Screening inhibitors against the Ef-Tu of Fusobacterium nucleatum: a docking, ADMET and PBPK assessment study.

The oral pathogen Fusobacterium nucleatum has recently been associated with an elevated risk of colorectal cancer (CRC), endometrial metastasis, chemoresistance, inflammation, metastasis, and DNA damage, along with several other diseases. This study aimed to explore the disruption of protein machinery of F. nucleatum via inhibition of elongation factor thermo unstable (Ef-Tu) protein, through natural products. No study on Ef-Tu inhibition by natural products or in Fusobacterium spp. exists till todate. Ef-Tu is an abundant specialized drug target in bacteria that varies from human Ef-Tu. Elfamycins target Ef-Tu and hence, Enacyloxin IIa was used to generate pharmacophore for virtual screening of three natural product libraries, Natural Product Activity and Species Source (NPASS) (n = 30000 molecules), Tibetan medicinal plant database (n = 54 molecules) and African medicinal plant database (n > 6000 molecules). Peptaibol Septocylindrin B (NPC141050), Hirtusneanoside, and ZINC95486259 were prioritized from these libraries as potential therapeutic candidates. ADMET profiling was done for safety assessment, physiological-based pharmacokinetic modeling in human and mouse for getting insight into drug interaction with body tissues and molecular dynamics was used to assess stability of the best hit NPC141050 (Septocylindrin B). Based on the promising results, we propose further in vitro, in vivo and pharmacokinetic testing on the lead Septocylindrin B, for possible translation into therapeutic interventions.

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.

An integrated in silico based subtractive genomics and reverse vaccinology approach for the identification of novel vaccine candidate and chimeric vaccine against XDR Salmonella typhi H58.

Salmonella typhi is notorious for causing enteric fever which is also known as typhoid fever. It emerged as an extreme drug resistant strain that requires urgent attention to prevent its global spread. Statistically, about 11-17 million typhoid illnesses are reported worldwide annually. The only alternative approach for the control of this illness is proper vaccination. However, available typhoid vaccine has certain limitations such as poor long-term efficacy, and non-recommendation for below 6 years children, which opens the avenues for designing new vaccines to overcome such limitations. Computational-based reverse vaccinology along with subtractive genomics analysis is one of the robust approaches used for the prioritization of vaccine candidates through direct screening of genome sequence assemblies. In the current study, we have successfully designed a peptide-based novel antigen chimeric vaccine candidate against the XDR strain of S. typhi H58. The pipeline revealed four peptides from WP_001176621.1 i.e., peptidoglycan-associated lipoprotein Pal and two peptides from WP_000747548.1 i.e., OmpA family lipoprotein as promising target for the induction of immune response against S. typhi. The six epitopes from both proteins were found as immunogenic, antigenic, virulent, highly conserved, nontoxic, and non-allergenic among whole Salmonella H58 proteome. Furthermore, the binding interaction between a chimeric vaccine and human population alleles was unveiled through structure-based studies. So far, these proteins have never been characterized as vaccine targets against S. typhi. The current study proposed that construct V2 could be a significant vaccine candidate against S. typhi H58. However, to ascertain this, future experimental holistic studies are recommended as follow-up.

Integrated bioinformatics based subtractive genomics approach to decipher the therapeutic function of hypothetical proteins from Salmonella typhi XDR H-58 strain.

PURPOSE: The efficacy of drugs against Salmonella infection have compromised due to emerging XDR H58 strain. There is a dire need to find novel antimicrobial drug targets as well as drug candidates to cure by the XDR strain of Salmonella. It is observed that the complete genome sequence of the XDR H58 strain contains a large number of hypothetical proteins with unknown cellular and biological functions. Hence, it is indispensable to annotate these proteins functionally as well as structurally to identify novel drug targets. METHODS: In the current study, a comparative genomics and proteomics based approach was applied to find the novel drug targets in XDR strain while comparing the MDR and NR strains of Salmonella typhi. RESULTS: The characterization of ~ 350 hypothetical proteins were performed through determination of their physio-chemical properties, sub-cellular localization, functional annotation, and structure-based studies. As a result, only five proteins were prioritized as essential, druggable, and virulent proteins. Moreover, only one protein i.e. WP_000916613.1 was functionally annotated with high confidence and subjected to further structure-based analysis. CONCLUSION: The current study presents a hypothetical protein from the XDR S. typhi proteome as a potential pharmacological target against which novel therapeutic candidates may be predicted. The outcome of the current study may lead to formulate a general set of pipelines for better understanding of the role of hypothetical proteins in pathogenesis of not only Salmonella but also for other pathogens.

Exploring the psychological and religious perspectives of cancer patients and their future financial planning: a Q-methodological approach.

BACKGROUND: Cancer patients are often hesitant to talk about their mental health, religious beliefs regarding the disease, and financial issues that drain them physically and psychologically. But there is a need to break this taboo to understand the perceptions and behaviours of the patients. Previous studies identified many psychological factors that are bothering cancer patients. However, it still requires exploring new elements affecting their mental and physical health and introducing new coping strategies to address patients' concerns. METHODS: The current study aims to identify cancer patients' perceived attitudes towards the severity of illness, understand their fears, tend towards religion to overcome the disease, and future financial planning by using a Q-methodological approach. Data were collected in three steps from January-June 2020, and 51 cancer patients participated in the final stage of Q-sorting. RESULTS: The findings of the study are based on the principal component factor analysis that highlighted three essential factors: (1) feelings, (2) religious beliefs about the acceptance of death, and (3) their future personal and financial planning. Further, the analysis shows that the patients differ in their beliefs, causes and support that they received as a coping mechanism. CONCLUSION: This study explains cancer patients' psychological discomfort and physical pain but cannot relate it to co-morbidities. Q methodology allows the contextualization of their thoughts and future planning in different sets, like acceptance of death, combating religion's help, and sharing experiences through various platforms. This study will help health professionals derive new coping strategies for treating patients and financial managers to design insurance policies that help them to share their financial burdens.

Re-purposing of hepatitis C virus FDA approved direct acting antivirals as potential SARS-CoV-2 protease inhibitors.

A new coronavirus strain called as SARS-CoV-2 has emerged from Wuhan, China in late 2019 and it caused a worldwide pandemic in a few months. After the Second World War, it is the biggest calamity observed as there is no specific US Food and Drugs Administration (USFDA) approved drug or vaccine available globally for the treatment. Several clinical trials are ongoing for therapeutic alternatives, however with little success rate. Considering that the time is crucial, the drug repurposing and data obtained from in silico models are one of the most important approaches to identify possible lead inhibitors against SARS-CoV-2. More recently, the Direct Acting Antivirals (DAAs) are emerged as the most promising drugs to control viral infection. The Main Protease (Mpro), a key enzyme in the SARS-CoV-2 replication cycle, is found close homolog to the Hepatitis C Virus (HCV) protease and could be susceptible of blocking its activity by DAAs. In the current study, the DAAs were investigated as antivirals using structure based computational approach against Mpro of SARS-CoV-2 to propose them as new therapeutics. In total, 20 DAAs of HCV, including a reference compound O6K were docked against Mpro. The docked structures were examined and resulted in the identification of six highly promising DAAs i.e. beclabuvir, elbasvir, paritaprevir, grazoprevir, simeprevir, and asunapevir exhibiting high theoretical binding affinity to Mpro from SARS-CoV-2 in comparison to other DAAs. Furthermore, the post docking analysis revealed that Cys145, Glu166, His163, Thr26, His41, and Met165 played potential role for the binding of these DAAs inside binding site of Mpro. Furthermore, the correlation between binding energies were found in accord with the results from the reported IC50s for some DAAs. Overall, the current study provides insight to combat COVID-19 using FDA-approved DAAs as repurposed drugs.

Pan-Genome Reverse Vaccinology Approach for the Design of Multi-Epitope Vaccine Construct against Escherichia albertii.

Escherichia albertii is characterized as an emerging pathogen, causing enteric infections. It is responsible for high mortality rate, especially in children, elderly, and immunocompromised people. To the best of our knowledge, no vaccine exists to curb this pathogen. Therefore, in current study, we aimed to identify potential vaccine candidates and design chimeric vaccine models against Escherichia albertii from the analysis of publicly available data of 95 strains, using a reverse vaccinology approach. Outer-membrane proteins (n = 4) were identified from core genome as vaccine candidates. Eventually, outer membrane Fimbrial usher (FimD) protein was selected as a promiscuous vaccine candidate and utilized to construct a potential vaccine model. It resulted in three epitopes, leading to the design of twelve vaccine constructs. Amongst these, V6 construct was found to be highly immunogenic, non-toxic, non-allergenic, antigenic, and most stable. This was utilized for molecular docking and simulation studies against six HLA and two TLR complexes. This construct can therefore be used for pan-therapy against different strains of E. albertii and needs to be tested in vitro and in vivo.

Examining the influence of technological self-efficacy, perceived trust, security, and electronic word of mouth on ICT usage in the education sector.

The context of education has changed due to revolutionary developments in the information communication technology (ICT) industry in the post-COVID era. Innovative learning methods were introduced in the education sector to promote quality education. The students find it more convenient to use ICT tools to integrate their knowledge-seeking. China has recently paid more attention to developing and adopting electronic infrastructure. The study assesses the effect of technology self-efficacy (TSE) on ICT acceptance and implementation in China's education sector. It also analyzed the role of perceived trust, perceived security, and electronic word of mouth (eWOM) in integrating digital information sharing and interaction tools. Data is collected from 382 business students at Chinese universities. The results revealed that perceived trust mediates the relationship between TSE and the actual use of ICT tools, intention to use ICT tools for information, and intention to use ICT tools for interaction. Further, perceived security and eWOM significantly moderate the relationship between TSE and perceived trust. The findings indicate that it is essential to offer assistance and instruction to students in the educational sector so they can use ICT technology more frequently. It is also crucial for organizations to establish a supportive culture and provide the necessary technological resources to facilitate the use of ICT.

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.

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.

Pharmacokinetics and drug-likeness of anti-cancer traditional Chinese medicine: molecular docking and molecular dynamics simulation study.

MCM7 (Minichromosome Maintenance Complex Component 7) is a component of the DNA replication licensing factor, which controls DNA replication. The MCM7 protein is linked to tumor cell proliferation and has a function in the development of several human cancers. Several types of cancer may be treated by inhibiting the protein, as it is strongly produced throughout this process. Significantly, Traditional Chinese Medicine (TCM), which has a long history of clinical adjuvant use against cancer, is rapidly gaining traction as a valuable medical resource for the development of novel cancer therapies, including immunotherapy. Therefore, the goal of the research was to find small molecular therapeutic candidates against the MCM7 protein that may be used to treat human cancers. A computational-based virtual screening of 36,000 natural TCM libraries is carried out for this goal using a molecular docking and dynamic simulation technique. Thereby, ∼8 novel potent compounds i.e., ZINC85542762, ZINC95911541, ZINC85542617, ZINC85542646, ZINC85592446, ZINC85568676, ZINC85531303, and ZINC95914464 were successfully shortlisted, each having the capacity to penetrate the cell as potent inhibitors for MCM7 to curb this disorder. These selected compounds were found to have high binding affinities compared to the reference (AGS compound) i.e. < -11.0 kcal/mol. ADMET and pharmacological properties showed that none of these 8 compounds poses any toxic property (carcinogenicity) and have anti-metastatic, and anticancer activity. Additionally, MD simulations were run to assess the compounds' stability and dynamic behavior with the MCM7 complex for about 100 ns. Finally, ZINC95914464, ZINC95911541, ZINC85568676, ZINC85592446, ZINC85531303, and ZINC85542646 are identified as highly stable within the complex throughout the 100 ns simulations. Moreover, the results of binding free energy suggested that the selected virtual hits significantly bind to the MCM7 which implied these compounds may act as a potential MCM7 inhibitor. However, in vitro testing protocols are required to further support these results. Further, assessment through various lab-based trial methods can assist with deciding the action of the compound that will give options in contrast to human cancer immunotherapy.Communicated by Ramaswamy H. Sarma.

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.

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.

Application of artificial intelligence in solar and wind energy resources: a strategy to deal with environmental pollution.

Environmental pollution has become a significant concern of nations. International organizations, local authorities, and social activists try to achieve sustainable development goals (SDGs) to protect the environment. However, this cannot be achieved without acknowledging the role of advanced technology applications. Previous studies found a significant relationship between technology and energy resources. But the need to highlight the significance of artificial intelligence (AI) in dealing with inevitable environmental issues still requires more attention. This study aims to analyze the application of AI applications in predicting, developing, and implementing wind and solar energy resources through a bibliometric analysis from 1991 to 2022. It uses bilioshiny of the "bibliometrix 3.0" package of R-programming for influential core aspects and keyword analysis and VOSviewer for co-occurrence analysis. The study provides significant implications for core authors, documents, sources, affiliations, and countries. It also provides keyword analysis and a co-occurrence network to cope with the conceptual integration of the literature. It reports three significant streams of literature in clusters: AI optimization and renewable energy resources; smart renewable energy resource challenges and opportunities; deep learning and machine learning forecasting; and energy efficiency. The findings will uncover the strategic perspective of AI technology for wind and solar energy generation projects.

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.

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.

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.

Effect of social influence, environmental awareness, and safety affordance on actual use of 5G technologies among Chinese students.

5G technology continues to evolve, and its potential to revolutionize various aspects of society is becoming increasingly evident. However, the successful adoption and utilization of 5G technology depends on various factors, particularly among consumers expected to be early adopters and critical drivers of technological advancements. This study investigates the effect of social influence, environmental awareness, and safety affordance on Chinese university students' actual use of 5G (AU5G) technologies. It also analyzed the mediating role of trustworthiness and intention to use 5G (ITU5G) between them and the moderating role of facilitation conditions between trustworthiness and ITU5G. Data was collected from university students (n = 364) in Beijing and was examined employing the structural equation modelling (SEM) approach. The outcomes confirm that trustworthiness and ITU5G significantly mediate the relationship between social influence, environmental awareness, and safety affordance on AU5G technologies among Chinese students. Further, the supportive facilitation conditions strengthen the relationship between trustworthiness and ITU5G. These findings are backed by UTAUT2 models that support the technology acceptance and adoption among the users. The study outcomes can help policymakers design strategies to address potential barriers and encourage greater uptake of 5G services among university students.

Pan-Genomics of Escherichia albertii for Antibiotic Resistance Profiling in Different Genome Fractions and Natural Product Mediated Intervention: In Silico Approach.

Escherichia albertii is an emerging, enteric pathogen of significance. It was first isolated in 2003 from a pediatric diarrheal sample from Bangladesh. In this study, a comprehensive in silico strategy was followed to first list out antibiotic-resistant genes from core, accessory and unique genome fractions of 95 available genomes of E. albertii. Then, 56 drug targets were identified from the core essential genome. Finally, ZipA, an essential cell division protein that stabilizes the FtsZ protofilaments by cross-linking them and serves as a cytoplasmic membrane anchor for the Z ring, was selected for further downstream processing. It was computationally modeled using a threading approach, followed by virtual screening of two phytochemical libraries, Ayurvedic (n = 2103 compounds) and Traditional Chinese Medicine (n = 36,043 compounds). ADMET profiling, followed by PBPK modeling in the central body compartment, in a population of 250 non-diseased, 250 cirrhotic and 250 renally impaired people was attempted. ZINC85624912 from Chinese medicinal library showed the highest bioavailability and plasma retention. This is the first attempt to simulate the fate of natural products in the body through PBPK. Dynamics simulation of 20 ns for the top three compounds from both libraries was also performed to validate the stability of the compounds. The obtained information from the current study could aid wet-lab scientists to work on the scaffold of screened drug-like compounds from natural resources and could be useful in our quest for therapy against antibiotic-resistant E. albertii.

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.