Chlamydia trachomatis core genome data mining for promising novel drug targets and chimeric vaccine candidates identification

Unveiling a Comprehensive Multi-epitope Subunit Vaccine Strategy Against Salmonella subsp. enterica: Bridging Core, Subtractive Proteomics, and Immunoinformatics

Salmonella subsp. enterica (SE) presents a significant global health challenge in both developed and developing countries. Current SE vaccines have limitations, targeting specific strains and demonstrating moderate efficacy in adults, while also being unsuitable for young children and often unaffordable in regions with lower income levels where the disease is prevalent. To address these challenges, this study employed a computational approach integrating core proteomics, subtractive proteomics, and immunoinformatics to develop a universal SE vaccine and identify potential drug targets. Analysis of the core proteome of 185 SE strains revealed 1964 conserved proteins. Subtractive proteomics identified 9 proteins as potential vaccine candidates and 41 as novel drug targets. Using reverse vaccinology-based immunoinformatics, four multi-epitope-based subunit vaccine constructs (MESVCs) were designed, aiming to stimulate cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte responses. These constructs underwent comprehensive evaluations for antigenicity, immunogenicity, toxicity, hydropathicity, and physicochemical properties. Predictive modeling, refinement, and validation were conducted to determine the secondary and tertiary structures of the SE-MESVCs, followed by docking studies with MHC-I, MHC-II, and TLR4 receptors. Molecular docking assessments showed favorable binding with all three receptors, with SE-MESVC-4 exhibiting the most promising binding energy. Molecular dynamics simulations confirmed the binding affinity and stability of SE-MESVC-4 with the TLR4/MD2 complex. Additionally, codon optimization and in silico cloning verified the efficient translation and successful expression of SE-MESVC-4 in Escherichia coli (E. coli) str. K12. Subsequent in silico immune simulation evaluated the efficacy of SE-MESVC-4 in triggering an effective immune response. These results suggest that SE-MESVC-4 may induce both humoral and cellular immune responses, making it a potential candidate for an effective SE vaccine. However, further experimental investigations are necessary to validate the immunogenicity and efficacy of SE-MESVC-4, bringing us closer to effectively combating SE infections.

Development of a subunit vaccine against the cholangiocarcinoma causing Opisthorchis viverrini: a computational approach

Opisthorchis viverrini is the etiological agent of the disease opisthorchiasis and related cholangiocarcinoma (CCA). It infects fish-eating mammals and more than 10 million people in Southeast Asia suffered from opisthorchiasis with a high fatality rate. The only effective drug against this parasite is Praziquantel, which has significant side effects. Due to the lack of appropriate treatment options and the high death rate, there is a dire need to develop novel therapies against this pathogen. In this study, we designed a multi-epitope chimeric vaccine design against O. viverrini by using immunoinformatics approaches. Non-allergenic and immunogenic MHC-1, MHC-2, and B cell epitopes of three candidate proteins thioredoxin peroxidase (Ov-TPx-1), cathepsin F1 (Ov-CF-1) and calreticulin (Ov-CALR) of O. viverrini, were predicted to construct a potent multiepitope vaccine. The coverage of the HLA-alleles of these selected epitopes was determined globally. Four vaccine constructs made by different adjuvants and linkers were evaluated in the context of their physicochemical properties, antigenicity, and allergenicity. Protein-protein docking and MD simulation found that vaccines 3 was more stable and had a higher binding affinity for TLR2 and TLR4 immune receptors. In-silico restriction cloning of vaccine model led to the formation of plasmid constructs for expression in a suitable host. Finally, the immune simulation showed strong immunological reactions to the engineered vaccine. These findings suggest that the final vaccine construct has the potential to be validated by in vivo and in vitro experiments to confirm its efficacy against the CCA causing O. viverrini.

Decrypting the multi-genome data for chimeric vaccine designing against the antibiotic resistant Yersinia pestis

Yersinia pestis, the causative agent of plague, is a gram-negative bacterium that can be fatal if not treated properly. Three types of plague are currently known: bubonic, septicemic, and pneumonic plague, among which the fatality rate of septicemic and pneumonic plague is very high. Bubonic plague can be treated, but only if antibiotics are used at the initial stage of the infection. But unfortunately, Y. pestis has also shown resistance to certain antibiotics such as kanamycin, minocycline, tetracycline, streptomycin, sulfonamides, spectinomycin, and chloramphenicol. Despite tremendous progress in vaccine development against Y. pestis, there is no proper FDA-approved vaccine available to protect people from its infections. Therefore, effective broad-spectrum vaccine development against Y. pestis is indispensable. In this study, vaccinomics-assisted immunoinformatics techniques were used to find possible vaccine candidates by utilizing the core proteome prepared from 58 complete genomes of Y. pestis. Human non-homologous, pathogen-essential, virulent, and extracellular and membrane proteins are potential vaccine targets. Two antigenic proteins were prioritized for the prediction of lead epitopes by utilizing reverse vaccinology approaches. Four vaccine designs were formulated using the selected B- and T-cell epitopes coupled with appropriate linkers and adjuvant sequences capable of inducing potent immune responses. The HLA allele population coverage of the T-cell epitopes selected for vaccine construction was also analyzed. The V2 constructs were top-ranked and selected for further analysis on the basis of immunological, physicochemical, and immune-receptor docking interactions and scores. Docking and molecular dynamic simulations confirmed the stability of construct V2 interactions with the host immune receptors. Immune simulation analysis anticipated the strong immune profile of the prioritized construct. In silico restriction cloning ensured the feasible cloning ability of the V2 construct in the expression system of E. coli strain K12. It is anticipated that the designed vaccine construct may be safe, effective, and able to elicit strong immune responses against Y. pestis infections and may, therefore, merit investigation using in vitro and in vivo assays.

Analysis and prediction of interactions between transmembrane and non-transmembrane proteins

BACKGROUND

Most of the important biological mechanisms and functions of transmembrane proteins (TMPs) are realized through their interactions with non-transmembrane proteins(nonTMPs). The interactions between TMPs and nonTMPs in cells play vital roles in intracellular signaling, energy metabolism, investigating membrane-crossing mechanisms, correlations between disease and drugs.

RESULTS

Despite the importance of TMP-nonTMP interactions, the study of them remains in the wet experimental stage, lacking specific and comprehensive studies in the field of bioinformatics. To fill this gap, we performed a comprehensive statistical analysis of known TMP-nonTMP interactions and constructed a deep learning-based predictor to identify potential interactions. The statistical analysis describes known TMP-nonTMP interactions from various perspectives, such as distributions of species and protein families, enrichment of GO and KEGG pathways, as well as hub proteins and subnetwork modules in the PPI network. The predictor implemented by an end-to-end deep learning model can identify potential interactions from protein primary sequence information. The experimental results over the independent validation demonstrated considerable prediction performance with an MCC of 0.541.

CONCLUSIONS

To our knowledge, we were the first to focus on TMP-nonTMP interactions. We comprehensively analyzed them using bioinformatics methods and predicted them via deep learning-based solely on their sequence. This research completes a key link in the protein network, benefits the understanding of protein functions, and helps in pathogenesis studies of diseases and associated drug development.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Deciphering the Immunogenicity of Monkeypox Proteins for Designing the Potential mRNA Vaccine

The Monkeypox virus (MPXV), an orthopox virus, is responsible for monkeypox in humans, a zoonotic disease similar to smallpox. This infection first appeared in the 1970s in humans and then in 2003, after which it kept on spreading all around the world. To date, various antivirals have been used to cure this disease, but now, MPXV has developed resistance against these, thus increasing the need for an alternative cure for this deadly disease. In this study, we devised a reverse vaccinology approach against MPXV using a messenger RNA (mRNA) vaccine by pinning down the antigenic proteins of this virus. By using bioinformatic tools, we predicted prospective immunogenic B and T lymphocyte epitopes. Based on cytokine inducibility score, nonallergenicity, nontoxicity, antigenicity, and conservancy, the final epitopes were selected. Our analysis revealed the stable structure of the mRNA vaccine and its efficient expression in host cells. Furthermore, strong interactions were demonstrated with toll-like receptors 2 (TLR2) and 4 (TLR4) according to the molecular dynamic simulation studies. The in silico immune simulation analyses revealed an overall increase in the immune responses following repeated exposure to the designed vaccine. Based on our findings, the vaccine candidate designed in this study has the potential to be tested as a promising novel mRNA therapeutic vaccine against MPXV infection.

Structural informatics approach for designing an epitope-based vaccine against the brain-eating Naegleria fowleri

Primary Amoebic Meningoencephalitis (PAM), a severe lethal brain disease, is caused by a parasite, Naegleria fowleri, also known as the "brain-eating amoeba". The chances of a patient's recovery after being affected by this parasite are very low. Only 5% of people are known to survive this life-threatening infection. Despite the fact that N. fowleri causes a severe, fatal infection, there is no proper treatment available to prevent or cure it. In this context, it is necessary to formulate a potential vaccine that could be able to combat N. fowleri infection. The current study aimed at developing a multi-epitope subunit vaccine against N. fowleri by utilizing immunoinformatics techniques and reverse vaccinology approaches. The T- and B-cell epitopes were predicted by various tools. In order to choose epitopes with the ability to trigger both T- and B-cell-mediated immune responses, the epitopes were put through a screening pipeline including toxicity, antigenicity, cytokine-inductivity, and allergenicity analysis. Three vaccine constructs were designed from the generated epitopes linked with linkers and adjuvants. The modeled vaccines were docked with the immune receptors, where vaccine-1 showed the highest binding affinity. Binding affinity and stability of the docked complex were confirmed through normal mode analysis and molecular dynamic simulations. Immune simulations developed the immune profile, and in silico cloning affirmed the expression probability of the vaccine construct in Escherichia coli (E. coli) strain K12. This study demonstrates an innovative preventative strategy for the brain-eating amoeba by developing a potential vaccine through immunoinformatics and reverse vaccinology approaches. This study has great preventive potential for Primary Amoebic Meningoencephalitis, and further research is required to assess the efficacy of the designed vaccine.

Proteome level analysis of drug-resistant Prevotella melaninogenica for the identification of novel therapeutic candidates

The management of infectious diseases has become more critical due to the development of novel pathogenic strains with enhanced resistance. Prevotella melaninogenica, a gram-negative bacterium, was found to be involved in various infections of the respiratory tract, aerodigestive tract, and gastrointestinal tract. The need to explore novel drug and vaccine targets against this pathogen was triggered by the emergence of antimicrobial resistance against reported antibiotics to combat P. melaninogenica infections. The study involves core genes acquired from 14 complete P. melaninogenica strain genome sequences, where promiscuous drug and vaccine candidates were explored by state-of-the-art subtractive proteomics and reverse vaccinology approaches. A stringent bioinformatics analysis enlisted 18 targets as novel, essential, and non-homologous to humans and having druggability potential. Moreover, the extracellular and outer membrane proteins were subjected to antigenicity, allergenicity, and physicochemical analysis for the identification of the candidate proteins to design multi-epitope vaccines. Two candidate proteins (ADK95685.1 and ADK97014.1) were selected as the best target for the designing of a vaccine construct. Lead B- and T-cell overlapped epitopes were joined to generate potential chimeric vaccine constructs in combination with adjuvants and linkers. Finally, a prioritized vaccine construct was found to have stable interactions with the human immune cell receptors as confirmed by molecular docking and MD simulation studies. The vaccine construct was found to have cloning and expression ability in the bacterial cloning system. Immune simulation ensured the elicitation of significant immune responses against the designed vaccine. In conclusion, our study reported novel drug and vaccine targets and designed a multi-epitope vaccine against the P. melaninogenica infection. Further experimental validation will help open new avenues in the treatment of this multi-drug-resistant pathogen.

Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach

Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine.

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.

Computer-aided genomic data analysis of drug-resistant Neisseria gonorrhoeae for the Identification of alternative therapeutic targets

Neisseria gonorrhoeae is an emerging multidrug resistance pathogen that causes sexually transmitted infections in men and women. The N. gonorrhoeae has demonstrated an emerging antimicrobial resistance against reported antibiotics, hence fetching the attention of researchers to address this problem. The present in-silico study aimed to find putative novel drug and vaccine targets against N. gonorrhoeae infection by the application of bioinformatics approaches. Core genes set of 69 N. gonorrhoeae strains was acquired from complete genome sequences. The essential and non-homologous metabolic pathway proteins of N. gonorrhoeae were identified. Moreover, different bioinformatics databases were used for the downstream analysis. The DrugBank database scanning identified 12 novel drug targets in the prioritized list. They were preferred as drug targets against this bacterium. A viable vaccine is unavailable so far against N. gonorrhoeae infection. In the current study, two outer-membrane proteins were prioritized as vaccine candidates via reverse vaccinology approach. The top lead B and T-cells overlapped epitopes were utilized to generate a chimeric vaccine construct combined with immune-modulating adjuvants, linkers, and PADRE sequences. The top ranked prioritized vaccine construct (V7) showed stable molecular interaction with human immune cell receptors as inferred during the molecular docking and MD simulation analyses. Considerable response for immune cells was interpreted by in-silico immune studies. Additional tentative validation is required to ensure the effectiveness of the prioritized vaccine construct against N. gonorrhoeae infection. The identified proteins can be used for further rational drug and vaccine designing to develop potential therapeutic entities against the multi-drug resistant N. gonorrhoeae.

Drug-target binding affinity prediction method based on a deep graph neural network

The development of new drugs is a long and costly process, Computer-aided drug design reduces development costs while computationally shortening the new drug development cycle, in which DTA (Drug-Target binding Affinity) prediction is a key step to screen out potential drugs. With the development of deep learning, various types of deep learning models have achieved notable performance in a wide range of fields. Most current related studies focus on extracting the sequence features of molecules while ignoring the valuable structural information; they employ sequence data that represent only the elemental composition of molecules without considering the molecular structure maps that contain structural information. In this paper, we use graph neural networks to predict DTA based on corresponding graph data of drugs and proteins, and we achieve competitive performance on two benchmark datasets, Davis and KIBA. In particular, an MSE of 0.227 and CI of 0.895 were obtained on Davis, and an MSE of 0.127 and CI of 0.903 were obtained on KIBA.

Therapeutic Options for Chlamydia trachomatis Infection: Present and Future

Sexually transmitted infections (STIs), such as Chlamydia trachomatis (Ct) infection, have serious consequences for sexual and reproductive health worldwide. Ct is one of the most common sexually transmitted bacterial infections in the world, with approximately 129 million new cases per year. C. trachomatis is an obligate intracellular Gram-negative bacterium. The infection is usually asymptomatic, notwithstanding, it could also be associated with severe sequels and complications, such as chronic pain, infertility, and gynecologic cancers, and thus there is an urgent need to adequately treat these cases in a timely manner. Consequently, beyond its individual effects, the infection also impacts the economy of the countries where it is prevalent, generating a need to consider the hypothesis of implementing Chlamydia Screening Programs, a decision that, although it is expensive to execute, is a necessary investment that unequivocally will bring financial and social long-term advantages worldwide. To detect Ct infection, there are different methodologies available. Nucleic acid amplification tests, with their high sensitivity and specificity, are currently the first-line tests for the detection of Ct. When replaced by other detection methods, there are more false negative tests, leading to underreported cases and a subsequent underestimation of Ct infection's prevalence. Ct treatment is based on antibiotic prescription, which is highly associated with drug resistance. Therefore, currently, there have been efforts in line with the development of alternative strategies to effectively treat this infection, using a drug repurposing method, as well as a natural treatment approach. In addition, researchers have also made some progress in the Ct vaccine development over the years, despite the fact that it also necessitates more studies in order to finally establish a vaccination plan. In this review, we have focused on the therapeutic options for treating Ct infection, expert recommendations, and major difficulties, while also exploring the possible avenues through which to face this issue, with novel approaches beyond those proposed by the guidelines of Health Organizations.

Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

This work is conducted to deal with the information security of enterprise management under the background of current global informatization, popularize the modern Internet of Things (IoT) management technology of enterprises, and maintain the information security of enterprises and provide modern upgrading means for enterprise management. In this work, it firstly introduces the application scenarios of current Internet and Artificial Intelligence (AI) technology and expounds the IoT technology. Secondly, the enterprise management platform is designed, the requirements of enterprise modern management are analyzed, and then the design requirements of system functions and the design of the information security architecture of the IoT are proposed. Furthermore, an enterprise information security management platform is designed, which covers four parts: IoT data mining management, equipment management, key management, and database management. In addition, the performance of the security management platform is tested from four parts: concurrency testing, stress testing, large data volume testing, and security testing. The research results show that the IoT-based enterprise information security management platform designed in this work under the background of AI has perfect functions and stable performance of each module. Concurrency testing, stress testing, large data volume testing, and stability testing are performed on it, and the success rate of the platform in each task reaches 100%. The average response time of concurrent testing and stress testing is about 0.13 seconds, and that of the event entry events is 0.25 seconds. The central processing unit (CPU) occupancy rate in each monitoring task is always lower than 20%. Therefore, it is determined that the performance of the IoT-based enterprise information security management platform designed in this work is sufficient to meet the daily management of enterprises. This work can provide a guarantee for enterprise information security management using AI technology, setting an example for future related research.