequence and structural determinants of target selectivity may also facilitate the design or discovery of improved sortagging tools.

Characterisation and Bioactivity Analysis of Peridinin-Chlorophyll a-Protein (PCP) Isolated from Symbiodinium tridacnidorum CS-73

Peridinin-Chlorophyll a-Proteins (PCP) are the major light harvesting proteins in photosynthetic dinoflagellates. PCP shows great variation in protein length, pigment ratio, sequence, and spectroscopic properties. PCP conjugates (PerCP) are widely used as fluorescent probes for cellular and tissue analysis in the biomedical field. PCP consists of a peridinin carotenoid; thereby, it can potentially be used as a bioactive compound in pharmaceutical applications. However, the biological activities of PCP are yet to be explored. In this study, we extracted, purified, and partially characterised the PCP from Symbiodinium tridacnidorum (CS-73) and explored its antioxidant, anti-cancer and anti-inflammation bioactivities. The PCP was purified using an ÄKTA™ PURE system and predicted to be of 17.3 kDa molecular weight (confirmed as a single band on SDS-PAGE) with an isoelectric point (pI) 5.6. LC-MS/MS and bioinformatic analysis of purified PCP digested with trypsin indicated it was 164 amino acids long with >90% sequence similarity to PCP of SymA3.s6014_g3 (belonging to clade A of Symbiodinium sp.) confirmed with 59 peptide combinations matched across its protein sequence. The spectroscopic properties of purified PCP showed a slight shift in absorption and emission spectra to previously documented analysis in Symbiodinium species possibly due to variation in amino acid sequences that interact with chl a and peridinin. Purified PCP consisted of a 19-amino-acid-long signal peptide at its N terminal and nine helixes in its secondary structure, with several protein binding sites and no DNA/RNA binding site. Furthermore, purified PCP exhibited antioxidant and in vitro anti-inflammation bioactivities, and anti-cancer activities against human metastatic breast adenocarcinoma (MDA-MB-231) and human colorectal (HTC-15) cancer cell lines. Together, all these findings present PCP as a promising candidate for continued investigations for pharmaceutical applications to cure chronic diseases, apart from its existing application as a fluorescent-probe.

Prioritization of human well-being spectrum related GWAS-SNVs using ENCODE-based web-tools predict interplay between PSMC3, ITIH4, and SERPINC1 genes in modulating well-being

Several traits related to positive and negative affect show a high genetic as well as phenotypic correlation with well-being in humans, and are therefore collectively termed as “Well-being spectrum”. Genome-Wide Association studies (GWA studies) on “well-being measurement” have led to identification of several genomic variants (Single Nucleotide Variants - SNVs), but very little has been explained with respect to their functionality and mode of alteration of well-being. Utilizing a pool of 1258 GWA studies based SNVs on “well-being measurement”, we prioritized the SNVs and tried to annotate well-being related functionality through several bioinformatic tools to predict whether a protein sequence variation affects protein function, as well as experimentally validated datasets available in ENCODE based web-tools namely rSNPBase, RegulomeDB, Haploreg, along with GTEx Portal and STRING based protein interaction networks. Prioritization yielded three key SNVs; rs3781627-A, rs13072536-T and 5877-C potentially regulating three genes, PSMC3, ITIH4 and SERPINC1, respectively. Interestingly, the genes showed well clustered protein-protein interaction (maximum combined confidence score >0.4) with other well-being candidate genes, namely TNF and CRP genes suggesting their important role in modulation of well-being. PSMC3 and ITIH4 genes are also involved in driving acute phase responses signifying a probable cross-talk between well-being and psychoneuroimmunological system. To best of our knowledge this study is the first of its kind where the well-being associated GWA studies-SNVs were prioritized and functionally annotated, majorly based on functional data available in public domain, which revealed PSMC3, ITIH4 and SERPINC1 genes as probable candidates in regulation of well-being spectrum.

Underlying selection for the diversity of spike protein sequences of SARS-CoV-2.

The global spread of SARS‐CoV‐2 is fast moving and has caused a worldwide public health crisis. In the present article, we analyzed spike protein sequences of SARS‐CoV‐2 genomes to assess the impact of mutational diversity. We observed from amino acid usage patterns that spike proteins are associated with a diversity of mutational changes and most important underlying cause of variation of amino acid usage is the changes in hydrophobicity of spike proteins. The changing patterns of hydrophobicity of spike proteins over time and its influence on the receptor binding affinity provides crucial information on the SARS‐CoV‐2 interaction with human receptor. Our results also show that spike proteins have evolved to prefer more hydrophobic residues over time. The present study provides a comprehensive analysis of molecular sequence data to consider that mutational variants might play a crucial role in modulating the virulence and spread of the virus and has immediate implications for therapeutic strategies.

Computational detection, analysis and interpretations of genomic variants in human diseases associated GENEMDM 2

Most of the mutations described in human MDM2 are tolerated without significantly disrupting the corresponding structural or molecular function. However, some of them are associated with a variety of human diseases, including cancer. Numerous computational methods have been developed to predict the effects of missense single nucleotide variants (SNVs). The non-synonymous single nucleotide polymorphisms affect the function of XRCC1, which impairs the ability to repair DNA and therefore increases the risk of diseases such as cancer. In this study, sequence and structure-based computational tools were used to screen the total listed coding SNPs of the MDM2 gene in order to recognize and describe them. The potential 6 ns SNP of MDM2 were identified from 29 ns SNP by consistent analysis using computational tools PolyPhen 2, SIFT, PANTHER and cSNP. The computational methods were used to systematically classify functional mutations in the regulatory and coding regions that modify the expression and function of the MDM2 enzyme. The HOPE project also made it possible to elaborate the structural effects of the substitutions of amino acids. In silico analysis predicted that rs759244097 is harmful. This study concluded that identifying this SNP will help to determine an individual's cancer susceptibility, prognosis and further treatment. Furthermore, current high-throughput sequencing efforts and the need for extensive interpretation of protein sequence variants requires more efficient and accurate computational methods in the coming years.

The landscape of coding RNA editing events in pediatric cancer

BackgroundRNA editing leads to post-transcriptional variation in protein sequences and has important biological implications. We sought to elucidate the landscape of RNA editing events across pediatric cancers.MethodsUsing RNA-Seq data mapped by a pipeline designed to minimize mapping ambiguity, we investigated RNA editing in 711 pediatric cancers from the St. Jude/Washington University Pediatric Cancer Genome Project focusing on coding variants which can potentially increase protein sequence diversity. We combined de novo detection using paired tumor DNA-RNA data with analysis of known RNA editing sites.ResultsWe identified 722 unique RNA editing sites in coding regions across pediatric cancers, 70% of which were nonsynonymous recoding variants. Nearly all editing sites represented the canonical A-to-I (n = 706) or C-to-U sites (n = 14). RNA editing was enriched in brain tumors compared to other cancers, including editing of glutamate receptors and ion channels involved in neurotransmitter signaling. RNA editing profiles of each pediatric cancer subtype resembled those of the corresponding normal tissue profiled by the Genotype-Tissue Expression (GTEx) project.ConclusionsIn this first comprehensive analysis of RNA editing events in pediatric cancer, we found that the RNA editing profile of each cancer subtype is similar to its normal tissue of origin. Tumor-specific RNA editing events were not identified indicating that successful immunotherapeutic targeting of RNA-edited peptides in pediatric cancer should rely on increased antigen presentation on tumor cells compared to normal but not on tumor-specific RNA editing per se.

Data-independent acquisition mass spectrometry for site-specific glycoproteomics characterization of SARS-CoV-2 spike protein.

The spike protein of SARS-CoV-2, the virus responsible for the global pandemic of COVID-19, is an abundant, heavily glycosylated surface protein that plays a key role in receptor binding and host cell fusion, and is the focus of all current vaccine development efforts. Variants of concern are now circulating worldwide that exhibit mutations in the spike protein. Protein sequence and glycosylation variations of the spike may affect viral fitness, antigenicity, and immune evasion. Global surveillance of the virus currently involves genome sequencing, but tracking emerging variants should include quantitative measurement of changes in site-specific glycosylation as well. In this work, we used data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry to quantitatively characterize the five N-linked glycosylation sites of the glycoprotein standard alpha-1-acid glycoprotein (AGP), as well as the 22 sites of the SARS-CoV-2 spike protein. We found that DIA compared favorably to DDA in sensitivity, resulting in more assignments of low-abundance glycopeptides. However, the reproducibility across replicates of DIA-identified glycopeptides was lower than that of DDA, possibly due to the difficulty of reliably assigning low-abundance glycopeptides confidently. The differences in the data acquired between the two methods suggest that DIA outperforms DDA in terms of glycoprotein coverage but that overall performance is a balance of sensitivity, selectivity, and statistical confidence in glycoproteomics. We assert that these analytical and bioinformatics methods for assigning and quantifying glycoforms would benefit the process of tracking viral variants as well as for vaccine development.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00216-021-03643-7.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants.

Simple SummaryWe reanalyzed a publicly available breast cancer proteomics dataset consisting of 122 human tumor samples using a scalable cloud computing workflow. By doing so, we were able to search these files against millions of known human sequence variants and hundreds of common post-translational protein modifications, thereby demonstrating the power of cloud computing to address proteomic data in a true biological context. We identified thousands of relevant sequence variants and PTMs, indicating that the original studies may have only scratched the surface of the true value of the CPTAC studies completed to date. We present the results of this reanalysis in a searchable web interface for community analysis and validation.The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Genome-wide identification and characterization of Pif genes in the Yesso scallop (Patinopecten yessoensis), and their expression patterns in response to Polydora shell infestation

Pif is an acid matrix protein that is known to be a key macromolecule in the shell nacre formation of molluscs. However, the investigation into the Pif gene at the whole genome level has seldom been carried out, limiting the systematic understanding of its evolution and function. The Yesso scallop (Patinopecten yessoensis), an ancient bivalve with important economic value and evolutionary status, has suffered severe shell infestation by Polydora that badly damaged shell structures. In this study, a total of eight Pif homologous genes (PyPif1–7 and PyBMSP) were identified in the whole genome of the Yesso scallop with diverse gene structures. Two conserved functional domains, VWA and chitin-binding domains, were detected in all PyPif proteins, although with variation in number of copies. Phylogenetic analysis of the Pifs among different molluscan species suggested the molecular evolution of Pif genes was consistent with taxonomic relationships. High sequence divergence of Pif proteins existed among the different species, but a high level of similarity was found in BMSPs, which own four VWA domains. High expression levels of PyBMSP in the central mantles of the healthy and Polydora shell infested Yesso scallops indicated its important role in shell formation. The other PyPifs (PyPif2, PyPif3, PyPif4, PyPif5, PyPif6) may function when the shell is injured, as significantly increased expression levels of these genes were found in the edge mantles of the infested scallops. In addition, haemocytes may also participate in shell repair by up-regulating the expression of PyPifs. This study provides new insights into the evolution and expression patterns of Pif genes in molluscs, which are valuable for elucidating the mechanism of shell repair and formation.

PyPEF-An Integrated Framework for Data-Driven Protein Engineering.

Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. PyPEF guides the identification and selection of beneficial proteins of a defined sequence space by systematically or randomly exploring the fitness of variants and by sampling random evolution pathways. The performance of PyPEF was evaluated concerning its predictive accuracy and throughput on four public protein and enzyme data sets using common regression models. It was proved that the program could efficiently predict the fitness of protein sequences for different target properties (predictive models with coefficient of determination values ranging from 0.58 to 0.92). By combining machine learning and protein evolution, PyPEF enabled the screening of proteins with various functions, reaching a screening capacity of more than 500,000 protein sequence variants in the timeframe of only a few minutes on a personal computer. PyPEF displayed significant accuracies on four public data sets (different proteins and properties) and underlined the potential of integrating data-driven technologies for covering different philosophies by either predicting the fitness of the variants to the highest accuracy accounting for epistatic effects or capturing the general trend of introduced mutations on the fitness in directed protein evolution campaigns. In essence, PyPEF can provide a powerful solution to current sequence exploration and combinatorial problems faced in protein engineering through exhaustive in silico screening of the sequence space.

A single m6A modification in U6 snRNA diversifies exon sequence at the 5\u2019 splice site

N6-methyladenosine (m6A) is a modification that plays pivotal roles in RNA metabolism and function, although its functions in spliceosomal U6 snRNA remain unknown. To elucidate its role, we conduct a large-scale transcriptome analysis of a Schizosaccharomyces pombe strain lacking this modification and found a global change of pre-mRNA splicing. The most significantly impacted introns are enriched for adenosine at the fourth position pairing the m6A in U6 snRNA, and exon sequences weakly recognized by U5 snRNA. This suggests cooperative recognition of 5’ splice site by U6 and U5 snRNPs, and also a role of m6A facilitating efficient recognition of the splice sites weakly interacting with U5 snRNA, indicating that U6 snRNA m6A relaxes the 5’ exon constraint and allows protein sequence diversity along with explosively increasing number of introns over the course of eukaryotic evolution.

A nanogel-based trivalent PspA nasal vaccine protects macaques from intratracheal challenge with pneumococci

Current polysaccharide-based pneumococcal vaccines are effective but not compatible with all serotypes of Streptococcus pneumoniae. We previously developed an adjuvant-free cationic nanogel nasal vaccine containing pneumococcal surface protein A (PspA), which is expressed on the surfaces of all pneumococcal serotypes. Here, to address the sequence diversity of PspA proteins, we formulated a cationic nanogel-based trivalent pneumococcal nasal vaccine and demonstrated the vaccine’s immunogenicity and protective efficacy in macaques by using a newly developed nasal spray device applicable to humans. Nasal vaccination of macaques with cationic cholesteryl pullulan nanogel (cCHP)-trivalent PspA vaccine effectively induced PspA-specific IgGs that bound to pneumococcal surfaces and triggered complement C3 deposition. The immunized macaques were protected from pneumococcal intratracheal challenge through both inhibition of lung inflammation and a dramatic reduction in the numbers of bacteria in the lungs. These results demonstrated that the cCHP-trivalent PspA vaccine is an effective candidate vaccine against pneumococcal infections.

Post-Translational Modification Networks of Contractile and Cellular Stress Response Proteins in Bladder Ischemia.

Molecular mechanisms underlying bladder dysfunction in ischemia, particularly at the protein and protein modification levels and downstream pathways, remain largely unknown. Here we describe a comparison of protein sequence variations in the ischemic and normal bladder tissues by measuring the mass differences of the coding amino acids and actual residues crossing the proteome. A large number of nonzero delta masses (11,056) were detected, spanning over 1295 protein residues. Clustering analysis identified 12 delta mass clusters that were significantly dysregulated, involving 30 upregulated (R2 > 0.5, ratio > 2, p < 0.05) and 33 downregulated (R2 > 0.5, ratio < −2, p < 0.05) proteins in bladder ischemia. These protein residues had different mass weights from those of the standard coding amino acids, suggesting the formation of non-coded amino acid (ncAA) residues in bladder ischemia. Pathway, gene ontology, and protein–protein interaction network analyses of these ischemia-associated delta-mass containing proteins indicated that ischemia provoked several amino acid variations, potentially post-translational modifications, in the contractile proteins and stress response molecules in the bladder. Accumulation of ncAAs may be a novel biomarker of smooth muscle dysfunction, with diagnostic potential for bladder dysfunction. Our data suggest that systematic assessment of global protein modifications may be crucial to the characterization of ischemic conditions in general and the pathomechanism of bladder dysfunction in ischemia.