An integrated comparative genomics, subtractive proteomics and immunoinformatics framework for the rational design of a Pan-Salmonella multi-epitope vaccine.

Salmonella infections pose a significant global public health concern due to the substantial expenses associated with monitoring, preventing, and treating the infection. In this study, we explored the core proteome of Salmonella to design a multi-epitope vaccine through Subtractive Proteomics and immunoinformatics approaches. A total of 2395 core proteins were curated from 30 different isolates of Salmonella (strain NZ CP014051 was taken as reference). Utilizing the subtractive proteomics approach on the Salmonella core proteome, Curlin major subunit A (CsgA) was selected as the vaccine candidate. csgA is a conserved gene that is related to biofilm formation. Immunodominant B and T cell epitopes from CsgA were predicted using numerous immunoinformatics tools. T lymphocyte epitopes had adequate population coverage and their corresponding MHC alleles showed significant binding scores after peptide-protein based molecular docking. Afterward, a multi-epitope vaccine was constructed with peptide linkers and Human Beta Defensin-2 (as an adjuvant). The vaccine could be highly antigenic, non-toxic, non-allergic, and have suitable physicochemical properties. Additionally, Molecular Dynamics Simulation and Immune Simulation demonstrated that the vaccine can bind with Toll Like Receptor 4 and elicit a robust immune response. Using in vitro, in vivo, and clinical trials, our findings could yield a Pan-Salmonella vaccine that might provide protection against various Salmonella species.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Gut microbiome composition reveals the distinctiveness between the Bengali people and the Indigenous ethnicities in Bangladesh.

Ethnicity has a significant role in shaping the composition of the gut microbiome, which has implications in human physiology. This study intends to investigate the gut microbiome of Bengali people as well as several indigenous ethnicities (Chakma, Marma, Khyang, and Tripura) residing in the Chittagong Hill Tracts areas of Bangladesh. Following fecal sample collection from each population, part of the bacterial 16 s rRNA gene was amplified and sequenced using Illumina NovaSeq platform. Our findings indicated that Bangladeshi gut microbiota have a distinct diversity profile when compared to other countries. We also found out that Bangladeshi indigenous communities had a higher Firmicutes to Bacteroidetes ratio than the Bengali population. The investigation revealed an unclassified bacterium that was differentially abundant in Bengali samples while the genus Alistipes was found to be prevalent in Chakma samples. Further research on these bacteria might help understand diseases associated with these populations. Also, the current small sample-sized pilot study hindered the comprehensive understanding of the gut microbial diversity of the Bangladeshi population and its potential health implications. However, our study will help establish a basic understanding of the gut microbiome of the Bangladeshi population.

Gene silencing of Helicobacter pylori through newly designed siRNA convenes the treatment of gastric cancer.

BACKGROUND: Helicobacter pylori is a gastric pathogen that is responsible for causing chronic inflammation and increasing the risk of gastric cancer development. It is capable of persisting for decades in the harsh gastric environment because of the inability of the host to eradicate the infection. Several treatment strategies have been developed against this bacterium using different antibiotics. But the effectiveness of treating H. pylori has significantly decreased due to widespread antibiotic resistance, including an increased risk of gastric cancer. The small interfering RNAs (siRNA), which is capable of sequence-specific gene-silencing can be used as a new therapeutic approach for the treatment of a variety of such malignancies. In the current study, we rationally designed two siRNA molecules to silence the cytotoxin-associated gene A (CagA) and vacuolating cytotoxin A (VacA) genes of H. pylori for their significant involvement in developing cancer. METHODS: We selected a common region of all the available transcripts from different countries of CagA and VacA to design the siRNA molecules. The final siRNA candidate was selected based on the results from machine learning algorithms, off-target similarity, and various thermodynamic properties. RESULT: Further, we utilized molecular docking and all atom molecular dynamics (MD) simulations to assess the binding interactions of the designed siRNAs with the major components of the RNA-induced silencing complex (RISC) and results revealed the ability of the designed siRNAs to interact with the proteins of RISC complex in comparable to those of the experimentally reported siRNAs. CONCLUSION: These designed siRNAs should effectively silence the CagA and VacA genes of H. pylori during siRNA mediated treatment in gastric cancer.

Identification of repurposable drug targets in Mycoplasma pneumoniae using subtractive genomics, molecular docking and dynamics simulation.

Mycoplasma pneumoniae is a significant causative agent of community-acquired pneumonia, causing acute inflammation in the upper and lower respiratory tract as well as extrapulmonary syndromes. In particular, the elderly and infants are at greater risk of developing severe, life-threatening pneumonia caused by M. pneumoniae. Yet, the global increase in antimicrobial resistance against antibiotics for the treatment of M. pneumoniae infection highlights the urgent need to explore novel drug targets. To this end, bioinformatics approaches, such as subtractive genomics, can be employed to identify specific metabolic pathways and essential proteins unique to the pathogen that could be potential targets for new drugs. In this study, we implemented a subtractive genomics approach to identify 61 metabolic pathways and 42 essential proteins that are unique to M. pneumoniae. A subsequent screening in the DrugBank database revealed three druggable proteins with similarity to FDA-approved small-molecule drugs, and finally, the compound CHEBI:97093 was identified as a promising novel putative drug target. These findings can provide crucial insights for the development of highly effective drugs that selectively inhibit the pathogen-specific metabolic pathways, leading to better management and treatment of M. pneumoniae infections.

Investigation of pathogenic germline variants in gastric cancer and development of "GasCanBase" database.

BACKGROUND: Gastric cancer, which is also known as stomach cancer, can be influenced by both germline and somatic mutations. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in germline have long been reported to play a pivotal role in cancer progression. AIM: The aim of this study is to examine the nsSNP in GC-associated genes. The study also aims to develop a database with extensive information regarding the nsSNPs in the GC-associated genes and their impacts. METHODS AND RESULTS: A total of 34,588 nsSNPs from 1,493,460 SNPs of the 40 genes were extracted from the available SNP database. Drug binding and energy minimization were examined by molecular docking and YASARA. To validate the existence of the germline CDH1 gene mutation (rs34466743) in the isolated blood DNA of gastric cancer (GC) patients, polymerase chain reaction (PCR) and DNA sequencing were performed. According to the results of the gene network analysis, 17 genes may interact with other types of cancer. A total of 11,363 nsSNPs were detected within the 40 GC genes. Among these, 474 nsSNPs were predicted to be damaging and 40 to be the most damaging. The SNPs in domain regions were thought to be strong candidates that alter protein functions. Our findings proposed that most of the selected nsSNPs were within the domains or motif regions. Free Energy Deviation calculation of protein structure pointed toward noteworthy changes in the structure of each protein that can demolish its natural function. Subsequently, drug binding confirmed the structural variation and the ineffectiveness of the drug against the mutant model in individuals with these germline variants. Furthermore, in vitro analysis of the rs34466743 germline variant from the CDH1 gene confirmed the strength and robustness of the pipeline that could expand the somatic alteration for causing cancer. In addition, a comprehensive gastric cancer polymorphism database named "GasCanBase" was developed to make data available to researchers. CONCLUSION: The findings of this study and the "GasCanBase" database may greatly contribute to our understanding of molecular epidemiology and the development of precise therapeutics for gastric cancer. GasCanBase is available at: https://www.gascanbase.com/.

Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function.

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.

Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An In Silico Approach.

As an omnipresent opportunistic bacterium, Pseudomonas aeruginosa PAO1 is responsible for acute and chronic infection in immunocompromised individuals. Currently, this bacterium is on WHO's red list where new antibiotics are urgently required for the treatment. Finding essential genes and essential hypothetical proteins (EHP) can be crucial in identifying novel druggable targets and therapeutics. This study is aimed at characterizing these EHPs and analyzing subcellular and physiochemical properties, PPI network, nonhomologous analysis against humans, virulence factor and novel drug target prediction, and finally structural analysis of the identified target employing around 42 robust bioinformatics tools/databases, the output of which was evaluated using the ROC analysis. The study discovered 18 EHPs from 336 essential genes, with domain and functional annotation revealing that 50% of these proteins belong to the enzyme category. The majority are cytoplasmic and cytoplasmic membrane proteins, with half being stable proteins subjected to PPIs network analysis. The network contains 261 nodes and 269 edges for 9 proteins of interest, with 11 hubs containing at least three nodes each. Finally, a pipeline builder predicts 7 proteins with novel drug targets, 5 nonhomologous proteins against human proteome, human antitargets, and human gut flora, and 3 virulent proteins. Among these, homology modeling of NP_249450 and NP_251676 was done, and the Ramachandran plot analysis revealed that more than 94% of the residues were in the preferred region. By analyzing functional attributes and virulence characteristics, the findings of this study may facilitate the development of innovative antibacterial drug targets and drugs of Pseudomonas aeruginosa PAO1.

Pathogen-driven gene expression patterns lead to a novel approach to the identification of common therapeutic targets.

Developing a common medication strategy for disease control and management could be greatly beneficial. Investigating the differences between diseased and healthy states using differentially expressed genes aids in understanding disease pathophysiology and enables the exploration of protein-drug interactions. This study aimed to find the most common genes in diarrhea-causing bacteria such as Salmonella enterica serovar Typhimurium, Campylobacter jejuni, Escherichia coli, Shigella dysenteriae (CESS) to find new drugs. Thus, differential gene expression datasets of CESS were screened through computational algorithms and programming. Subsequently, hub and common genes were prioritized from the analysis of extensive protein-protein interactions. Binding predictions were performed to identify the common potential therapeutic targets of CESS. We identified a total of 827 dysregulated genes that are highly linked to CESS. Notably, no common gene interaction was found among all CESS bacteria, but we identified 3 common genes in both Salmonella-Escherichia and Escherichia-Campylobacter infections. Later, out of 73 protein complexes, molecular simulations confirmed 5 therapeutic candidates from the CESS. We have developed a new pipeline for identifying therapeutic targets for a common medication strategy against CESS. However, further wet-lab validation is needed to confirm their effectiveness.

Exploration of Streptococcus core genome to reveal druggable targets and novel therapeutics against S. pneumoniae.

Streptococcus pneumoniae (S. pneumoniae), the major etiological agent of community-acquired pneumonia (CAP) contributes significantly to the global burden of infectious diseases which is getting resistant day by day. Nearly 30% of the S. pneumoniae genomes encode hypothetical proteins (HPs), and better understandings of these HPs in virulence and pathogenicity plausibly decipher new treatments. Some of the HPs are present across many Streptococcus species, systematic assessment of these unexplored HPs will disclose prospective drug targets. In this study, through a stringent bioinformatics analysis of the core genome and proteome of S. pneumoniae PCS8235, we identified and analyzed 28 HPs that are common in many Streptococcus species and might have a potential role in the virulence or pathogenesis of the bacteria. Functional annotations of the proteins were conducted based on the physicochemical properties, subcellular localization, virulence prediction, protein-protein interactions, and identification of essential genes, to find potentially druggable proteins among 28 HPs. The majority of the HPs are involved in bacterial transcription and translation. Besides, some of them were homologs of enzymes, binding proteins, transporters, and regulators. Protein-protein interactions revealed HP PCS8235_RS05845 made the highest interactions with other HPs and also has TRP structural motif along with virulent and pathogenic properties indicating it has critical cellular functions and might go under unconventional protein secretions. The second highest interacting protein HP PCS8235_RS02595 interacts with the Regulator of chromosomal segregation (RocS) which participates in chromosome segregation and nucleoid protection in S. pneumoniae. In this interacting network, 54% of protein members have virulent properties and 40% contain pathogenic properties. Among them, most of these proteins circulate in the cytoplasmic area and have hydrophilic properties. Finally, molecular docking and dynamics simulation demonstrated that the antimalarial drug Artenimol can act as a drug repurposing candidate against HP PCS8235_RS 04650 of S. pneumoniae. Hence, the present study could aid in drugs against S. pneumoniae.

High-Risk Polymorphisms Associated with the Molecular Function of Human HMGCR Gene Infer the Inhibition of Cholesterol Biosynthesis.

HMG-CoA reductase or HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) is a rate-limiting enzyme involved in cholesterol biosynthesis. HMGCR plays an important role in the possible occurrence of hypercholesterolemia leading to atherosclerosis and coronary heart disease. This enzyme is a major target for cholesterol-lowering drugs such as "statin" which blocks the synthesis of mevalonate, a precursor for cholesterol biosynthesis. This study is aimed at characterizing deleterious mutations and classifying functional single nucleotide polymorphisms (SNPs) of the HMGCR gene through analysis of functional and structural evaluation, domain association, solvent accessibility, and energy minimization studies. The functional and characterization tools such as SIFT, PolyPhen, SNPs and GO, Panther, I-Mutant, and Pfam along with programming were employed to explore all the available SNPs in the HMGCR gene in the database. Among 6815 SNP entries from different databases, approximately 388 SNPs were found to be missense. Analysis showed that seven missense SNPs are more likely to have deleterious effects. A tertiary model of the mutant protein was constructed to determine the functional and structural effects of the HMGCR mutation. In addition, the location of the mutations suggests that they may have deleterious effects because most of the mutations are residing in the functional domain of the protein. The findings from the analysis predicted that rs147043821 and rs193026499 missense SNPs could cause significant structural and functional instability in the mutated proteins of the HMGCR gene. The findings of the current study will likely be useful in future efforts to uncover the mechanism and cause of hypercholesterolemia. In addition, the identified SNPs of HMGCR gene could set up a strong foundation for further therapeutic discovery.

Hypothetical protein predicted to be tumor suppressor: a protein functional analysis.

Litorilituus sediminis is a Gram-negative, aerobic, novel bacterium under the family of Colwelliaceae, has a stunning hypothetical protein containing domain called von Hippel-Lindau that has significant tumor suppressor activity. Therefore, this study was designed to elucidate the structure and function of the biologically important hypothetical protein EMK97_00595 (QBG34344.1) using several bioinformatics tools. The functional annotation exposed that the hypothetical protein is an extracellular secretory soluble signal peptide and contains the von Hippel-Lindau (VHL; VHL beta) domain that has a significant role in tumor suppression. This domain is conserved throughout evolution, as its homologs are available in various types of the organism like mammals, insects, and nematode. The gene product of VHL has a critical regulatory activity in the ubiquitous oxygen-sensing pathway. This domain has a significant role in inhibiting cell proliferation, angiogenesis progression, kidney cancer, breast cancer, and colon cancer. At last, the current study depicts that the annotated hypothetical protein is linked with tumor suppressor activity which might be of great interest to future research in the higher organism.

Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.

Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.

Wave-wise comparative genomic study for revealing the complete scenario and dynamic nature of COVID-19 pandemic in Bangladesh.

As the COVID-19 pandemic continues to ravage across the globe and take millions of lives and like many parts of the world, the second wave of the pandemic hit Bangladesh, this study aimed at understanding its causative agent, SARS-CoV-2 at the genomic and proteomic level and provide precious insights about the pathogenesis, evolution, strengths and weaknesses of the virus. As of Mid-June 2021, over 1500 SARS-CoV-2 genomesequences have been deposited in the GISAID database from Bangladesh which were extracted and categorized into two waves. By analyzing these genome sequences, it was discovered that the wave-2 samples had a significantly greater average rate of mutation/sample (30.79%) than the wave-1 samples (12.32%). Wave-2 samples also had a higher frequency of deletion, and transversion events. During the first wave, the GR clade was the most predominant but it was replaced by the GH clade in the latter wave. The B.1.1.25 variant showed the highest frequency in wave-1 while in case of wave-2, the B.1.351.3 variant, was the most common one. A notable presence of the delta variant, which is currently at the center of concern, was also observed. Comparison of the Spike protein found in the reference and the 3 most common lineages found in Bangladesh namely, B.1.1.7, B.1.351, B.1.617 in terms of their ability to form stable complexes with ACE2 receptor revealed that B.1.617 had the potential to be more transmissible than others. Importantly, no indigenous variants have been detected so far which implies that the successful prevention of import of foreign variants can diminish the outbreak in the country.

Genomic Surveillance of SARS-CoV-2 Viruses Collected during the Ending Phase of the First Wave of the COVID-19 Pandemic in Bangladesh.

Mutations, deletions, and the emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may pose a serious health threat. Here, we report the genome sequences of SARS-CoV-2 viruses that were collected from SARS-CoV-2-infected patients during the end phase of the first wave of the COVID-19 pandemic in Dhaka, Bangladesh.

Prognostic and clinicopathological insights of phosphodiesterase 9A gene as novel biomarker in human colorectal cancer.

BACKGROUND: PDE9A (Phosphodiesterase 9A) plays an important role in proliferation of cells, their differentiation and apoptosis via intracellular cGMP (cyclic guanosine monophosphate) signaling. The expression pattern of PDE9A is associated with diverse tumors and carcinomas. Therefore, PDE9A could be a prospective candidate as a therapeutic target in different types of carcinoma. The study presented here was designed to carry out the prognostic value as a biomarker of PDE9A in Colorectal cancer (CRC). The present study integrated several cancer databases with in-silico techniques to evaluate the cancer prognosis of CRC. RESULTS: The analyses suggested that the expression of PDE9A was significantly down-regulated in CRC tissues than in normal tissues. Moreover, methylation in the DNA promoter region might also manipulate PDE9A gene expression. The Kaplan-Meier curves indicated that high level of expression of PDE9A gene was associated to higher survival in OS, RFS, and DSS in CRC patients. PDE9A demonstrated the highest positive correlation for rectal cancer recurrence with a marker gene CEACAM7. Furtheremore, PDE9A shared consolidated pathways with MAPK14 to induce survival autophagy in CRC cells and showed interaction with GUCY1A2 to drive CRPC. CONCLUSIONS: Overall, the prognostic value of PDE9A gene could be used as a potential tumor biomarker for CRC.

Novel mutations in NSP-1 and PLPro of SARS-CoV-2 NIB-1 genome mount for effective therapeutics.

BACKGROUND: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiologic agent of coronavirus disease 2019 (COVID-19), is rapidly acquiring new mutations. Analysis of these mutations is necessary for gaining knowledge regarding different aspects of therapeutic development. Previously, we have reported a Sanger method-based genome sequence of a viral isolate named SARS-CoV-2 NIB-1, circulating in Bangladesh. The genome has four novel non-synonymous mutations in V121D, V843F, A889V, and G1691C positions. RESULTS: Using different computational tools, we have found V121D substitution has the potential to destabilize the non-structural protein-1 (NSP-1). NSP-1 inactivates the type-1 interferon-induced antiviral system. Hence, this mutant could be a basis of attenuated vaccines against SARS-CoV-2. V843F, A889V, and G1691C are all located in nonstructural protein-3 (NSP-3). G1691C can decrease the flexibility of the protein. V843F and A889V might change the binding pattern and efficacy of SARS-CoV-2 papain-like protease (PLPro) inhibitor GRL0617. V843F substitution in PLPro was the most prevalent mutation in the clinical samples. This mutation showed a reduced affinity for interferon-stimulated gene-15 protein (ISG-15) and might have an impact on innate immunity and viral spread. However, V843F+A889V double mutant exhibited the same binding affinity as wild type PLPro. A possible reason behind this phenomenon can be that V843F is a conserved residue of PLPro which damaged the protease structure, but A889V, a less conserved residue, presumably neutralized that damage. CONCLUSIONS: Mutants of NSP-1 could provide attenuated vaccines against coronavirus. Also, these mutations of PLPro might be targeted to develop better anti-SARS therapeutics. We hope our study will help to get better insides during the development of attenuated vaccine and PLPro inhibitors.

Evaluation of the susceptibility and fatality of lung cancer patients towards the COVID-19 infection: A systemic approach through analyzing the ACE2, CXCL10 and their co-expressed genes.

Coronavirus disease-2019 (COVID-19) is a recent world pandemic disease that is caused by a newly discovered strain of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS- CoV-2). Patients with comorbidities are most vulnerable to this disease. Therefore, cancer patients are reported to be more susceptible to COVID-19 infection, particularly lung cancer patients. To evaluate the probable reasons behind the excessive susceptibility and fatality of lung cancer patients to COVID-19 infection, we targeted the two most crucial agents, Angiotensin-converting enzyme 2 (ACE2) and C-X-C motif 10 (CXCL10). ACE2 is a receptor protein that plays a vital role in the entry of SARS-CoV-2 into the host cell and CXCL10 is a cytokine mainly responsible for the lung cell damage involving in a cytokine storm. By using the UALCAN and GEPIA2 databases, we observed that ACE2 and CXCL10 are mostly overexpressed in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). We then identified the functional significance of ACE2 and CXCL10 in lung cancer development by determining the genetic alteration frequency in their amino acid sequences using the cBioPortal web portal. Lastly, we did the pathological assessment of targeted genes using the PANTHER database. Here, we found that ACE2 and CXCL10 along with their commonly co-expressed genes are involved respectively in the binding activity and immune responses in case of lung cancer and COVID-19 infection. Finally, based on this systemic analysis, we concluded that ACE2 and CXCL10 are two possible biomarkers responsible for the higher susceptibility and fatality of lung cancer patients towards the COVID-19.

Newly designed analogues from SARS-CoV inhibitors mimicking the druggable properties against SARS-CoV-2 and its novel variants.

The emerging variants of SARS coronavirus-2 (SARS-CoV-2) have been continuously spreading all over the world and have raised global health concerns. The B.1.1.7 (United Kingdom), P.1 (Brazil), B.1.351 (South Africa) and B.1.617 (India) variants, resulting from multiple mutations in the spike glycoprotein (SGp), are resistant to neutralizing antibodies and enable increased transmission. Hence, new drugs might be of great importance against the novel variants of SARS-CoV-2. The SGp and main protease (Mpro) of SARS-CoV-2 are important targets for designing and developing antiviral compounds for new drug discovery. In this study, we selected seventeen phytochemicals and later performed molecular docking to determine the binding interactions of the compounds with the two receptors and calculated several drug-likeliness properties for each compound. Luteolin, myricetin and quercetin demonstrated higher affinity for both the proteins and interacted efficiently. To obtain compounds with better properties, we designed three analogues from these compounds and showed their greater druggable properties compared to the parent compounds. Furthermore, we found that the analogues bind to the residues of both proteins, including the recently identified novel variants of SARS-CoV-2. The binding study was further verified by molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) approaches by assessing the stability of the complexes. MD simulations revealed that Arg457 of SGp and Met49 of Mpro are the most important residues that interacted with the designed inhibitors. Our analysis may provide some breakthroughs to develop new therapeutics to treat the proliferation of SARS-CoV-2 in vitro and in vivo.