UniProtKB Database Research Articles

Analysis of the lake trout heart, blood, liver, and brain proteome using evolutionary proteomics

IntroductionSalvelinus namaycush (lake trout) is a top‐predator fish in the Great Lakes region. The Great Lakes Fish Monitoring and Surveillance Program (GLFMSP) use lake trout as a bioindicator of chemical stress by monitoring the concentrations of persistent, bioaccumulative and toxic (PBT) chemicals in their bodies. Studies have shown that elevated concentrations of PBTs can cause changes in transcribed genes, translated mRNAs, proteins produced, and post‐translational modifications in aquatic species. Though lake trout is used to monitor chemical concentrations for over 50 years, there is very little information available on the proteome of this species. A well‐developed protein database for the lake trout would help elucidate the effects of PBT chemicals on this species providing a direct health metric for the Great Lakes ecosystem.MethodsIn this study, heart, blood, brain, and liver samples from lake trout were analyzed by SDS‐PAGE, followed by in‐gel trypsin digestion and analysis by nanoLC‐MS/MS. The raw data was searched against different NCBI and UniProtKB databases in Mascot Daemon and the output was analyzed by Scaffold 4.3 software. Databases used include Actinopterygii, Salmonidae, Salvelinus, as well as the highly studied species Oncorhynchus mykissand Danio rerio.Preliminary DataPreviously, our group has reported a large number of proteins for lake trout liver, heart and blood (Dupree et al., Proteomics, 2019, PMID: 31578773 and Dupree et al., Proteomics, 2021, PMID: 34676671). In lake trout liver we identified 4194 proteins using the NCBI databases and 3811 potential protein hits in the UniProtKB databases. In the blood and heart we identified 838 and 580 proteins, respectively, using NCBI databases and 1180 and 561 potential protein hits for the heart and blood, respectively in BR2. A lake trout brain proteomics analysis completed using an in‐house SDS‐PAGE, followed by in gel digestion and nanoLC‐MS/MS analysis. The Mascot database search is underway. Once protein hits are cataloged, BLAST and Scaffold‐based comparisons of the liver, heart, blood and brain will be performed. Additional search of the data against the other fish relatives will also be completed. Through this process, evolutionary relationships for the lake trout species will also be explored. The current study will add to our understanding of the lake trout proteome that will be used to understand the biochemical/physiological effects of legacy PBT chemicals in the Great Lakes ecosystem.Novel AspectUse of four types of lake trout tissue together to develop a protein database for the species.

Reduced protein sequence patterns in identifying key structural elements of dissimilatory sulfite reductase homologs

Methanogenic archaea carry homologs of dissimilatory sulfite reductase (Dsr), called Dsr Like proteins (DsrLP). Dsr reduces sulfite to sulfide, a key step in an Earth’s ancient metabolic process called dissimilatory sulfate reduction. The DsrLPs do not function as Dsr, and a computational approach is needed to develop hypotheses for guiding wet bench investigations on DsrLP’s function. To make the computational analysis process efficient, the DsrLP amino acid sequences were transformed using only eight alphabets functionally representing twenty amino acids. The resultant reduced amino acid sequences were analyzed to identify conserved signature patterns in DsrLPs. Many of these patterns mapped on critical structural elements of Dsr and some were associated tightly with particular DsrLP groups. A search into the UniProtKB database identified several proteins carrying DsrLP’s signature patterns; cysteine desulfurase, nucleosidase, and uroporphyrinogen III methylase were such matches. These outcomes provided clues to the functions of DsrLPs and highlighted the utility of the computational approach used.

Wei2GO: weighted sequence similarity-based protein function prediction.

BackgroundProtein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases.MethodsThis paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations.ResultsWei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes.AvailabilityWei2GO is written in Python 3, and can be found at https://gitlab.com/mreijnders/Wei2GO.

Comparison stability Rusticyanin 23270 wild-type and mutant His143Leu using molecular dynamics simulation

The Acidithiobacillus ferroxidans bacterium plays an important role in the bioleaching process of uranium. The rusticyanin protein is the second most crucial component in the electron transport chain in the membrane of the Acidithiobacillus ferroxidnas bacterium. This protein belongs to the large family of copper blue proteins. The protein sequence rusticyanin 23270 was derived from UniProtKB database. A suitable template for modeling was prepared from the Swiss model server, and the best protein model was made with Modeller software. The His143Leu mutation was developed using the Pymol software in the protein. The effect of the mutation on the stability of the protein structure was investigated by analysing the results of molecular dynamics simulation on the wild-type and mutant protein. The values RMSD and RMSF are the same for both wild-type and mutant. The amount of Rg in mutant protein is reduced. His143Leu mutation in the rusticyanin 23270 protein does not affect the secondary structure protein and slightly increases the folding and stability of the tertiary structure.

Toxicology of respiratory system: Profiling chemicals in PM10 for molecular targets and adverse outcomes.

Numerous studies have shown that the increasing trend of respiratory diseases have been closely associated with the endogenous toxic chemicals (polycyclic aromatic hydrocarbons, heavy metal ions, etc.) in PM10. In the present study, we aim to determine the strong correlations between the chemicals in PM10 and the adverse consequences. We used the ChemView DB, the ToxRef DB and a comprehensive literature analysis to collect, identify, and evaluate the chemicals in PM10 and their adverse effects on respiratory system, and then used the ToxCast DB to analyze their bioactivity and key targets through 1192 molecular targets and cell characteristic endpoints. Meanwhile, the bioinformatics analysis were carried out on the molecular targets to screen out prevention and treatment targets. A total of 310 chemicals related to the respiratory system were identified. An unsupervised two-directional heatmap was constructed based on hierarchical clustering of 227 chemicals by their effect scores. A subset of 253 chemicals with respiratory system toxicity had in vitro bioactivity on 318 molecular targets that could be described, clustered and annotated in the heatmap and bipartite network, which were analyzed based on the protein information in UniProt KB database and the software of GO, STRING, and KEGG. These results showed that the chemicals in PM10 have strong correlation with different types of respiratory system injury. The main pathways of respiratory system injury caused by PM10 are the Calcium signaling pathway, MAPK signaling pathway, and PI3K-AKT signaling pathway, and the core proteins in which are likely to be the molecular targets for the prevention and treatment of damage caused by PM10.

Green seaweed Ulva lactuca, a potential source of bioactive peptides revealed by in silico analysis

The green seaweed Ulva lactuca is found in coastal areas. Potential bioactive peptides have a wide range of uses, including antibacterial, antifungal, antiviral, alpha-amylase inhibitory, antiamnestic, ACE inhibitory, immunomodulatory, anticancer, antioxidative, contracting, anti-inflammatory, and more. This study's major goal was to use in-silico methods to reveal the availability and abundance of putative bioactive peptides in the RuBisCO protein of Ulva lactuca seaweed. The protein sequence was obtained using the UniProtKB database. The peptide sequences were produced by using the BIOPEP-UWM enzyme activity tool. The peptides were ranked using PeptideRanker, and the toxicity profile was assessed using ToxinPred. For the first time, the current in-silico investigation depicts the pharmacological characteristics of Ulva lactuca by focusing on the RuBisCO protein. Numerous bioactive peptides with inhibitory activity against the angiotensin-I converting enzyme (ACE), dipeptidyl peptidase IV, antioxidant, dipeptidyl peptidase III, renin, antithrombotic, antiamnestic, α-glucosidase, anti-inflammatory, hypolipidemic activity, and ion flow regulation were produced by the in-silico proteolysis of rubisco using 18 proteases. Based on the cutoff score >0.75, pancreatic elastase liberated the most bioactive peptide sequences from the rubisco compared to other proteases. Additionally, PeptideRanker's findings showed that a number of di-, tri-, and/or polypeptides produced from the RuBisCO have a great deal of potential to be used as bioactive peptides. These studies open up new opportunities for these bioactive peptides in the fields of pharmacy, medicine, the food sector, and marine biotechnology.

Annotating Cancer-Related Variants at Protein-Protein Interface with Structure-PPi.

A comprehensive analysis of germline and somatic variants requires complex computational approaches that combine next-generation sequencing (NGS)-based omics data with curated annotations from public repositories. Here, we describe Structure-PPi, which facilitates the analysis of cancer-related variants onto protein 3D structures, interaction interfaces, and other important functional sites (i.e., catalytic, ligand-binding, posttranslational modification). Our approach relies on features extracted from Interactome3D, UniProtKB, InterPro, APPRIS, dbNSFP, and COSMIC databases and provides complementary information to pathogenicity prediction methods. Thus, Structure-PPi helps in the discrimination of false-positive predictions and adds both mechanistic and biological insights into the role of variants in a given cancer. An online version of the tools is available at https://rbbt.bsc.es/StructurePPI/ .

Proteomics for Development of Food Allergy Vaccines.

Food allergy is a hypersensitivity reaction to food products initiated by immunologic mechanisms, which represents one of the major concerns in food safety. New therapies for food allergies including oral and epicutaneous allergen-specific immunotherapy are required, and B cell epitope-based allergy vaccines are a good promise to improve this field. In this chapter, we describe a workflow for the design of food allergy vaccines using proteomic tools. The strategy is defined based on the characterization of B cell epitopes for a particular food allergen. For that, the workflow comprises five consecutive steps: (1) shotgun proteomics analysis of different protein isoforms for a particular food allergen, (2) downloading all protein sequences for the specific allergen included in UniProtKB database, (3) analysis by protein-based bioinformatics of B cell epitopes, (4) synthesizing of the selected B cell peptide epitopes, and (5) performing of immunoassays using sera from healthy and allergic patients. The results from this method provide a rationale repository of B cell epitopes for the design of new specific immunotherapies for a particular food allergen. The strategy was optimized for all the beta-parvalbumins (β-PRVBs), which are considered as the main fish allergens. Using this workflow, a total of 35 peptides were identified as B cell epitopes, among them the top 4 B cell peptide epitopes that may induce protective immune response were selected as potential peptide vaccine candidates for fish allergy.

Bioinformatic Analysis of Human Collagen Sequence Mutations on Osteogenesis Imperfecta

Collagen has been implicated in a number of pathological conditions. When an amino acid in triple helix is replaced with other amino acids, the collagen structure is destroyed. The deterioration in the collagen structure causes various hereditary diseases and dysfunctions. In this study, the mutations on the alpha-1 chain of type I collagen, which is the most common in the human body, were examined using Python programming language. Based on the previous studies, brittle bone disease (OI) type 2 caused by mutations in type-I collagen alpha-1 chains, has been focused on. UniprotKB database were used for the mutations reported. The mutations obtained were combined in an alpha-I chain and it was seen that the most mutated amino acid was glycine (Gly). Since glycine amino acid affects the stability of the helix structure of the collagen alpha-I chain, it can be considered to influence collagen-induced diseases. The most frequently recurring mutations (glycine (G)> arginine (R), glycine (G)> serine (S), glycine (G)>aspartate (D)) were detected. As a result of comparison, increase in molecular mass, change in isoelectric point, decrease in hydropathy index, change in charge state and acid-base properties were observed. The effect of these changed features on brittle bone disease (OI) has been interpreted.

Multi-epitope based subunit vaccine construction against Banna virus targeting on two outer proteins (VP4 and VP9): A computational approach.

Recently, RNA viruses have gained a mammoth concern for causing various outbreaks, and due to pandemics, they are acquiring additional attention throughout the world. An emerging RNA as well as vector-borne Banna Virus (BAV) is a human pathogen resulting in encephalitis, fever, headache, muscle aches, and severe coma. Besides human, pathogenic BAV was also detected from pigs, cattle, ticks, midges, and mosquitoes in Indonesia, China, and Vietnam. Due to high mutation tendency and dearth of a species barrier, this virus will consider as a significant threat in the near future throughout the planet, particularly in Africa. Despite of severe human case fatalities in several countries, there are no specific therapeutics, available vaccines, and other preventive measures against BAV. Thus, to find out the effective therapeutics and preventive strategies are crying exigency. In the present study, a unique multi-epitope-based peptide vaccine candidate is constructed using bioinformatics' tools that efficiently instigate immune cells for generating BAV antibodies. The potential vaccine candidates were developed using both T and B -cell epitopes. UniprotKB database was used to retrieve of two outer proteins (VP9 and VP4), and homologous sequences of BAV taxid: 7763, 649,604, 77,763, and 8453 were searched by NCBI BLAST. These serotypes are the most closely associated with the disease. Then combining the best-selected epitopes in various combinations with different adjuvants, three distinct vaccine candidates were formed. The validity tests were performed for the screened vaccine candidate regarding stability, allergenicity, and antigenicity parameters. Moreover, molecular dynamic simulations of the selected vaccine with TLR-8 immune receptor confirmed the stability of the binding pose and showed a significant response to immune cells. Thus, the results established that the designed chimeric peptide vaccine could enhance the immune response against BAV.

Proteomic analysis of the lake trout (Salvelinus namaycush) heart and blood: The beginning of a comprehensive lake trout protein database.

Lake trout (Salvelinus namaycush) are a top-predator species in the Laurentian Great Lakes that are often used as bioindicators of chemical stressors in the ecosystem. Although many studies are done using these fish to determine concentrations of stressors like legacy persistent, bioaccumulative and toxic chemicals, there are currently no proteomic studies on the biological effects these stressors have on the ecosystem. This lack of proteomic studies on Great Lakes lake trout is because there is currently no complete, comprehensive protein database for this species. Here, we employed proteomics approaches to develop a lake trout protein database that could aid in future research on this fish, in particular exposomics and adductomics. The current study utilized heart tissue and blood from two lake trout. Our previous work using lake trout liver revealed 4194 potential protein hits in the NCBI databases and 3811 potential protein hits in the UniProtKB databases. In the current study, using the NCBI databases we identified 838 proteins for the heart and 580 proteins for the blood tissues in the biological replicate 1 (BR1) and 1180 potential protein hits for the heart and 561 potential protein hits for the blood in BR2. Similar results were obtained using the UniProtKB databases. This study builds on our previous work by continuing to build the first comprehensive lake trout protein database and provides insight into protein homology through evolutionary relationships. This data is available via the PRIDE partner repository with the dataset identifier PXD023970.

PASS: Protein Annotation Surveillance Site for Protein Annotation Using Homologous Clusters, NLP, and Sequence Similarity Networks.

Advances in genome sequencing have accelerated the growth of sequenced genomes but at a cost in the quality of genome annotation. At the same time, computational analysis is widely used for protein annotation, but a dearth of experimental verification has contributed to inaccurate annotation as well as to annotation error propagation. Thus, a tool to help life scientists with accurate protein annotation would be useful. In this work we describe a website we have developed, the Protein Annotation Surveillance Site (PASS), which provides such a tool. This website consists of three major components: a database of homologous clusters of more than eight million protein sequences deduced from the representative genomes of bacteria, archaea, eukarya, and viruses, together with sequence information; a machine-learning software tool which periodically queries the UniprotKB database to determine whether protein function has been experimentally verified; and a query-able webpage where the FASTA headers of sequences from the cluster best matching an input sequence are returned. The user can choose from these sequences to create a sequence similarity network to assist in annotation or else use their expert knowledge to choose an annotation from the cluster sequences. Illustrations demonstrating use of this website are presented.

Molecular mimicry between SARS-CoV-2 and the female reproductive system.

IntroductionOogenesis, the process of egg production by the ovary, involves a complex differentiation program leading to the production of functional oocytes. This process comprises a sequential pathway of steps that are finely regulated. The question related to SARS‐CoV‐2 infection and fertility has been evoked for several reasons, including the mechanism of molecular mimicry, which may contribute to female infertility by leading to the generation of deleterious autoantibodies, possibly contributing to the onset of an autoimmune disease in infected patients.ObjectiveThe immunological potential of the peptides shared between SARS‐CoV‐2 spike glycoprotein and oogenesis‐related proteins; Thus we planned a systematic study to improve our understanding of the possible effects of SARS‐CoV‐2 infection on female fertility using the angle of molecular mimicry as a starting point.MethodsA library of 82 human proteins linked to oogenesis was assembled at random from UniProtKB database using oogenesis, uterine receptivity, decidualization, and placentation as a key words. For the analyses, an artificial polyprotein was built by joining the 82 a sequences of the oogenesis‐associated proteins. These were analyzed by searching the Immune Epitope DataBase for immunoreactive SARS‐CoV‐2 spike glycoprotein epitopes hosting the shared pentapeptides.ResultsSARS‐CoV‐2 spike glycoprotein was found to share 41 minimal immune determinants, that is, pentapeptides, with 27 human proteins that relate to oogenesis, uterine receptivity, decidualization, and placentation. All the shared pentapeptides that we identified, with the exception of four, are also present in SARS‐CoV‐2 spike glycoprotein–derived epitopes that have been experimentally validated as immunoreactive.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy.

Background Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics. Materials and Methods The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves. Results Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls. Conclusions AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

IDOPS, a Profile HMM-Based Tool to Detect Pesticidal Sequences and Compare Their Genetic Context.

Biopesticide-based crop protection is constantly challenged by insect resistance. Thus, expansion of available biopesticides is crucial for sustainable agriculture. Although Bacillus thuringiensis is the major agent for pesticide bioprotection, the number of bacteria species synthesizing proteins with biopesticidal potential is much higher. The Bacterial Pesticidal Protein Resource Center (BPPRC) offers a database of sequences for the control of insect pests, grouped in structural classes. Here we present IDOPS, a tool that detects novel biopesticidal sequences and analyzes them within their genetic environment. The backbone of the IDOPS detection unit is a curated collection of high-quality hidden Markov models that is in accordance with the BPPRC nomenclature. IDOPS was positively benchmarked with BtToxin_Digger and Cry_Processor. In addition, a scan of the UniProtKB database using the IDOPS models returned an abundance of new pesticidal protein candidates distributed across all of the structural groups. Gene expression depends on the genomic environment, therefore, IDOPS provides a comparative genomics module to investigate the genetic regions surrounding pesticidal genes. This feature enables the investigation of accessory elements and evolutionary traits relevant for optimal toxin expression and functional diversification. IDOPS contributes and expands our current arsenal of pesticidal proteins used for crop protection.

In silico evidence of cardiac troponin non‐classical secretion: new insights on the pathobiology of myocardial injury

Background Cardiac troponin is one of the most important biomarkers in cardiology. Traditionally, it has represented a biochemical sign of necrosis, a characteristic of acute myocardial infarction (AMI). However, with the arrival of high-sensitivity detection assays, cardiac troponin is now detected in healthy people and in a number of uncommon scenarios unrelated to AMI (such as strenuous exercise, rapid atrial pacing and COVID-19). This suggest that troponin could be released as a consequence of alternative processes other than the well-stablished necrotic cell death. To try to explain this phenomenon, multiple mechanisms of troponin release have been postulated, although there is still no consensus on this matter. Furthermore, the term myocardial injury was included in the Fourth Universal Definition of Myocardial Infarction. This entity is understood as a troponin determination above the upper reference limit, not necessarily associated with a specific diagnosis. Nonetheless, the histological or pathobiological process responsible of myocardial injury has not been determined. Here, we studied the possibility of troponin release through non-classical secretion, a pathway specially triggered by cellular stress and inflammation. Methods The protein sequences of the eight different isoforms of troponin (I, T and C of fast-twitch skeletal, slow-twitch skeletal and cardiac muscle) were retrieved from the UniProtKB database. SecretomeP 2.0, a neural networks-based program, was used to predict if any of these proteins undergoes non-classical release. SignalP 5.0 and TMHMM 2.0 were also applied to rule out the possibility of classical (endoplasmic reticulum/Golgi dependent) and Type IV (transmembrane) non-classical secretion. Results and Discussion After analyzing the sequences, the cardiac T isoform was predicted to be non-classically secreted, as well as the I, T and C subunits of the fast-twitch skeletal muscle and the I subunit of the slow-twitch skeletal muscle. Additionally, none of the troponin isoforms was found to be subject to classical or Type IV secretion. The present study provides in silico evidence of cardiac troponin T release by means of non-classical secretion. We believe that this potential new mechanism could contribute to the debate around cardiac troponin high-sensitivity assays. Thus, it might establish a theoretical frame capable of explaining troponin elevations by causes other than myocyte necrosis. Conclusions Troponin T secretion may help in the pathobiological definition of myocardial injury. There are already some experimental studies showing the release of troponin through extracellular microvesicles (a type of non-classical secretion) in various cell lines. Nonetheless, more work is needed to fully understand this intriguing phenomenon in cardiomyocytes.

Sequencing, assembly and annotation of the whole-insect genome of Lymantria dispar dispar, the European gypsy moth.

The European gypsy moth, Lymantria dispar dispar (LDD), is an invasive insect and a threat to urban trees, forests and forest-related industries in North America. For use as a comparator with a previously published genome based on the LD652 pupal ovary-derived cell line, as well as whole-insect genome sequences obtained from the Asian gypsy moth subspecies L. dispar asiatica and L. dispar japonica, the whole-insect LDD genome was sequenced, assembled and annotated. The resulting assembly was 998 Mb in size, with a contig N50 of 662 Kb and a GC content of 38.8%. Long interspersed nuclear elements constitute 25.4% of the whole-insect genome, and a total of 11,901 genes predicted by automated gene finding encoded proteins exhibiting homology with reference sequences in the NCBI NR and/or UniProtKB databases at the most stringent similarity cutoff level (i.e., the gold tier). These results will be especially useful in developing a better understanding of the biology and population genetics of L. dispar and the genetic features underlying Lepidoptera in general.

Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus.

The world is reeling under severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, and it will be frightening if compounded by other co-existing infections. The co-occurrence of the Dengue virus (DENV) and Chikungunya virus (CHIKV) has been into existence, but recently the co-infection of DENV and SARS-CoV-2 has been reported. Thus, the possibility of DENV, CHIKV, and SARS-CoV-2 co-infection could be predicted in the future with enhanced vulnerability. It is essential to elucidate the host interactors and the connected pathways to understand the biological insights. The in silico approach using Cytoscape was exploited to elucidate the common human proteins interacting with DENV, CHIKV, and SARS-CoV-2 during their probable co-infection. In total, 17 interacting host proteins were identified showing association with envelope, structural, non-structural, and accessory proteins. Investigating the functional and biological behaviour using PANTHER, UniProtKB, and KEGG databases uncovered their association with several cellular pathways including, signaling pathways, RNA processing and transport, cell cycle, ubiquitination, and protein trafficking. Withal, exploring the DrugBank and Therapeutic Target Database, total seven druggable host proteins were predicted. Among all integrin beta-1, histone deacetylase-2 (HDAC2) and microtubule affinity-regulating kinase-3 were targeted by FDA approved molecules/ drugs. Furthermore, HDAC2 was predicted to be the most significant target, and some approved drugs are available against it. The predicted druggable targets and approved drugs could be investigated to obliterate the identified interactions that could assist in inhibiting viral infection.

Liquid chromatography-tandem mass spectrometry glycoproteomic study of porcine IgG and detection of subtypes.

While high-throughput proteomic methods have been widely applied to monoclonal antibodies and human immunoglobulin gamma (IgG) samples, less information is available on porcine IgG. As pigs are considered one of the most suitable species for xenotransplantation, it is important to characterize IgG amino acid sequences and glycosylation profiles, which is the focus of this study. Three different purified porcine IgG samples, including wild-type and knockout species, were digested with trypsin and enriched for glycopeptides. Digestion mixtures were spiked with a mixture of six standard peptides. Analysis was performed using electrospray ionization liquid chromatography-tandem mass spectrometry (MS/MS) in standard MS/MS data-dependent acquisition mode on a hybrid triple quadrupole time-of-flight mass spectrometer. To facilitate the classification of subtypes detected experimentally, UniprotKB database entries were organized using comparative alignment scores. Sequences were grouped based on 11 different subtypes as translated from GenBank entries. Proteomic searches were accomplished automatically using specialized software, whereas glycoprotein searches were performed manually by monitoring the extracted chromatograms of diagnostic MS/MS glycan fragments and studying their corresponding mass spectra; 40-50 non-glycosylated peptides and 4-5 glycosylated peptides were detected in each sample, with several glycoforms per sequence. Proteomic analysis of porcine IgG is complicated by factors such as the presence of several subtypes, redundant heavy chain (HC) sequences in protein databases, and the lack of consistent cross-referencing between databases. Aligning and comparing HC sequences were necessary to eliminate redundancy. This study highlights the complexity of pig IgG and shows the importance of MS in proteomics and glycoproteomics.

In Silico Analysis of Nonsynonumus Single Nucleotide Polymorphisms(nsSNPs) in Human GAAGene

Background: Pompe disease an autosomal recessive lysosomal disorder caused by deficiency of acid alpha-glucosidase (GAA) gene, leading to a progressive accumulation of glycogen in lysosomes in skeletal, cardiac, and smooth muscles. GAA gene provides instructions for producing an enzyme called acid alpha-glucosidase. This enzyme is active in lysosomes, which are structures that serve as recycling centers within cells. Acid alpha-glucosidase normally breaks down glycogen into a simpler glucose. Glucose is the main energy source for most cells. Material and Methods: Information regarding GAA SNPs was obtained from National Center for Biological Information (NCBI) SNPs database, November 2020.The SNPs and the related ensembles proteins (ESNP) were obtained from the SNPs database and UniprotKB database. Analysis of Functional Consequences of coding nonsynonoumus single nucleotide polymorphisms (nsSNPs) by Sequence Homology Based Method wee done using SIFTS, Provean and Polyphen-2 software. Protein stability was predicted using I-Mutant and Mupro software. The pathogenicity and relation of the mutation to disease association was predicted by SNP&GO and PHD software. For the effect of the mutation on the protein structure and function, ProjectHope, Chimera and Raptor X were used. For the association, co-expression and shared domains of the gene to other genes GeneMANIA software was used. Results:GAA gene had254 SNPS WITH 179 SNPS IN CODING REGION AND 75 IN NON CODINGregion, nsSNPs were analyzed by SIFT software, and only 64 nsSNPs were predicted to be deleterious while 11 were tolerated. Using Provean software 53nsSNPs were deleterious and 11nsSNPs were neutral. These deleterious SNPs were analyzed using polyphen_2 software to predict the damaging SNPs, the damaging SNPs were 53 nsSNPs. Using I-mutant software for evaluation of the degree of stability due to mutation. The present study predicted 43 nsSNPsto decrease the stability of the protein, While only 10 SNPs had increased in the stability of the protein. For Mupro software52 nsSNPs decrease the stability of protein and only one SNP increase the stability of protein. For more confirmation for the mutation the study used PHD software in which 46nsSNPs were reported as a disease related, while 7SNPs were reported as neutral. SNP &GO software was also used, it predicted 37 SNPs as disease related, while 16 SNPs were reported as neutral. For ProjectHope the 20 damaging nsSNPs from previous software gave effect on the function and structure of the protein, Conclusion: The study concluded that there were 20 nsSNPs predicted to be damaging to the protein (rs1800307 rs18003122 rs28937909 rs61736895 rs121907937 rs121907938 rs121907945 rs139009731 rs142752477 rs144016984 rs147327209 rs148842275 rs202095215 rs369098202 rs372486238 rs372604133 rs374143224 rs374470794 rs374687883 rs377544304 ).These results may provide useful information needed to help researchers to use these nsSNPs as biomarkers for Pompe disease .