Endogenous tau released from human ReNCell VM cultures by neuronal activity is phosphorylated at multiple sites.

Tau is an intracellular protein but also known to be released into the extracellular fluid. Tau release mechanisms have drawn intense attention as these are known to play a key role in Alzheimer's disease (AD) pathology. However, tau can also be released under physiological conditions although its physiological function and release mechanisms have been poorly characterized, especially in human neuronal cells. We investigated endogenous tau release in ReNCell VM, a human neuroprogenitor cell line, under physiological conditions and found that tau is spontaneously released from cells. To study activity-dependent release of endogenous tau, human ReNCell VM culture was stimulated by 100µM AMPA or 50mM KCl for one-hour, tau was actively released to the culture medium. The released tau was highly phosphorylated at nine phosphorylation sites (pSites) detected by phospho-specific tau antibodies including AT270 (T175/T181), AT8 (S202/T205), AT100 (T212/S214), AT180 (T231), and PHF-1 (S396/S404), showing that these pSites are important for activity-dependent tau release from human ReNCell VM. Intracellular tau showed various phosphorylation status across these sites, with AT270 and PHF-1 highly phosphorylated while AT8 and AT180 were minimally phosphorylated, suggesting that AT8 and AT180 pSites exhibit a propensity for secretion rather than being retained intracellularly. This activity-dependent tau release was significantly decreased by inhibition of GSK-3ß, demonstrating that GSK3ß-dependent phosphorylation of tau plays an important role in its release by neuronal activity. In this study, we showed that ReNCell VM serves as a valuable model for studying endogenous physiological tau release. Further, ReNCell model can be also used to study pathological release of human tau that will contribute to our understanding of the progression of AD and related dementias.

Identification of therapeutic drug target of Shigella Flexneri serotype X through subtractive genomic approach and in-silico screening based on drug repurposing.

Shigellosis, induced by Shigella flexneri, constitutes a significant health burden in developing nations, particularly impacting socioeconomically disadvantaged communities. Designated as the second most prevalent cause of diarrheal illness by the World Health Organization (WHO), it precipitates an estimated 212,000 fatalities annually. Within the spectrum of S. flexneri strains, serotype X is notably pervasive and resilient, yet its comprehensive characterization remains deficient. The present investigation endeavors to discern potential pharmacological targets and repurpose existing drug compounds against S. flexneri serotype X. Employing the framework of subtractive genomics, the study interrogates the reference genome of S. flexneri Serotype X (strain 2,002,017; UP000001884) to delineate its proteome into categories of non-homologous, non-paralogous, essential, virulent, and resistant constituents, thereby facilitating the identification of therapeutic targets. Subsequently, a screening of approximately 9000 compounds from the FDA library against the identified drug target aims to delineate efficacious agents for combating S. flexneri serotype X infections. The application of subtractive genomics methodology yields prognostic insights, unveiling non-paralogous proteins (n = 4122), non-homologues (n = 1803), essential (n = 1246), drug-like (n = 389), resistant (n = 167), alongside 42 virulent proteins within the reference proteome. This iterative process culminates in the identification of Serine O-acetyltransferase as a viable drug target. Subsequent virtual screening endeavors to unearth FDA-approved medicinal compounds capable of inhibiting Serine O-acetyltransferase. Noteworthy candidates such as DB12983, DB15085, DB16098, DB16185, and DB16262 emerge, exhibiting potential for mitigating S. flexneri Serotype X. Despite the auspicious findings, diligent scrutiny is imperative to ascertain the efficacy and safety profile of the proposed drug candidates vis-à-vis S. flexneri.

Integrating core subtractive proteomics and reverse vaccinology for multi-epitope vaccine design against Rickettsia prowazekii endemic typhus.

Rickettsia prowazekii is an intracellular, obligate, gram-negative coccobacillus responsible for epidemic typhus. Usually, the infected body louse or its excrement when rubbed into the skin abrasions transmits the disease. The infection with R. prowazekii causes the highest death rate (> 20% without antibiotic treatment and now 1-7%), followed by epidemic typhus, which often manifests in unsanitary conditions (up to 15-30%). Conventionally, vaccine design has required pathogen growth and both assays (in vivo and in vitro), which are costly and time-consuming. However, advancements in bioinformatics and computational biology have accelerated the development of effective vaccine designs, reducing the need for traditional, time-consuming laboratory experiments. Subtractive genomics and reverse vaccinology have become prominent computational methods for vaccine model construction. Therefore, the RefSeq sequence of Rickettsia prowazekii (strain Madrid E) (Proteome ID: UP000002480) was subjected to subtractive genomic analysis, including factors such as non-similarity to host proteome, essentiality, subcellular localization, antigenicity, non-allergenicity, and stability. Based on these parameters, the vaccine design process selected specific proteins such as outer membrane protein R (O05971_RICPR PETR; OmpR). Eventually, the OmpR was subjected to a reverse vaccinology approach that included molecular docking, immunological simulation, and the discovery of B-cell epitopes and MHC-I and MHC-II epitopes. Consequently, a chimeric or multi-epitope-based vaccine was proposed by selecting the V11 vaccine and its 3D structure modeling along with molecular docking against TLR and HLA protein, in silico simulation, and vector designing. The obtained results from this investigation resulted in a new perception of inhibitory ways against Rickettsia prowazekii by instigating novel immunogenic targets. To further assess the efficacy and protective ability of the newly designed V11 vaccine against Rickettsia prowazekii infections, additional evaluation such as in vitro or in vivo immunoassays is recommended.

Pangenome diversification and resistance gene characterization in Salmonella Typhi prioritized RfaJ as a significant therapeutic marker.

BACKGROUND: Salmonella Typhi stands as the etiological agent responsible for the onset of human typhoid fever. The pressing demand for innovative therapeutic targets against S. Typhi is underscored by the escalating prevalence of this pathogen and the severe nature of its infections. Consequently, this study employs pangenome analysis to scrutinize 119 S. Typhi-resistant strains, aiming to identify the most promising therapeutic targets originating from its core genome. RESULTS: Subtractive genomics was employed to systematically eliminate non-homologous (n=1147), essential (n=551), drug-like (n=80), and pathogenicity-related (n=18) proteins from the initial pool of 3351 core genome proteins. Consequently, lipopolysaccharide 1,2-glucosyltransferase RfaJ was designated as the optimal pharmacological target due to its potential versatility. Furthermore, a compendium of 9000 FDA-approved compounds was repurposed for evaluation against the RfaJ drug target, with the specific intent of prioritizing novel, high-potency therapeutic candidates for combating S. Typhi. Ultimately, four compounds, namely DB00549 (Zafirlukast), DB15637 (Fluzoparib), DB15688 (Zavegepant), and DB12411 (Bemcentinib), were singled out as potential inhibitors based on the ligand-protein binding affinity (indicated by the lowest anticipated binding energy) and the overall stability of these compounds. Notably, molecular dynamics simulations, conducted over a 50 nanosecond interval, convincingly demonstrated the stability of these compounds in the context of the RfaJ protein. CONCLUSION: In summary, the present findings hold significant promise as an initial stride in the broader drug discovery endeavor against S. Typhi infections. However, the experimental validation of the identified drug target and drug candidate is further required to increase the effectiveness of the applied methodology.

Lead identification against Mycobacterium tuberculosis using highly enriched active molecules against pantothenate synthetase.

The Pantothenate synthetase (PS) from the Mycobacterium tuberculosis (Mtb) holds a crucial role in the survival and robust proliferation of bacteria through its catalysis of coenzyme A and acyl carrier protein synthesis. The present study undertook the PS drug target in complex with a co-crystallized ligand and subjected it to docking and virtual screening approaches. The experimental design encompassed three discrete datasets: an active dataset featuring 136 compounds, an inactive dataset comprising 56 compounds, and a decoys dataset curated from the zinc library, comprising an extensive compilation of approximately 53,000 compounds. The compounds' binding energies were observed to be in the range of -5 to ∼-14 kcal/mol. Additionally, binding energy results were further refined through Enrichment Factor analysis (EF). EF is a new statistical approach which uses the scores obtained from docking-based virtual screening and predicts the precision of the scoring function. Remarkably, the Enrichment Factor (EF) analysis produced exceptionally favorable outcomes, attaining an EF of approximately 49% within the uppermost 1% fraction of the compound distribution. Finally, a total of eight compounds, evenly distributed between the active dataset and the decoys dataset, emerged as potent inhibitors of the Pantothenate synthetase (PS) enzyme. The analysis of inhibition constants and binding energy revealed a notable correlation, with an r-squared value (r2) of 0.912 between the two parameters. Furthermore, the shortlisted compounds were subjected to 100 ns MD simulation to determine their stability and dynamics behavior. The decoy compounds that have been identified, exhibiting properties comparable to the active compounds, are postulated as potential candidates for targeting the Pantothenate synthetase (PS) enzyme to treat Mtb infection. Nevertheless, in the pursuit of a comprehensive investigation, it is advisable to undertake additional experimental validation as a component of the subsequent study.Communicated by Ramaswamy H. Sarma.

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network.

Tuberculosis remains the leading cause of death from a single pathogen. On the other hand, antimicrobial resistance (AMR) makes it increasingly difficult to deal with this disease. We present the hyperbolic embedding of the Mycobacterium tuberculosis protein interaction network (mtbPIN) of resistant strain (MTB XDR1219) to determine the biological relevance of its latent geometry. In this hypermap, proteins with similar interacting partners occupy close positions. An analysis of the hypermap of available drug targets (DTs) and their direct and intermediate interactors was used to identify potentially useful drug combinations and drug targets. We identify rpsA and rpsL as close DTs targeted by different drugs (pyrazinamide and aminoglycosides, respectively) and propose that the combination of these drugs could have a synergistic effect. We also used the hypermap to explain the effects of drugs that affect multiple DTs, for example, forcing the bacteria to deal with multiple stresses like ethambutol, which affects the synthesis of both arabinogalactan and lipoarabinomannan. Our strategy uncovers novel potential DTs, such as dprE1 and dnaK proteins, which interact with two close DT pairs: arabinosyltransferases (embC and embB), Ser/Thr protein kinase (pknB) and RNA polymerase (rpoB), respectively. Our approach provides mechanistic explanations for existing drugs and suggests new DTs. This strategy can also be applied to the study of other resistant strains.

The Gene Network Correlation Analysis of Obesity to Type 1 Diabetes and Cardiovascular Disorders: An Interactome-Based Bioinformatics Approach.

The current study focuses on the importance of Protein-Protein Interactions (PPIs) in biological processes and the potential of targeting PPIs as a new treatment strategy for diseases. Specifically, the study explores the cross-links of PPIs network associated with obesity, type 1 diabetes mellitus (T1DM), and cardiac disease (CD), which is an unexplored area of research. The research aimed to understand the role of highly connected proteins in the network and their potential as drug targets. The methodology for this research involves retrieving genes from the NCBI online gene database, intersecting genes among three diseases (type 1 diabetes, obesity, and cardiovascular) using Interactivenn, determining suitable drug molecules using NetworkAnalyst, and performing various bioinformatics analyses such as Generic Protein-Protein Interactions, topological properties analysis, function enrichment analysis in terms of GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG), gene co-expression network, and protein drug as well as protein chemical interaction network. The study focuses on human subjects. The results of this study identified 12 genes [VEGFA (Vascular Endothelial Growth Factor A), IL6 (Interleukin 6), MTHFR (Methylenetetrahydrofolate reductase), NPPB (Natriuretic Peptide B), RAC1 (Rac Family Small GTPase 1), LMNA (Lamin A/C), UGT1A1 (UDP-glucuronosyltransferase family 1 membrane A1), RETN (Resistin), GCG (Glucagon), NPPA (Natriuretic Peptide A), RYR2 (Ryanodine receptor 2), and PRKAG2 (Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2)] that were shared across the three diseases and could be used as key proteins for protein-drug/chemical interaction. Additionally, the study provides an in-depth understanding of the complex molecular and biological relationships between the three diseases and the cellular mechanisms that lead to their development. Potentially significant implications for the therapy and management of various disorders are highlighted by the findings of this study by improving treatment efficacy, simplifying treatment regimens, cost-effectiveness, better understanding of the underlying mechanism of these diseases, early diagnosis, and introducing personalized medicine. In conclusion, the current study provides new insights into the cross-links of PPIs network associated with obesity, T1DM, and CD, and highlights the potential of targeting PPIs as a new treatment strategy for these prevalent diseases.

Proteogenomic analysis of Serratia marcescens using computational subtractive genomics approach.

Serratia marcescens, a Gram-negative bacterium (Enterobacteriaceae) is a hospital-acquired opportunistic pathogen that infects the urinary and central nervous systems. The identification of new therapeutics against S. marcescens is crucial since it is now multi-drug resistant. Therefore, the current study was aimed to identify potential drug targets against S. marcescens strains i.e. WW4, SM39, and Db11 using comparative metabolic pathway analysis and subtractive genomics approach. The applied bioinformatics-based method was used to identify the unique metabolic pathways as the prioritized drug targets. The downstream analysis has led to the identification of three pathways that are specifically absent and/or present in the specific strain. Consequently, six proteins were identified through subtractive genomic analysis. The identified proteins were found as non-homologous and essential to the pathogen's survival as well as unique to the WW4 strain. The estimated features proposed it as a potential drug target. The selected protein was further subjected to in-depth structural analysis for the structure modeling, structure validation, and protein-protein interaction analysis. Furthermore, the library of ~1500 approved compounds was screened against selected drug target to identify potential drug candidates. The current work may help in repurposing of the drug compounds as novel medication against S. marcescens.

Pangenome profiling of novel drug target against vancomycin-resistant Enterococcus faecium.

Enterococcus faecium is a frequent causative agent of nosocomial infection mainly acquired from outgoing hospital patients (Hospital Acquired Infection-HAIs). They are largely involved in the outbreaks of bacteremia, UTI, and endocarditis with a high transmissibility rate. The recent emergence of VRE strain (i.e. vancomycin resistant enterococcus) turned it into high priority pathogen for which new drug research is of dire need. Therefore, in current study, pangenome and resistome analyses were performed for available antibiotic-resistant genomes (n = 216) of E. faecium. It resulted in the prediction of around 5,059 genes as an accessory gene, 1,076 genes as core and 1,558 genes made up a unique genome fraction. Core genes common to all strains were further used for the identification of potent drug targets by applying subtractive genomics approach. Moreover, the COG functional analysis showed that these genomes are highly enriched in metabolic pathways such as in translational, ribosomal, proteins, carbohydrates and nucleotide transport metabolism. Through subtractive genomics it was observed that 431 proteins were non-homologous to the human proteome, 166 identified as essential for pathogen survival while 26 as potential and unique therapeutic targets. Finally, 3-dehydroquinate dehydrogenase was proposed as a potent drug target for further therapeutic candidate identification. Moreover, the molecular docking and dynamic simulation technique were applied to performed a virtual screening of natural product libraries (i.e., TCM and Ayurvedic compounds) along with 3-amino-4,5-dihydroxy-cyclohex-1-enecarboxylate (DHS) as a standard compound to validate the study. Consequently, Argeloside I, Apigenin-7-O-gentiobioside (from Ayurvedic library), ZINC85571062, and ZINC85570908 (TCM library) compounds were identified as potential inhibitors of 3-dehydroquinate dehydrogenase. The study proposed new compounds as novel therapeutics, however, further experimental validation is needed as a follow-up.Communicated by Ramaswamy H. Sarma.

An integrated computational approach to infer therapeutic targets from Campylobacter concisus and peptidomimetic based inhibition of its pyrimidine metabolism pathway.

Campylobacter concisus is a commensal of the human oral flora that has been allied with persistent diarrhea and inflammatory bowel disease (IBD). In children under the age of two, Campylobacter infections are common in the developing countries and have frequently been associated with mortality. They are becoming a prevalent cause of bacterial diarrhea in early adulthood in developed countries as well. The need for identifying new therapeutic targets and drugs is crucial for curbing such infections. Therefore, we identified 18 cytoplasmic potential therapeutic candidates against the type strain of C. concisus and deoxycytidine triphosphate deaminase (dCTP deaminase), involved in pyrimidine synthesis was selected for screening of peptidomimetic inhibitors (n > 30,000 peptidomimetics) against it. To the best of our knowledge, this target has not been studied for Campylobacter spp. Three potent inhibitors of this enzyme were prioritized i.e. peptidomimetic 27, 64, and 150. Dynamics simulation of 100 ns was carried out to validate findings for top-scored inhibitors along with physiology-based pharmacokinetics to estimate behavior in human body and predict dosing parameters. This verification demonstrates a first-in-human pharmacokinetic simulation for these peptidomimetics and can help enhance confidence in these peptide-like structures. Moiety 27 (IUPAC name: 5-[(3,5-dimethyl-1H-pyrazol-1-yl)methyl]-N-{[2-(2-methoxyethyl)-1-oxo-1H,2H,3H,4H-pyrrolo[1,2-a]pyrazin-3-yl]methyl}furan-2-carboxamide), 64 (IUPAC name: 3-(2-methylpropyl)-1-{3-[5-(5-oxo-1-phenylpyrrolidin-3-yl)-1,2,4-oxadiazol-3-yl]phenyl}urea), and 150 (IUPAC name: N-(3-methoxypropyl)-1-[6-(4-methylphenyl)-4H,6H,7H-[1,2,3]triazolo[4,3-c][1,4]oxazine-3-carbonyl]piperidine-4-carboxamide) were identified as potent inhibitors of C. concisus.Communicated by Ramaswamy H. Sarma.

Construction of a comprehensive protein-protein interaction map for multi drug resistant klebsiella pneumoniae to identify drug targets: A computational approach.

Klebsiella pneumoniae is an encapsulated rod-shaped, Gram-negative microbe that can form biofilm. It is an opportunistic Enterobacter usually involved in nosocomial infection, conferring resistance to almost all antibiotics and hence become therapeutically challenging. In the current study, the Protein Interaction Network (PIN) of MDR K. pneumoniae has been identified. The proteins are the building blocks of all organisms. Proteins interact with each other to carry out their physiological functions. The interactions are integrated to form Protein Interaction Network (PIN). The strain DA48896 has been selected as it was isolated from Pakistan and harboring bla-oxa-181, conferring resistance to carbapenem. Total 20,936 high confidence interactions of 3782 proteins have been predicted from the STRING database. The predicted interactions were annotated functionally and mapped on their corresponding pathways. The predicted PIN was verified using semantic similarity between the Gene Ontology. The topological properties were calculated and retrieved topologically significant proteins consisting of 390 proteins. Among them 49 proteins are non-homologous essential that can serve as the potential drug targets. These proteins were further explored for druggability, their association with pathways involved in drug resistance and eventually prioritized as potential drug targets. This study will be helpful to design drug candidates against prioritized proteins.

Mining therapeutic targets from the antibiotic-resistant Campylobacter coli and virtual screening of natural product inhibitors against its riboflavin synthase.

Campylobacter coli resides in the intestine of several commonly consumed animals, as well as water and soil. It leads to campylobacteriosis when humans eat raw/undercooked meat or come into contact with infected animals. A common manifestation of the infection is fever, nausea, headache, and diarrhea. Increasing antibiotic resistance is being observed in this pathogen. The increased incidence of C. coli infection, and post-infection complications like Guillain-Barré syndrome, make it an important pathogen. It is essential to find novel therapeutic targets and drugs against it, especially with the emergence of antibiotic-resistant strains. In the current study, genomes of 89 antibiotic-resistant strains of C. coli were downloaded from the PATRIC database. Potent drug targets (n = 36) were prioritized from the core genome (n = 1,337 genes) of this species. Riboflavin synthase was selected as a drug target and pharmacophore-based virtual screening was performed to predict its inhibitors from the NPASS (n = ~ 30,000 compounds) natural product library. The top three docked compounds (NPC115144, NPC307895, and NPC470462) were selected for dynamics simulation (for 50 ns) and ADMET profiling. These identified compounds appear safe for targeting this pathogen and can be further validated by experimental analysis before clinical trials.

Immunoinformatic-guided designing of multi-epitope vaccine construct against Brucella Suis 1300.

Brucella suis mediates the transmission of brucellosis in humans and animals and a significant facultative zoonotic pathogen found in livestock. It has the capacity to survive and multiply in a phagocytic environment and to acquire resistance under hostile conditions thus becoming a threat globally. Antibiotic resistance is posing a substantial public health threat, hence there is an unmet and urgent clinical need for immune-based non-antibiotic methods to treat brucellosis. Hence, we aimed to explore the whole proteome of Brucella suis to predict antigenic proteins as a vaccine target and designed a novel chimeric vaccine (multi-epitope vaccine) through subtractive genomics-based reverse vaccinology approaches. The applied subsequent hierarchical shortlisting resulted in the identification of Multidrug efflux Resistance-nodulation-division (RND) transporter outer membrane subunit (gene BepC) that may act as a potential vaccine target. T-cell and B-cell epitopes have been predicted from target proteins using a number of immunoinformatic methods. Six MHC I, ten MHC II, and four B-cell epitopes were used to create a 324-amino-acid MEV construct, which was coupled with appropriate linkers and adjuvant. To boost the immunological response to the vaccine, the vaccine was combined with the TLR4 agonist HBHA protein. The MEV structure predicted was found to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. To confirm the interactions with the receptors, a molecular docking simulation of the MEV was done using the human TLR4 (toll-like receptor 4) and HLAs. The stability and binding of the MEV-docked complexes with TLR4 were assessed using molecular dynamics (MD) simulation. Finally, MEV was reverse translated, its cDNA structure was evaluated, and then, in silico cloning into an E. coli expression host was conducted to promote maximum vaccine protein production with appropriate post-translational modifications. These comprehensive computer calculations backed up the efficacy of the suggested MEV in protecting against B. suis infections. However, more experimental validations are needed to adequately assess the vaccine candidate's potential. HIGHLIGHTS: • Subtractive genomic analysis and reverse vaccinology for the prioritization of novel vaccine target • Examination of chimeric vaccine in terms of allergenicity, antigenicity, MHC I, II binding efficacy, and structural-based studies • Molecular docking simulation method to rank based vaccine candidate and understand their binding modes.

New lead compounds identification against KRas mediated cancers through pharmacophore-based virtual screening and in vitro assays.

Cancer remains the leading cause of mortality and morbidity in the world, with 19.3 million new diagnoses and 10.1 million deaths in 2020. Cancer is caused due to mutations in proto-oncogenes and tumor-suppressor genes. Genetic analyses found that Ras (Rat sarcoma) is one of the most deregulated oncogenes in human cancers. The Ras oncogene family members including NRas (Neuroblastoma ras viral oncogene homolog), HRas (Harvey rat sarcoma) and KRas are involved in different types of human cancers. The mutant KRas is considered as the most frequent oncogene implicated in the development of lung, pancreatic and colon cancers. However, there is no efficient clinical drug even though it has been identified as an oncogene for 30 years. Therefore there is an emerging need to develop potent, new anticancer drugs. In this study, computer-aided drug designing approaches as well as experimental methods were employed to find new and potential anti-cancer drugs. The pharmacophore model was developed from an already known FDA approved anti-cancer drug Bortezomib using the software MOE. The validated pharmacophore model was then used to screen the in-house and commercially available databases. The pharmacophore-based virtual screening resulted in 26 and 86 hits from in-house and commercial databases respectively. Finally, 6/13 (in-house database) and 24/64 hits (commercial databases) were selected with different scaffolds having good interactions with the significant active residues of KRasG12D protein that were predicted as potent lead compounds. Finally, the results of pharmacophore-based virtual screening were further validated by molecular dynamics simulation analysis. The 6 hits of the in-house database were further evaluated experimentally. The experimental results showed that these compounds have good anti-cancer activity which validate the protocol of our in silico studies. KRasG12D protein is a very important anti-cancer target and potent inhibitors for this target are still not available, so small lead compound inhibitors were identified to inhibit the activity of this protein by blocking the GTP-binding pocket.Communicated by Ramaswamy H. Sarma.

Identification of potent compounds against SARs-CoV-2: An in-silico based drug searching against Mpro.

The worldwide pandemic of coronavirus disease 2019 (COVID-19) along with the various newly discovered major SARS-CoV-2 variants, including B.1.1.7, B.1.351, and B.1.1.28, constitute the Variant of Concerns (VOC). It's difficult to keep these variants from spreading over the planet. As a result of these VOCs, the fifth wave has already begun in several countries. The rapid spread of VOCs is posing a serious threat to human civilization. There is currently no specific medicine available for the treatment of COVID-19. Here, we present the findings of methods that used a combination of structure-assisted drug design, virtual screening, and high-throughput screening to swiftly generate lead compounds against Mpro protein of SARs-CoV-2. Therapeutics, in addition to vaccinations, are an essential element of the healthcare response to COVID-19's persistent threat. In the current study, we designed the efficient compounds that may combat all emerging variants of SARs-CoV-2 by targeting the common Mpro protein. The present study was aimed to discover new compounds that may be proposed as new therapeutic agents to treat COVID-19 infection without any adverse effects. For this purpose, a computational-based virtual screening of 352 in-house synthesized compounds library was performed through molecular docking and Molecular Dynamics (MD) simulation approach. As a result, four novel potent compounds were successfully shortlisted by implementing certain pharmacological, physiological, and ADMET criteria i.e., compounds 3, 4, 21, and 22. Furthermore, MD simulations were performed to evaluate the stability and dynamic behavior of these compounds with Mpro complex for about 30 ns. Eventually, compound 22 was found to be highly potent against Mpro protein and was further evaluated by applying 100 ns simulations. Our findings showed that these shortlisted compounds may have potency to treat the COVID-19 infection for which further experimental validation is proposed as part of a follow-up investigation.

Phytochemical profiling, in vitro biological activity, docking studies, and cytotoxicity assessments of Rondeletia odorata Jacquin: An unexplored plant of the coffee family.

Rondeletia odorata Jacquin is a flowering plant that belongs to the coffee family. As a rich source of polyphenols with significant antioxidant potential, R. odorata may have health benefits. Therefore, in the current work, ethanolic extract of aerial parts and its n-hexane, ethyl acetate, and n-butanol soluble fractions were analyzed for their antioxidant potential and various enzyme inhibition properties. The total phenolic and flavonoid contents of the crude ethanol extract (ROE) and its n-hexane (ROH), ethyl acetate (ROEA), and n-butanol (ROB) fractions were determined spectrophotometrically, while metabolic profiling was established through UHPLC-MS analysis, which revealed the presence of 58 phytochemicals. Total phenolic and flavonoid contents of ROE extract were measured as 51.92 mg GA.Eq./g of dry extract and 52.35 mg Qu.Eq./g of the dry extract, respectively. In the DPPH radical scavenging activity assay, ROE and ROEA showed the highest potential with values of 62.13 ± 0.62 and 76.31% ± 1.86%, respectively, comparable to quercetin (80.89% ± 0.54%). Similarly, in the FRAP assay, the same pattern of the activity was observed with ROE and ROEA, which displayed absorbance values of 1.32 ± 0.01 and 0.80 ± 0.02 at 700 nm, respectively, which are comparable (1.76 ± 0.02) with the reference compound quercetin, whereas the ROH showed maximum metal-chelating capacity (62.61% ± 1.01%) among all extracts and fractions. Antibacterial activity assay indicated that the ROEA fraction was the most active against Serratia marcescens, Stenotrophomonas maltophilia, Bacillus subtilis, Klebsiella pneumonia, and Staphylococcus aureus, while the rest of the fractions showed good to moderate activity. Enzyme inhibition assays showed that ROEA fraction exhibited the highest activity with IC50 values of 2.78 ± 0.42 and 3.95 ± 0.13 mg/mL against urease and carbonic anhydrase (CA), respectively. Furthermore, the docking studies of some of the major compounds identified in the extract revealed a strong correlation with their inhibitory activity. All extracts and fractions were also tested for their thrombolytic activity, and the ROB fraction showed a notable potential. Antiviral assay led to remarkable outcomes. Thus, it can be inferred that aerial parts of R. odorata are potential sources of bioactive components with several significant pharmacological activities.

Total Bioactive Contents, Metabolic Profiling, Docking Studies, Antioxidant and Enzyme Inhibition Activities of Convolvulus Arvensis L. and Multivariate Analysis to Unravel a Potential Herb as Natural Resource for Pharmaceutical Industry.

Convolvulus arvensis L. is an evergreen herb growing in various regions of Pakistan. Despite of several medicinal properties associated to this herb, it was not investigated scientifically for its bioactive compounds and detailed pharmaceutical properties. Therefore, its methanolic extract was divided into hexane (CA-H), chloroform (CA-C), ethyl acetate (CA-E) and butanol (CA-B) soluble fractions. CA-H and CA-C were found rich in phenolics (30.73±0.63 and 20.15±0.59 mg GAE/g of the extract, respectively), and the same fractions exhibited significant antioxidant activities (DPPH: 5.23±0.11 & 12.34±0.17 mg TE/g extract, respectively; ABTS: 36.82±0.04 & 56.74±0.61 mg TE/g extract, respectively). Also in CUPRAC activity assay, CA-H and CA-C exhibited highest activities as 87.30±0.46 and 56.74±0.61 mg TE/g extract, respectively, while CA-C was most active in FRAP activity assay with value of 40.21±2.19 mg TE/g extract. Total antioxidant capacity (1.23±0.033 mmol TE/g extract) was also found higher for CA-C, while CA-H activity was also comparable, however, CA-H showed higher metal chelating activity (22.74±0.001 mg EDTAE/g extract) than that of CA-C (17.55±0.22 mg EDTAE/g extract). These activities clearly revealed a direct relation between antioxidant potential and phenolic contents of CA-H and CA-C. In AChE and BChE inhibitory assay, CA-H and CA-E showed better inhibition (AChE: 8.24±0.77 & 4.46±0.007 mg GALAE/g extract; BChE: 5.40±0.02 & 1.92±0.24 mg GALAE/g extract) as compared to other fractions, whereas, against tyrosinase, CA-B was most active (37.35±0.53 mg KAE/g extract). CA-H and CA-C also showed higher inhibitory potential (0.98±0.08 & 0.58±0.01 mmol ACAE/g extract) against α-Amylase; while against α-Glucosidase, CA-E was the most active fraction. UHPLC/MS analysis of the methanolic extract of C. arvensis disclosed the presence of 62 compounds as sterols, triterpenes, flavonoids, fatty acids, alkaloids and coumarins. In Multivariate Analysis, the total phenolic contents were correlated strongly with all antioxidant assays except FRAP and DPPH. Regarding enzyme inhibitory properties, only AChE, BChE and α-amylase were correlated with the total phenolic contents in the extracts. Docking analyses confirmed these findings, as identified compounds had high binding free energy and inhibition constants with the enzymes studied. It was finally concluded that C. arvensis is a potential industrial crop, which can be a component of nutraceuticals and functional foods, if evaluated for its toxicity.

Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis.

Moraxella catarrhalis (M. catarrhalis) is a gram-negative bacterium, responsible for major respiratory tract and middle ear infection in infants and adults. The recent emergence of the antibiotic resistance M. catarrhalis demands the prioritization of an effective drug target as a top priority. Fortunately, the failure of new drugs and host toxicity associated with traditional drug development approaches can be avoided by using an in silico subtractive genomics approach. In the current study, the advanced in silico genome subtraction approach was applied to identify potential and pathogen-specific drug targets against M. catarrhalis. We applied a series of subtraction methods from the whole genome of pathogen based on certain steps i.e. paralogous protein that have extensive homology with humans, essential, drug like, non-virulent, and resistant proteins. Only 38 potent drug targets were identified in this study. Eventually, one protein was identified as a potential new drug target and forwarded to the structure-based studies i.e. histidine kinase (UniProt ID: D5VAF6). Furthermore, virtual screening of 2000 compounds from the ZINC database was performed against the histidine kinase that resulted in the shortlisting of three compounds as the potential therapeutic candidates based on their binding energies and the properties exhibited using ADMET analysis. The identified protein gives a platform for the discovery of a lead drug candidate that may inhibit it and may help to eradicate the otitis media caused by drug-resistant M. catarrhalis. Nevertheless, the current study helped in creating a pipeline for drug target identification that may assist wet-lab research in the future.

Machine Learning-based Virtual Screening for STAT3 Anticancer Drug Target.

BACKGROUND: Signal transducers and activators of the transcription (STAT) family is composed of seven structurally similar and highly conserved members, including STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. The STAT3 signaling cascade is activated by upstream kinase signals and undergoes phosphorylation, homo-dimerization, nuclear translocation, and DNA binding, resulting in the expression of target genes involved in tumor cell proliferation, metastasis, angiogenesis, and immune editing. STAT3 hyperactivation has been documented in a number of tumors, including head and neck, breast, lung, liver, kidney, prostate, pancreas cancer, multiple myeloma, and acute myeloid leukemia. Drug discovery is a timeconsuming and costly process; it may take ten to fifteen years to bring a single drug to the market. Machine learning algorithms are very fast and effective and commonly used in the field, such as drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. OBJECTIVE: The present work aims to perform machine learning-based virtual screening for the STAT3 drug target. METHODS: Machine learning models, such as k-nearest neighbor, support vector machine, Gaussian naïve Bayes, and random forest for classifying the active and inactive inhibitors against a STAT3 drug target, were developed. Ten-fold cross-validation was used for model validation. Then the test dataset prepared from the zinc database was screened using the random forest model. A total of 20 compounds with 88% accuracy was predicted as active against STAT3. Furthermore, these twenty compounds were docked into the active site of STAT3. The two complexes with good docking scores as well as the reference compound were subjected to MD simulation. A total of 100ns MD simulation was performed. RESULTS: Compared to all other models, the random forest model revealed better results. Compared to the standard reference compound, the top two hits revealed greater stability and compactness. CONCLUSION: In conclusion, our predicted hits have the ability to inhibit STAT3 overexpression to combat STAT3-associated diseases.

Anxiolytic, anti-nociceptive and body weight reducing effects of L-lysine in rats: Relationship with brain serotonin an In-Vivo and In-Silico study.

L-lysine (L-lys) had long been comprehended as an essential amino acid for humans. There were reports that the absence or inadequate availability of L-lys in the diet may lead to mental and physical impairments. The present study was designed to explore the effects of L-lys on body weight changes, cumulative food intake, anxiety-like behavior and pain perception in rats. 5-Hydroxytryptamine (5-HT, serotonin) metabolism, and tryptophan (Trp) levels in the midbrain (MB), hippocampus (HP), and prefrontal cortex (PFC) were also determined. Animals were treated with L-lys in doses of 0.5 g/kg and 1 g/kg for 20 days and behavioral studies were performed on day 1st and day 20th. After monitoring behaviors on day 20th, animals were killed to collect the serum and brain regions MB, HP and PFC. 5-HT metabolism and Trp levels were determined by HPLC-EC. The treatment produce no effect on food intakes but body weights were reduced. 20 days administration of L-lys produced an anxiolytic effect and increased exploratory activity on day 1st. Repeated administration of L-lys increased 5-HT levels in the PFC and HP. 5-Hydroxyindoleacetic acid (5-HIAA), the metabolite of 5-HT, decreased in the HP. Trp, the precourser of 5-HT, decreased in the PFC. Results suggested a decrease in 5-HT degredation in enhancing 5-HT levels. Results of in-silico analysis showed that lysine had a potential binding affinity for MAO (monoamine oxidase) A and B with an energy of (-4.8 kcal/mol and -5.3 kcal/mol) respectively. The molecular dynamic simulation study revealed the stability of L-lys after 10 ns for each protein. Conclusively, the present study showed that L-lys produced an anxiolytic effect and reduced body weight. These beneficial effects were associated with an increase in 5-HT levels in the PFC and HP. In-silico analysis suggested that 5-HT increase were due to the binding of L-lys with MAOs resulting in an inhibition of the degradation of monoamine.