iMod: multipurpose normal mode analysis in internal coordinates

Designing of new trans-stilbene derivative: An entry barrier of Zika virus in host cell

A large population in the world lives in tropical and subtropical regions, showing a high risk of Zika viral infection which leads to a situation of global health emergency and demands extensive research to create effective antiviral medicines. Herein, we introduce the design of a new derivatized trans-stilbene molecule to investigate the inhibition of Zika virus entry into the host cell by molecular docking approach. The synthesized compound has been characterized by different analytical techniques such as FTIR, 1H NMR,13C NMR and UV-visible spectroscopy as well as Mass spectrometry (MS). Moreover, the complete structure elucidation was achieved via X-ray crystallography and DFT analysis. The article describes the life cycle and genome of the Zika virus along with its mechanism of entry inhibition by illustrating the structure and function of the ZIKV envelop (E) protein. The docking studies disclosed that the newly synthesized stilbene compound confers an excellent inhibitory response towards the entry of Zika virus in host cells as supported by calculated docking score and its binding conformation with Zika virus E-protein. Further, the normal mode analysis (NMA) simulation technique is used to predict the conformational states of the target E-protein, which explains the potency of the compound to bind with the Zika virus E-protein. We hope that the present study will help and encourage researchers in the field of medicinal chemistry to develop potential drugs against the Zika virus.

Computational and GC-MS screening of bioactive compounds from Thymus Vulgaris targeting mycolactone protein associated with Buruli ulcer

Buruli ulcer (BU) a neglected disease induced by the bacterium Mycobacterium ulcerans, predominantly impacts tropical and subtropical areas with its pathophysiology ascribed to the Mycolactone protein. Current antibiotics frequently prove insufficient to manage advanced or chronic ulcers and the rise of drug resistance presents a considerable challenge. This work aims to address these challenges by employing computational methods to identify therapeutic candidates from organic compounds, which may be developed into more effective therapies for Buruli ulcer. The Gas-Chromatography Mass Spectrometry (GCMS) analysis of the Thymus Vulgaris identified the 29 bioactive compounds as potential drug candidates having different medicinal properties. Out of the 29 compounds against the mycolactone protein, 14 compounds demonstrated a binding affinity higher than - 6 kcal/mol predicted through PyRx. Among all compounds, gamma sitosterol and borneol showed the highest binding affinity - 7.7 kcal/mol. The ADMET analysis predicted that the compound borneol crosses the PGP + through the Blood Brain Barrier and gastrointestinal tract without violating Lipinski's rule of 5 having high water solubility, and log p-value of 2.29. The molecular dynamic simulation was performed and showed the Eigenvalue of 1.332692e-04. The leads identified in the study have demonstrated encouraging outcomes with regard to their efficacy, toxicity, pharmacokinetics, and safety. Further experimental investigations can be conducted to evaluate their anti-bacterial activity, and their molecular frameworks could be utilized as a valuable foundation for designing new drugs for the treatment of Buruli ulcer.

Exploration of novel triazolo-thiadiazine hybrids of deferasirox as multi-target-directed anti-neuroinflammatory agents with structure-activity relationship (SAR): a new treatment opportunity for Alzheimer's disease

It is believed that inflammation influences several physiological processes, including the function of the central nervous system. Moreover, the impairment of lipid mechanisms/pathways is associated with neurodegenerative disorders and onset of Alzheimer's disease (AD). AD is a chronic neurodegenerative disease representing the major cause of dementia worldwide. In this case, the overexpression of different pharmacological targets has been confirmed to address neuronal inflammation and AD, with acetylcholinesterase (AChE), monoamine oxidase-B (MAO-B), cyclooxygenase-2 (COX-2) and 5-lipoxygenase (LOX-5) being the most explored targets. Currently, the available treatments are only capable of alleviating the symptoms and not capable of delivering disease-modifying effects. Thus, the current research objective is to synthesize triazolo-thiadiazine derivatives of the deferasirox drug as multi-target compounds that could concurrently inhibit ChEs, MAOs, LOX-5 and COX-2. The synthesized derivatives were confirmed by FTIR, 1H NMR, 13C NMR and DEPT-135 spectroscopic techniques. During in vitro investigations, compound 11 was found to be the most potent inhibitor of all the targeted enzymes. Briefly, this compound exhibited inhibitory values (IC50 ± SEM) of 0.31 ± 0.16, 0.13 ± 0.16 and 0.94 ± 0.16 μM against AChE, MAO-B and COX-2, respectively, suggesting that it is a lead molecule for the synthesis of more potential multi-targeted inhibitors. Several compounds, such as compound 9 and 13, showed dual inhibition potential in comparison to standard drugs. Furthermore, molecular docking analysis was performed to validate the in vitro results, where the potent compounds showed some significant interactions with the key amino acids present in the active site of the targeted enzymes. Furthermore, molecular dynamics (MD) simulation data and physicochemical properties supported deferasirox-substituted triazolo-thiadiazine as a promising horizon for the discovery and development of new molecules to treat multifactorial diseases associated with neuro-inflammation, such as AD.

In silico design and assessment of a multi-epitope peptide vaccine against multidrug-resistant Acinetobacter baumannii

Acinetobacter baumannii, an opportunistic and notorious nosocomial pathogen, is responsible for many infections affecting soft tissues, skin, lungs, bloodstream, and urinary tract, accounting for more than 722,000 cases annually. Despite the numerous advancements in therapeutic options, no approved vaccine is currently available for this particular bacterium. Consequently, this study focused on creating a rational vaccine design using bioinformatics tools. Three outer membrane proteins with immunogenic potential and properties of good vaccine candidates were used to select epitopes based on good antigenic properties, non-allergenicity, high binding scores, and a low IC50 value. A multi-epitope peptide (MEP) construct was created by sequentially linking the epitopes using suitable linkers. ClusPro 2.0 and C-ImmSim web servers were used for docking analysis with TLR2/TLR4 and immune response respectively. The Ramachandran plot showed an accurate model of the MEP with 100% residue in the most favored and allowed regions. The construct was highly antigenic, stable, non-allergenic, non-toxic, and soluble, and showed maximum population coverage. Additionally, molecular docking demonstrated strong binding between the designed MEP vaccine and TLR2/TLR4. In silico immunological simulations showed significant increases in T-cell and B-cell populations. Finally, codon optimization and in silico cloning were conducted using the pET-28a (+) plasmid vector to evaluate the efficiency of the expression of vaccine peptide in the host organism (Escherichia coli). This designed MEP vaccine would support and accelerate the laboratory work to develop a potent vaccine targeting MDR Acinetobacter baumannii.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00292-3.

ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning

The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of normal mode correlation maps derived from elastic network models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the normal mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.

Targeting serotonin receptors with phytochemicals - an in-silico study

The potential of natural phytochemicals in mitigating depression has been supported by substantial evidence. This study evaluated a total of 88 natural phytochemicals with potential antidepressant properties by targeting serotonin (5-HT) receptors (5-HT1A, 5-HT4, and 5-HT7) using molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, internal coordinates normal mode analysis (NMA), molecular dynamics simulation (MDS), and free energy calculation. Five evaluated compounds (Genistein, Kaempferol, Daidzein, Peonidin, and glycitein) exhibited favorable pharmacokinetic properties and improved binding scores, indicating their potential as effective antidepressants. Redocking and superimposition analysis of 5-HT with cocrystal structures validated these findings. Furthermore, NMA, MDS, and free energy calculations confirmed the stability and deformability of the ligand-receptor complexes, suggesting that these phytochemicals can effectively interact with 5-HT receptors to modulate depressive symptoms. These powerful phytochemicals, abundantly found in soybeans, fruits, vegetables, and herbs, represent a promising avenue for developing natural treatments for depression. Further in vitro and in vivo studies are warranted to explore their efficacy in alleviating stress and depression through their interactions with 5-HT receptors.

Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate

Background

Tuberculosis (TB) remains a global public health challenge. The existing Bacillus Calmette-Guérin vaccine has limited efficacy in preventing adult pulmonary TB, necessitating the development of new vaccines with improved protective effects.

Methods

Computer-aided design and artificial intelligence technologies, combined with bioinformatics and immunoinformatics approaches, were used to design a multi-epitope vaccine (MEV) against TB. Comprehensive bioinformatics analyses were conducted to evaluate the physicochemical properties, spatial structure, immunogenicity, molecular dynamics (MD), and immunological characteristics of the MEV.

Results

We constructed a MEV, designated ZL12138L, containing 13 helper T lymphocyte epitopes, 12 cytotoxic T lymphocyte epitopes, 8 B-cell epitopes, as well as Toll-like receptor (TLR) agonists and helper peptides. Bioinformatics analyses revealed that ZL12138L should exhibit excellent immunogenicity and antigenicity, with no toxicity or allergenicity, and had potential to induce robust immune responses and high solubility, the immunogenicity score was 4.14449, the antigenicity score was 0.8843, and the immunological score was 0.470. Moreover, ZL12138L showed high population coverage for human leukocyte antigen class I and II alleles, reaching 92.41% and 90.17%, respectively, globally. Molecular docking analysis indicated favorable binding affinity of ZL12138L with TLR-2 and TLR-4, with binding energies of -1173.4 and -1360.5 kcal/mol, respectively. Normal mode analysis and MD simulations indicated the stability and dynamic properties of the vaccine construct. Immune simulation predictions suggested that ZL12138L could effectively activate innate and adaptive immune cells, inducing high levels of Type 1 T helper cell cytokines.

Conclusions

This study provides compelling evidence for ZL12138L as a promising TB vaccine candidate. Future research will focus on experimental validation and further optimization of the vaccine design.

Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics

In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.

Molecular docking and antimicrobial activities of photoexcited inhibitors in antimicrobial photodynamic therapy against Enterococcus faecalis biofilms in endodontic infections

Antimicrobial photodynamic therapy (aPDT) is a promising approach to combat antibiotic resistance in endodontic infections. It eliminates residual bacteria from the root canal space and reduces the need for antibiotics. To enhance its effectiveness, an in silico and in vitro study was performed to investigate the potential of targeted aPDT using natural photosensitizers, Kojic acid and Parietin. This approach aims to inhibit the biofilm formation of Enterococcus faecalis, a frequent cause of endodontic infections, by targeting the Ace and Esp proteins. After determining the physicochemical characteristics of Ace and Esp proteins and model quality assessment, the molecular dynamic simulation was performed to recognize the structural variations. The stability and physical movement of the protein-ligand complexes were evaluated. In silico molecular docking was conducted, followed by ADME/Tox profiling, pharmacokinetics characteristics, and assessment of drug-likeness properties of the natural photosensitizers. The study also investigated the changes in the expression of genes (esp and ace) involved in E. faecalis biofilm formation. The results showed that both Kojic acid and Parietin complied with Lipinski's rule of five and exhibited drug-like properties. In silico analysis indicated stable complexes between Ace and Esp proteins and the natural photosensitizers. The molecular docking studies demonstrated good binding affinity. Additionally, the expression of the ace and esp genes was significantly downregulated in aPDT using Kojic acid and Parietin with blue light compared to the control group. This investigation concluded that Kojic acid and Parietin with drug-likeness could efficiently interact with Ace and Esp proteins with a strong binding affinity. Hence, natural photosensitizers-mediated aPDT can be considered a promising adjunctive treatment against endodontic infections.

Design of a Helicobacter pylori multi-epitope vaccine based on immunoinformatics

Helicobacter pylori (H. pylori) is an infectious bacterium that colonizes the stomach of approximately half of the global population. It has been classified as a Group I carcinogen by the World Health Organization due to its strong association with an increased incidence of gastric cancer and exacerbation of stomach diseases. The primary treatment for H. pylori infection currently involves triple or quadruple therapy, primarily consisting of antibiotics and proton pump inhibitors. However, the increasing prevalence of antibiotic resistance poses significant challenges to this approach, underscoring the urgent need for an effective vaccine. In this study, a novel multi-epitope H. pylori vaccine was designed using immunoinformatics. The vaccine contains epitopes derived from nine essential proteins. Software tools and online servers were utilized to predict, evaluate, and analyze the physiochemical properties, secondary and tertiary structures, and immunogenicity of the candidate vaccine. These comprehensive assessments ultimately led to the formulation of an optimal design scheme for the vaccine. Through constructing a novel multi-epitope vaccine based on immunoinformatics, this study offers promising prospects and great potential for the prevention of H. pylori infection. This study also provides a reference strategy to develop multi-epitope vaccines for other pathogens.

aXonica: A support package for MRI based Neuroimaging

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for 'free for use' tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with 'aXonica,' an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.

Immunoinformatics approach for design novel multi-epitope prophylactic and therapeutic vaccine based on capsid proteins L1 and L2 and oncoproteins E6 and E7 of human papillomavirus 16 and human papillomavirus 18 against cervical cancer

BACKGROUND

This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process involved using capsid epitopes L1 and L2, as well as oncoproteins E5, E6, and E7 from human papillomavirus (HPV) types 16 and 18.

METHODS

An experimental in silico analysis with an immunoinformatics approach was used to develop 2 multi-epitope vaccine constructs (A and B). Further analysis was then conducted to compare the constructs and select the one with the highest potential against cervical cancer.

RESULTS

This study produced 2 antigenic, non-allergenic, and nontoxic multi-epitope vaccine constructs (A and B), which exhibited the ideal physicochemical properties for a vaccine. Further analysis revealed that construct B effectively induced both cellular and humoral immune responses.

CONCLUSION

The multi-epitope vaccine construct B for HPV 16 and 18, designed for both prophylactic and therapeutic purposes, met the development criteria for a cervical cancer vaccine. However, these findings need to be validated through in vitro and in vivo experiments.

Reverse vaccinology-based multi-epitope vaccine design against Indian group A rotavirus targeting VP7, VP4, and VP6 proteins

Rotavirus, a primary contributor to severe cases of infantile gastroenteritis on a global scale, results in significant morbidity and mortality in the under-five population, particularly in middle to low-income countries, including India. WHO-approved live-attenuated vaccines are linked to a heightened susceptibility to intussusception and exhibit low efficacy, primarily attributed to the high genetic diversity of rotavirus, varying over time and across different geographic regions. Herein, molecular data on Indian rotavirus A (RVA) has been reviewed through phylogenetic analysis, revealing G1P[8] to be the prevalent strain of RVA in India. The conserved capsid protein sequences of VP7, VP4 and VP6 were used to examine helper T lymphocyte, cytotoxic T lymphocyte and linear B-cell epitopes. Twenty epitopes were identified after evaluation of factors such as antigenicity, non-allergenicity, non-toxicity, and stability. These epitopes were then interconnected using suitable linkers and an N-terminal beta defensin adjuvant. The in silico designed vaccine exhibited structural stability and interactions with integrins (αvβ3 and αIIbβ3) and toll-like receptors (TLR2 and TLR4) indicated by docking and normal mode analyses. The immune simulation profile of the designed RVA multiepitope vaccine exhibited its potential to trigger humoral as well as cell-mediated immunity, indicating that it is a promising immunogen. These computational findings indicate potential efficacy of the designed vaccine against rotavirus infection.

Super epitope dengue vaccine instigated serotype independent immune protection in-silico

Dengue becomes the most common life-threatening infectious arbovirus disease globally, with prevalence in the tropical and subtropical areas. The major clinical features include dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS), a condition of hypovolemic shock. Four different serotypes of the dengue virus, known as dengue virus serotype (DENV)- 1, 2, 3 and 4 can infect humans. Only one vaccine is available in the market, named Dengvaxia by Sanofi Pasteur, but there is no desired outcome of this treatment due the antibody dependent enhancement (ADE) of the multiple dengue serotypes. As of now, there is no cure against dengue disease. Our goal in this work was to create a subunit vaccine based on several epitopes that would be effective against every serotype of the dengue virus. Here, computational methods like- immunoinformatics and bioinformatics were implemented to find out possible dominant epitopes. A total of 21 epitopes were chosen using various in-silico techniques from the expected 133 major histocompatibility complex (MHC)- I and major histocompatibility complex (MHC)- II epitopes, along with 95 B-cell epitopes which were greatly conserved. Immune stimulant, non-allergenic and non-toxic immunodominant epitopes (super epitopes) with a suitable adjuvant (Heparin-Binding Hemagglutinin Adhesin, HBHA) were used to construct the vaccine. Following the physicochemical analysis, vaccine construct was docked with Toll-like receptors (TLRs) to predict the immune stimulation. Consequently, the optimal docked complex that demonstrated the least amount of ligand-receptor complex deformability was used to conduct the molecular dynamics analysis. By following the codon optimization, the final vaccine molecule was administered into an expressing vector to perform in-silico cloning. The robust immune responses were generated in the in-silico immune simulation analysis. Hence, this study provides a hope to control the dengue infections. For validation of the immune outcomes, in-vitro as well as in-vivo investigations are essential.

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.

Identification of novel T-cell epitopes on monkeypox virus and development of multi-epitopes vaccine using immunoinformatics approaches

Monkeypox virus (MPV) is closely related to the smallpox virus, and previous data from Africa suggest that the smallpox vaccine (VARV) is at least 85% effective in preventing MPV. No multi-epitope vaccine has yet been developed to prevent MPV infection. In this work, we used in silico structural biology and advanced immunoinformatic strategies to design a multi-epitope subunit vaccine against MPV infection. The designed vaccine sequence is adjuvanted with CpG-ODN and includes HTL/CTL epitopes for similar proteins between vaccinia virus (VACV) that induced T-cell production in vaccinated volunteers and the first draft sequence of the MPV genome associated with the suspected outbreak in several countries, May 2022. In addition, the specific binding of the modified vaccine and the immune Toll-like receptor 9 (TLR9) was estimated by molecular interaction studies. Strong interaction in the binding groove as well as good docking scores confirmed the stringency of the modified vaccine. The stability of the interaction was confirmed by a classical molecular dynamics simulation and normal mode analysis. Then, the immune simulation also indicated the ability of this vaccine to induce an effective immune response against MPV. Codon optimization and in silico cloning of the vaccine into the pET-28a (+) vector also showed its expression potential in the E. coli K12 system. The promising data obtained from the various in silico studies indicate that this vaccine is effective against MPV. However, additional in vitro and in vivo studies are still needed to confirm its efficacy.Communicated by Ramaswamy H. Sarma.

Structural and functional insights into a novel homozygous missense pathogenic variant in CUL7 identified in consanguineous Pakistani family

3M syndrome is a rare genetic familial disorder characterized by short stature, growth retardation, facial dysmorphism, skeletal abnormalities, fleshy protruding heels, and normal intelligence, caused by mutations in the CUL7, OBSL1 and CCDC8 genes. In the present study, a novel homozygous missense variant of CUL7 (NP_001161842.1, c.4493T > C, p.L1498P) has been identified in a consanguineous Pakistani family by whole exome sequencing. In silico structural evaluation, molecular docking and simulation studies of mutant CUL7 provides substantial evidence about its crucial role in the progression of discussed ailment. The newly discovered variant significantly altered the protein's three dimensional structure, leading to abnormal interaction with binding proteins. This computational and experimental investigation provides useful information to drug developers for the synthesis of novel therapeutics against the discussed ailment.Communicated by Ramaswamy H. Sarma.

In silico designing of novel epitope-based peptide vaccines against HIV-1

The HIV-1 virus has been regarded as a catastrophe for human well-being. The global incidence of HIV-1-infected individuals is increasing. Hence, development of effective immunostimulatory molecules has recently attracted an increasing attention in the field of vaccine design against HIV-1 infection. In this study, we explored the impacts of CD40L and IFN-γ as immunostimulatory adjuvants for our candidate HIV-1 Nef vaccine in human and mouse using immunoinformatics analyses. Overall, 18 IFN-γ-based vaccine constructs (9 constructs in human and 9 constructs in mouse), and 18 CD40L-based vaccine constructs (9 constructs in human and 9 constructs in mouse) were designed. To find immunogenic epitopes, important characteristics of each component (e.g., MHC-I and MHC-II binding, and peptide-MHC-I/MHC-II molecular docking) were determined. Then, the selected epitopes were applied to create multiepitope constructs. Finally, the physicochemical properties, linear and discontinuous B cell epitopes, and molecular interaction between the 3D structure of each construct and CD40, IFN-γ receptor or toll-like receptors (TLRs) were predicted. Our data showed that the full-length CD40L and IFN-γ linked to the N-terminal region of Nef were capable of inducing more effective immune response than multiepitope vaccine constructs. Moreover, molecular docking of the non-allergenic full-length- and epitope-based CD40L and IFN-γ constructs to their cognate receptors, CD40 and IFN-γ receptors, and TLRs 4 and 5 in mouse were more potent than in human. Generally, these findings suggest that the full forms of these adjuvants could be more efficient for improvement of HIV-1 Nef vaccine candidate compared to the designed multiepitope-based constructs.

Multifunctional in vitro, in silico and DFT analyses on antimicrobial BagremycinA biosynthesized by Micromonospora chokoriensis CR3 from Hieracium canadense

Among the actinomycetes in the rare genera, Micromonospora is of great interest since it has been shown to produce novel therapeutic compounds. Particular emphasis is now on its isolation from plants since its population from soil has been extensively explored. The strain CR3 was isolated as an endophyte from the roots of Hieracium canadense, and it was identified as Micromonospora chokoriensis through 16S gene sequencing and phylogenetic analysis. The in-vitro analysis of its extract revealed it to be active against the clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) and Candida tropicalis (15 mm). No bioactivity was observed against Gram-negative bacteria, Escherichia coli ATCC 25922, and Klebsiella pneumoniae ATCC 706003. The Micromonospora chokoriensis CR3 extract was also analyzed through the HPLC-DAD-UV-VIS resident database, and it gave a maximum match factor of 997.334 with the specialized metabolite BagremycinA (BagA). The in-silico analysis indicated that BagA strongly interacted with the active site residues of the sterol 14-α demethylase and thymidylate kinase enzymes, with the lowest binding energies of - 9.7 and - 8.3 kcal/mol, respectively. Furthermore, the normal mode analysis indicated that the interaction between these proteins and BagA was stable. The DFT quantum chemical properties depicted BagA to be reasonably reactive with a HOMO-LUMO gap of (ΔE) of 4.390 eV. BagA also passed the drug-likeness test with a synthetic accessibility score of 2.06, whereas Protox-II classified it as a class V toxicity compound with high LD50 of 2644 mg/kg. The current study reports an endophytic actinomycete, M. chokoriensis, associated with H. canadense producing the bioactive metabolite BagA with promising antimicrobial activity, which can be further modified and developed into a safe antimicrobial drug.

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

Interaction of zincite, alpha-terpineol, geranyl acetate, linalool, myrcenol, terpinolene, and thymol with virulence factors of Escherichia coli, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Staphylococcus aureus

BACKGROUND

Based on gas chromatography - mass spectrometry (GC-MS) results of a previous study, six metabolites including alpha-terpineol, geranyl acetate, linalool, myrcenol, terpinolene, and thymol showed significantly higher amounts relative to other metabolites.

METHODS

A continuation of the previous study, the interaction of these metabolites with the main virulence factors of P. aeruginosa (pseudomonas elastase and exotoxin A), Staphylococcus aureus (alpha-hemolysin and protein 2a), Mycobacterium tuberculosis (ESX-secreted protein B and the serine/threonine protein kinase), and Escherichia coli (heat-labile enterotoxin and Shiga toxin) were evaluated by molecular docking study and molecular simulation.

RESULTS

In the case of Shiga toxin, higher and lower binding affinities were related to alpha-terpinolene and zincite with values of -5.8 and -2.6 kcal/mol, respectively. For alpha-hemolysin, terpinolene and alpha-terpinolene demonstrated higher binding affinities with similar energies of -5.9 kcal/mol. Thymol and geranyl acetate showed lower binding energy of -5.7 kcal/mol toward protein 2a. Furthermore, thymol had a higher binding affinity toward heat-labile enterotoxin and ESX-secreted protein B with values of -5.9 and -6.1 kcal/mol, respectively.

CONCLUSIONS

It is concluded that the availability of secondary metabolites of A. haussknechtii surrounding zinc oxide (ZnO) NPs can hinder P. aeruginosa by inactivating Pseudomonas elastase and exotoxin.

Design of a multi-epitope vaccine against goatpox virus using an immunoinformatics approach

Introduction

Goatpox, a severe infectious disease caused by goatpox virus (GTPV), leads to enormous economic losses in the livestock industry. Traditional live attenuated vaccines cause serious side effects and exist a risk of dispersal. Therefore, it is urgent to develop efficient and safer vaccines to prevent and control of GTPV.

Methods

In the present study, we are aimed to design a multi-epitope subunit vaccine against GTPV using an immunoinformatics approach. Various immunodominant cytotoxic T lymphocytes (CTL) epitopes, helper T lymphocytes (HTL) epitopes, and B-cell epitopes from P32, L1R, and 095 proteins of GTPV were screened and liked by the AAY, GPGPG, and KK connectors, respectively. Furthermore, an adjuvant β-defensin was attached to the vaccine's N-terminal using the EAAAK linker to enhance immunogenicity.

Results

The constructed vaccine was soluble, non-allergenic and non-toxic and exhibited high levels of antigenicity and immunogenicity. The vaccine's 3D structure was subsequently predicted, refined and validated, resulting in an optimized model with a Z-value of -3.4. Molecular docking results demonstrated that the vaccine had strong binding affinity with TLR2(-27.25 kcal/mol), TLR3(-39.84 kcal/mol), and TLR4(-59.42 kcal/mol). Molecular dynamics simulation results indicated that docked vaccine-TLR complexes were stable. Immune simulation analysis suggested that the vaccine can induce remarkable increase in antibody titers of IgG and IgM, higher levels of IFN-γ and IL-2.

Conclusion

The designed GTPV multi-epitope vaccine is structurally stable and can induce robust humoral and cellular immune responses, which may be a promising vaccine candidate against GTPV.

Designing a T-cell epitope-based vaccine using in silico approaches against the Sal k 1 allergen of Salsola kali plant

Allergens originated from Salsola kali (Russian thistle) pollen grains are one of the most important sources of aeroallergens causing pollinosis in desert and semi-desert regions. T-cell epitope-based vaccines (TEV) are more effective among different therapeutic approaches developed to alleviate allergic diseases. The physicochemical properties, and B as well as T cell epitopes of Sal k 1 (a major allergen of S. kali) were predicted using immunoinformatic tools. A TEV was constructed using the linkers EAAAK, GPGPG and the most suitable CD4+ T cell epitopes. RS04 adjuvant was added as a TLR4 agonist to the amino (N) and carboxyl (C) terminus of the TEV protein. The secondary and tertiary structures, solubility, allergenicity, toxicity, stability, physicochemical properties, docking with immune receptors, BLASTp against the human and microbiota proteomes, and in silico cloning of the designed TEV were assessed using immunoinformatic analyses. Two CD4+ T cell epitopes of Sal k1 that had high affinity with different alleles of MHC-II were selected and used in the TEV. The molecular docking of the TEV with HLADRB1, and TLR4 showed TEV strong interactions and stable binding pose to these receptors. Moreover, the codon optimized TEV sequence was cloned between NcoI and XhoI restriction sites of pET-28a(+) expression plasmid. The designed TEV can be used as a promising candidate in allergen-specific immunotherapy against S. kali. Nonetheless, effectiveness of this vaccine should be validated through immunological bioassays.

Immunoinformatics, molecular docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus

Lymphoid leukosis is a poultry neoplastic disease caused by avian leukosis virus (ALV) and is characterized by high morbidity and variable mortality rates in chicks. Currently, no effective treatment and vaccination is the only means to control it. This study exploited the immunoinformatics approaches to construct multi-epitope vaccine against ALV. ABCpred and IEDB servers were used to predict B and T lymphocytes epitopes from the viral proteins, respectively. Antigenicity, allergenicity and toxicity of the epitopes were assessed and used to construct the vaccine with suitable adjuvant and linkers. Secondary and tertiary structures of the vaccine were predicted, refined and validated. Structural errors, solubility, stability, immune simulation, dynamic simulation, docking and in silico cloning were also evaluated.The constructed vaccine was hydrophilic, antigenic and non-allergenic. Ramchandran plot showed most of the residues in the favored and additional allowed regions. ProsA server showed no errors in the vaccine structure. Immune simulation showed significant immunoglobulins and cytokines levels. Stability was enhanced by disulfide engineering and molecular dynamic simulation. Docking of the vaccine with chicken's TLR7 revealed competent binding energies.The vaccine was cloned in pET-30a(+) vector and efficiently expressed in Escherichia coli. This study provided a potent peptide vaccine that could assist in tailoring a rapid and cost-effective vaccine that helps to combat ALV. However, experimental validation is required to assess the vaccine efficiency.

Immunoinformatics Approach to Design a Chimeric CD70-Peptide Vaccine against Renal Cell Carcinoma

Renal cell carcinoma (RCC) accounts for the majority of cancer-related deaths worldwide. Overexpression of CD70 has been linked to advanced stages of RCC. Therefore, this study aims to develop a multiepitope vaccine targeting the overexpressed CD70 using immunoinformatics techniques. In this investigation, in silico multiepitope vaccines were constructed by linking specific CD70 protein epitopes for helper T lymphocytes and CD8+ T lymphocytes. To enhance immunogenicity, sequences of cell-penetrating peptide (CPP), penetratin (pAntp), along with the entire sequence of tumor necrosis factor-α (TNF-α), were attached to the N-terminal and C-terminal of the CD70 epitopes. Computational assessments were performed on these chimeric vaccines for antigenicity, allergenicity, peptide toxicity, population coverage, and physicochemical properties. Furthermore, refined 3D constructs were subjected to a range of analyses, encompassing structural B-cell epitope prediction and molecular docking. The chosen vaccine construct underwent diverse assessments such as molecular dynamics simulation, immune response simulation, and in silico cloning. All vaccines comprised antigenic, nontoxic, and nonallergenic epitopes, ensuring extensive global population coverage. The vaccine constructs demonstrated favorable physicochemical characteristics. The binding affinity of chimeric vaccines to the TNF receptor remained relatively stable, influenced by the alignment of vaccine components. Molecular docking and dynamics analyses predicted stable interactions between CD70-CPP-TNF and the TNF receptor, indicating potential efficacy. In silico codon optimization and cloning of the vaccine nucleic acid sequence were accomplished using the pET28a plasmid. Furthermore, this vaccine displayed the capacity to modulate humoral and cellular immune responses. Overall, the results suggest therapeutic potential for the chimeric CD70-CPP-TNF vaccine against RCC. However, validation through in vitro and in vivo experiments is necessary. This trial is registered with NCT04696731 and NCT04046445.

Design of a novel multi-epitopes vaccine against Escherichia fergusonii: a pan-proteome based in- silico approach

Escherichia fergusonii a gram-negative rod-shaped bacterium in the Enterobacteriaceae family, infect humans, causing serious illnesses such as urinary tract infection, cystitis, biliary tract infection, pneumonia, meningitis, hemolytic uremic syndrome, and death. Initially treatable with penicillin, antibiotic misuse led to evolving resistance, including resistance to colistin, a last-resort drug. With no licensed vaccine, the study aimed to design a multi-epitope vaccine against E. fergusonii. The study started with the retrieval of the complete proteome of all known strains and proceeded to filter the surface exposed virulent proteins. Seventeen virulent proteins (4 extracellular, 4 outer membranes, 9 periplasmic) with desirable physicochemical properties were identified from the complete proteome of known strains. Further, these proteins were processed for B-cell and T-cell epitope mapping. Obtained epitopes were evaluated for antigenicity, allergenicity, solubility, MHC-binding, and toxicity and the filtered epitopes were fused by specific linkers and an adjuvant into a vaccine construct. Structure of the vaccine candidate was predicted and refined resulting in 78.1% amino acids in allowed regions and VERIFY3D score of 81%. Vaccine construct was docked with TLR-4, MHC-I, and MHC-II, showing binding energies of -1040.8 kcal/mol, -871.4 kcal/mol, and -1154.6 kcal/mol and maximum interactions. Further, molecular dynamic simulation of the docked complexes was carried out resulting in a significant stable nature of the docked complexes (high B-factor and deformability values, lower Eigen and high variance values) in terms of intermolecular binding conformation and interactions. The vaccine was also reported to stimulate a variety of immunological pathways after administration. In short, the designed vaccine revealed promising predictions about its immune protective potential against E. fergusonii infections however experimental validation is needed to validate the results.

Exploring the structural and molecular interaction landscape of nirmatrelvir and Mpro complex: The study might assist in designing more potent antivirals targeting SARS-CoV-2 and other viruses

BACKGROUND

Several therapeutics have been developed and approved against SARS-CoV-2 occasionally; nirmatrelvir is one of them. The drug target of nirmatrelvir is Mpro, and therefore, it is necessary to comprehend the structural and molecular interaction of the Mpro-nirmatrelvir complex.

METHODS

Integrative bioinformatics, system biology, and statistical models were used to analyze the macromolecular complex.

RESULTS

Using two macromolecular complexes, the study illustrated the interactive residues, H-bonds, and interactive interfaces. It informed of six and nine H-bond formations for the first and second complex, respectively. The maximum bond length was observed as 3.33 Å. The ligand binding pocket's surface area and volume were noted as 303.485 Å2 and 295.456 Å3 for the first complex and 308.397 Å2 and 304.865 Å3 for the second complex. The structural proteome dynamics were evaluated by analyzing the complex's NMA mobility, eigenvalues, deformability, and B-factor. Conversely, a model was created to assess the therapeutic status of nirmatrelvir.

CONCLUSIONS

Our study reveals the structural and molecular interaction landscape of Mpro-nirmatrelvir complex. The study will guide researchers in designing more broad-spectrum antiviral molecules mimicking nirmatrelvir, which assist in fighting against SARS-CoV-2 and other infectious viruses. It will also help to prepare for future epidemics or pandemics.

Design of a multi-epitope vaccine against brucellosis fused to IgG-fc by an immunoinformatics approach

Introduction

Brucella, a type of intracellular Gram-negative bacterium, has unique features and acts as a zoonotic pathogen. It can lead to abortion and infertility in animals. Eliminating brucellosis becomes very challenging once it spreads among both humans and animals, putting a heavy burden on livestock and people worldwide. Given the increasing spread of brucellosis, it is crucial to develop improved vaccines for susceptible animals to reduce the disease's impact.

Methods

In this study, we effectively used an immunoinformatics approach with advanced computer software to carefully identify and analyze important antigenic parts of Brucella abortus. Subsequently, we skillfully designed chimeric peptides to enhance the vaccine's strength and effectiveness. We used computer programs to find four important parts of the Brucella bacteria that our immune system recognizes. Then, we carefully looked for eight parts that are recognized by a type of white blood cell called cytotoxic T cells, six parts recognized by T helper cells, and four parts recognized by B cells. We connected these parts together using a special link, creating a strong new vaccine. To make the vaccine even better, we added some extra parts called molecular adjuvants. These included something called human β-defensins 3 (hBD-3) that we found in a database, and another part that helps the immune system called PADRE. We attached these extra parts to the beginning of the vaccine. In a new and clever way, we made the vaccine even stronger by attaching a part from a mouse's immune system to the end of it. This created a new kind of vaccine called MEV-Fc. We used advanced computer methods to study how well the MEV-Fc vaccine interacts with certain receptors in the body (TLR-2 and TLR-4).

Results

In the end, Immunosimulation predictions showed that the MEV-Fc vaccine can make the immune system respond strongly, both in terms of cells and antibodies.

Discussion

In summary, our results provide novel insights for the development of Brucella vaccines. Although further laboratory experiments are required to assess its protective effect.

Developmental landscape of computational techniques to explore the potential phytochemicals from Punica granatum peels for their antioxidant activity in Alzheimer's disease

Alzheimer's disease (AD) is more commonly found in women than in men as the risk increases with age. Phytochemicals are screened in silico from Punica granatum peels for their antioxidant activity to be utilized for Alzheimer's disease. Alzheimer's disease is inhibited by the hormone estrogen, which protects the brain from the bad effects of amyloid beta and acetylcholine (ACh), and is important for memory processing. For the purpose, a library of about 1,000 compounds from P. granatum were prepared and studied by applying integrated computational calculations like 3D-QSAR, molecular docking, MD simulation, ADMET, and density functional theory (DFT). The 3D-QSAR model screened the active compounds B25, B29, B35, B40, B45, B46, B48, B61, and B66 by the field points and activity atlas model from the prepared library. At the molecular level, docking was performed on active compounds for leading hit compounds such as B25 and B35 that displayed a high MolDock score, efficacy, and compatibility with drug delivery against the antioxidant activity. Optimization of the structure and chemical reactivity parameter of the hit compound was calculated by DFT. Moreover, ADMET prediction was evaluated to check the bioavailability and toxicity of the hit compound. Hesperidin (B25) is found to be a hit compound after the whole study and can be synthesized for potent drug discovery in the future.

Cupressus sempervirens L. flavonoids as potent inhibitors to xanthine oxidase: in vitro, molecular docking, ADMET and PASS studies

Excessive intake of purine-rich foods such as seafood and red meat leads to excess xanthine oxidase activity and provokes gout attacks. The aim of this paper is to evaluate in vitro and in silico, the inhibition effect of Cupressus sempervirens plant extracts (flavonoids (Cae) and alkaloids (CaK)) and its six derivative compounds on bovine xanthine oxidase (BXO). The in silico study consists of molecular docking with GOLD v4.0 based on the best PLPchem score (PLP) and prediction of biological activity with the PASS server tool. The inhibitors used were lignan (cp1), Amentoflavone (cp2), Cupressuflavone (cp3), Isocryptomerin (cp4), Hinokiflavone (cp5), and Neolignan (cp6). The in vitro results showed that CaK gives an IC50 of 3.52 ± 0.04 μg/ml. Similarly, Cae saved an IC50 of 8.46 ± 1.98 μg/ml compared with the control (2.82 ± 0.10 μg/ml). The in silico results show that cp1 was the best inhibitor model (PLP of 88.09) with approved pharmacokinetics. These findings suggest that cp1 and cp2 may offer good alternatives for the treatment of hyperuricemia; cp3 was moderate, while the others (cp4 to cp6) were considered weak inhibitors according to their PLP.Communicated by Ramaswamy H. Sarma.

Design and in silico analysis of mRNA vaccine construct against Salmonella

Salmonella infections are continuously growing. Causative serovars have gained enhanced drug resistance and virulence. Current vaccines have fallen short of providing sufficient protection. mRNA vaccines have come up with huge success against SARS-CoV-2; Pfizer-BioNTech and Moderna vaccines have resulted in >90% efficacy with efficient translocation, expression, and presentation of antigen to the host immune system. Herein, based on the same approach a mRNA vaccine construct has been designed and analyzed against Salmonella by joining regions of genes of outer membrane proteins C and F of S. Typhi through a flexible linker. Construct was flanked by regulatory regions that have previously shown better expression and translocation of encoded protein. GC content of the construct was improved to attain structural and thermodynamic stability and smooth translation. Sites of strong binding miRNAs were removed through codon optimization. Protein encoded by this construct is structurally plausible, highly antigenic, non-allergen to humans, and does not cross-react to the human proteome. It is enriched in potent, highly antigenic, and conserved linear and conformational epitopes. Most conserved conformational epitopes of core protein lie on extended beta hairpins exposed to the cellular exterior. Stability and thermodynamic attributes of the final construct were found highly comparable to the Pfizer-BioNTech vaccine construct. Both contain a stable stem-loop structure downstream of the start codon and do not offer destabilizing secondary structures upstream of the start codon. Given structural and thermodynamic stability, effective immune response, and epitope composition the construct is expected to provide broad-spectrum protection against clinically important Salmonella serovars.Communicated by Ramaswamy H. Sarma.

Exploring antidiabetic potential of a polyherbal formulation Madhurakshak Activ: An in vitro and in silico study

Madhurakshak Activ (MA), a commercial polyherbal antidiabetic preparation is known to manage diabetes mellitus (DM) by reducing blood glucose levels. However, lacks systematic mechanistic evaluation for their molecular and cellular mode of actions. In the present study, hydro-alcoholic and aqueous extract of MA were evaluated for their effects on glucose adsorption, diffusion, amylolysis kinetics and transport across the yeast cells using in vitro techniques. Bioactive compounds identified from MA by LC-MS/MS were assessed for their binding potential against DPP-IV and PPARγ via an in silico approach. Our results revealed that the adsorption of glucose increased dose dependently (5 mM -100 mM). Both extracts exhibited linear glucose uptake into the yeast cells (5 mM - 25 mM), whereas glucose diffusion was directly proportional to time (30-180 min). Pharmacokinetic analysis revealed drug-like properties and low toxicity levels for all the selected compounds. Among the tested compounds, 6-hydroxyluteolin (-8.9 against DPP-IV and PPARγ) and glycyrrhetaldehyde (DPP-IV -9.7 and PPARγ -8.5) have exhibited higher binding affinity compared to the positive control. Therefore, the above compounds were further considered for molecular dynamics simulation which showed stability of the docked complexes. Hence, studied mode of actions might produce a concerted role of MA in increasing the rate of glucose absorption and uptake followed by the in silico studies which suggest that the compounds identified from MA may inhibit DPP-IV and PPARγ phosphorylation.

The stem region of group A transferase is crucial for its specificity, and its alteration promotes heterologous Forssman synthase activity

Some stem region mutants of human blood group A transferase (hAT) possess Forssman synthase (FS) activity, but very little is known about the mechanisms responsible for this enzymatic crosstalk. We performed confocal microscopy and image analysis to determine whether different intra-Golgi localization was accountable for this acquired activity. We also performed structural modeling and mutational and normal mode analyses. We introduced new mutations in the stem region and tested its FS and AT activities. No differences in subcellular localization were found between hAT and FS-positive mutants. AlphaFold models of hAT and mFS (mouse Forssman synthase) showed that the hAT stem region has a tether-like stem region, while in mFS, it encircles its catalytic domain. In silico analysis of FS-positive mutants indicated that stem region mutations induced structural changes, decreasing interatomic interactions and mobility of hAT that correlated with FS activity. Several additional mutations introduced in that region also bestowed FS activity without altering the AT activity: hAT 37-55 aa substitution by mFS 34-52, 37-55 aa deletion, and missense mutations: S46P, Q278Y, and Q286M. Stem region structure, mobility, and interactions are crucial for hAT specificity. Moreover, stem region mutations can lead to heterologous Forssman activity without changes in the catalytic machinery.

Genome-Wide Mining of Selaginella moellendorffii for Hevein-like Lectins and Their Potential Molecular Mimicry with SARS-CoV-2 Spike Glycoprotein

Multidisciplinary research efforts on potential COVID-19 vaccine and therapeutic candidates have increased since the pandemic outbreak of SARS-CoV-2 in 2019. This search has become imperative due to the increasing emergences and limited widely available medicines. The presence of bioactive anti-SARS-CoV-2 molecules was examined from various plant sources. Among them is a group of proteins called lectins that can bind carbohydrate moieties. In this article, we present ten novel, chitin-specific Hevein-like lectins that were derived from Selaginella moellendorffii v1.0's genome. The capacity of these lectin homologs to bind with the spike protein of SARS-CoV-2 was examined. Using the HDOCK server, 3D-modeled Hevein-domains were docked to the spike protein's receptor binding domain (RBD). The Smo446851, Smo125663, and Smo99732 interacted with Asn343-located complex N-glycan and RBD residues, respectively, with binding free energies of -17.5, -13.0, and -26.5 Kcal/mol. The molecular dynamics simulation using Desmond and the normal-state analyses via torsional coordinate association for the Smo99732-RBD complex using iMODS is characterized by overall higher stability and minimum deformity than the other lectin complexes. The three lectins interacting with carbohydrates were docked against five individual mutations that frequently occur in major SARS-CoV-2 variants. These were in the spike protein's receptor-binding motif (RBM), while Smo125663 and Smo99732 only interacted with the spike glycoprotein in a protein-protein manner. The precursors for the Hevein-like homologs underwent additional characterization, and their expressional profile in different tissues was studied. These in silico findings offered potential lectin candidates targeting key N-glycan sites crucial to the virus's virulence and infection.

Immunoinformatic prediction of potential immunodominant epitopes from cagW in order to investigate protection against Helicobacter pylori infection based on experimental consequences

Helicobacter pylori is a leading cause of stomach cancer and peptic ulcers. Thus, identifying epitopes in H. pylori antigens is important for disease etiology, immunological surveillance, enhancing early detection tests, and developing optimal epitope-based vaccines. We used immunoinformatic and computational methods to create a potential CagW epitope candidate for H. pylori protection. The cagW gene of H. pylori was amplified and cloned into pcDNA3.1 (+) for injection into the muscles of healthy BALB/c mice to assess the impact of the DNA vaccine on interleukin levels. The results will be compared to a control group of mice that received PBS or cagW-pcDNA3.1 (+) vaccinations. An analysis of CagW protein antigens revealed 8 CTL and 7 HTL epitopes linked with AYY and GPGPG, which were enhanced by adding B-defensins to the N-terminus. The vaccine's immunogenicity, allergenicity, and physiochemistry were validated, and its strong activation of TLRs (1, 2, 3, 4, and 10) suggests it is antigenic. An in-silico cloning and immune response model confirmed the vaccine's expression efficiency and predicted its impact on the immune system. An immunofluorescence experiment showed stable and bioactive cagW gene expression in HDF cells after cloning the whole genome into pcDNA3.1 (+). In vivo vaccination showed that pcDNA3.1 (+)-cagW-immunized mice had stronger immune responses and a longer plasmid DNA release window than control-plasmid-immunized mice. After that, bioinformatics methods predicted, developed, and validated the three-dimensional structure. Many online services docked it with Toll-like receptors. The vaccine was refined using allergenicity, antigenicity, solubility, physicochemical properties, and molecular docking scores. Virtual-reality immune system simulations showed an impressive reaction. Codon optimization and in-silico cloning produced E. coli-expressed vaccines. This study suggests a CagW epitopes-protected H. pylori infection. These studies show that the proposed immunization may elicit particular immune responses against H. pylori, but laboratory confirmation is needed to verify its safety and immunogenicity.

Antidiarrheal Potential of Viola canescens: In Vivo and In Silico Approaches

Viola canescens Wall. is an important medicinal plant with reported therapeutic benefits. The current work sought to investigate the antidiarrheal properties of V. canescens extracts both in vivo and in silico. This study applied molecular docking to unravel the molecular mechanism of V. canescens and to find the most effective phytocompounds with antidiarrheal effects. The antidiarrheal activity of V. canescens was assessed utilizing the castor oil-induced diarrhea assay and the charcoal meal assay. Antidiarrheal characteristics were evaluated by measuring parameters such as intestinal motility, fecal score, and hypersecretion. The V. canescens extract had a dose-dependent and statistically significant impact in the charcoal meal assay and castor oil-induced diarrhea assay. In the castor oil-induced diarrhea assay, the ethyl acetate fraction (65.96%) showed the highest percentage of defecation inhibition at the highest dose (300 mg/kg (bw)), followed by the uncorrected crystalline compound (63.83%), crude alkaloids (63.83%), chloroform fraction (63.83%), and crude flavonoids (55.32%), while the aqueous fraction (40.43%) and n-Hexane fraction (42.55%) revealed the lowest antidiarrheal potential. In addition, the molecular docking investigation showed emetine, quercetin, and violanthin, isolated chemicals of V. canescens, to have the highest binding affinity to the target μ and δ opioid receptors with significant inhibitory capacity. These pharmacologically active metabolites in V. canescens were effective in treating diarrhea. This study lends credence to the traditional usage of V. canescens in treating gastrointestinal disorders.

In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross-immunity against human visceral and cutaneous leishmaniasis: an immunoinformatics-based approach

CONTEXT

Leishmaniasis is a group of vector-borne infectious diseases caused by over 20 pathogenic Leishmania species that are endemic in many tropical and subtropical countries. The emergence of drug-resistant strains, the adverse side effects of anti-Leishmania drugs, and the absence of a preventative vaccination strategy threaten the sensitive population. Recently, many groups of researchers have exploited the field of reverse vaccinology to develop vaccines, focusing chiefly on inducing immunity against either visceral or cutaneous leishmaniasis.

METHODS

This present work involves retrieving twelve experimentally validated leishmanial antigenic protein sequences from the UniProt database, followed by their antigenicity profiling employing ANTIGENpro and Vaxijen 2.0 servers. MHC-binding epitopes for the same were predicted using both NetCTL 1.2 and SYFPEITHI servers, while epitopes for B cell were computed using ABCpred and BepiPred 2.0 servers. The screened epitopes with significantly higher scores were utilized for designing the vaccine construct with appropriate linkers and natural adjuvant. The secondary and tertiary structures of the synthetic peptide were determined by conditional random fields, shallow neural networks, and profile-profile threading alignment with iterative structure assembly simulations, respectively. The 3-D vaccine model was validated through CASP10-tested refinement and the MolProbity web server. Molecular docking and multi-scale normal mode analysis simulation were performed to analyze the best vaccine-TLR complex. Finally, computational immune simulation findings revealed promising cellular and humoral immune responses, suggesting that the engineered chimeric peptide is a potential broad-spectrum vaccine against visceral and cutaneous leishmaniasis.

Immunoinformatics design of multi-epitope vaccine using OmpA, OmpD and enterotoxin against non-typhoidal salmonellosis

BACKGROUND

Non-typhoidal Salmonella (NTS) is one of the important bacteria that cause foodborne diseases and invasive infections in children and elderly people. Since NTS infection is difficult to control due to the emergence of antibiotic-resistant species and its adverse effect on immune response, the development of a vaccine against NTS would be necessary. This study aimed to develop a multi-epitope vaccine against the most prevalent serovars of NTS (Salmonella Typhimurium, Salmonella Enteritidis) using an immunoinformatics approach and targeting OmpA, OmpD, and enterotoxin (Stn).

RESULTS

Initially, the B cell and T cell epitopes were predicted. Then, epitopes and suitable adjuvant were assembled by molecular linkers to construct a multi-epitope vaccine. The computational tools predicted the tertiary structure, refined the tertiary structure and validated the final vaccine construct. The effectiveness of the vaccine was evaluated via molecular docking, molecular dynamics simulation, and in silico immune simulation. The vaccine model had good binding affinity and stability with MHC-I, MHC-II, and toll-like receptors (TLR-1, 2, 4) as well as activation of T cells, IgM, IgG, IFN-γ and IL-2 responses. Furthermore, after codon optimization of the vaccine sequence, this sequence was cloned in E. coli plasmid vector pET-30a (+) within restriction sites of HindIII and BamHI.

CONCLUSIONS

This study, for the first time, introduced a multi-epitope vaccine based on OmpA, OmpD and enterotoxin (Stn) of NTS that could stimulate T and B cell immune responses and produced in the prokaryotic system. This vaccine was validated in-silico phase which is an essential study to reduce challenges before in vitro and in vivo studies.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses.

METHODS

Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed.

RESULTS

A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

Immunoinformatics Approach to Design a Multi-Epitope Nanovaccine against Leishmania Parasite: Elicitation of Cellular Immune Responses

Leishmaniasis is a vector-borne disease caused by an intracellular parasite of the genus Leishmania with different clinical manifestations that affect millions of people worldwide, while the visceral form may be fatal if left untreated. Since the available chemotherapeutic agents are not satisfactory, vaccination emerges as the most promising strategy for confronting leishmaniasis. In the present study, a reverse vaccinology approach was adopted to design a pipeline starting from proteome analysis of three different Leishmania species and ending with the selection of a pool of MHCI- and MHCII-binding epitopes. Epitopes from five parasite proteins were retrieved and fused to construct a multi-epitope chimeric protein, named LeishChim. Immunoinformatics analyses indicated that LeishChim was a stable, non-allergenic and immunogenic protein that could bind strongly onto MHCI and MHCII molecules, suggesting it as a potentially safe and effective vaccine candidate. Preclinical evaluation validated the in silico prediction, since the LeishChim protein, encapsulated simultaneously with monophosphoryl lipid A (MPLA) into poly(D,L-lactide-co-glycolide) (PLGA) nanoparticles, elicited specific cellular immune responses when administered to BALB/c mice. These were characterized by the development of memory CD4+ T cells, as well as IFNγ- and TNFα-producing CD4+ and CD8+ T cells, supporting the potential of LeishChim as a vaccine candidate.

Molecular docking/dynamics simulations, MEP analysis, bioisosteric replacement and ADME/T prediction for identification of dual targets inhibitors of Parkinson's disease with novel scaffold

Monoamine oxidase B and Adenosine A2A receptors are used as key targets for Parkinson's disease. Recently, hMAO-B and hA2AR Dual-targets inhibitory potential of a novel series of Phenylxanthine derivatives has been established in experimental findings. Hence, the current study examines the interactions between 38 compounds of this series with hMAO-B and hA2AR targets using different molecular modeling techniques to investigate the binding mode and stability of the formed complexes. A molecular docking study revealed that the compounds L24 ((E)-3-(3-Chlorophenyl)-N-(4-(1,3-dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-8-yl) phenyl) acrylamide and L32 ((E)-3-(3-Chlorophenyl)-N-(3-(1,3-dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-8-yl)phenyl)acrylamide) had a high affinity (S-score: -10.160 and -7.344 kcal/mol) with the pocket of hMAO-B and hA2AR targets respectively, and the stability of the studied complexes was confirmed during MD simulations. Also, the MEP maps of compounds 24 and 32 were used to identify the nucleophilic and electrophilic attack regions. Moreover, the bioisosteric replacement approach was successfully applied to design two new analogs of each compound with similar biological activities and low energy scores. Furthermore, ADME-T and Drug-likeness results revealed the promising pharmacokinetic properties and oral bioavailability of these compounds. Thus, compounds L24, L32, and their analogs can undergo further analysis and optimization in order to design new lead compounds with higher efficacy toward Parkinson's disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00139-3.

Multi epitope vaccine candidate design against Streptococcus pneumonia

Streptococcus pneumonia, the causative agent of sepsis, meningitis and pneumonia, is held responsible for causing invasive diseases predominantly in children along with adults from both developing and developed countries. The available vaccines coverage in the context of different serotypes is limited and emergence of non-vaccine serotypes could further emerge as a threat in future. Advanced immunoinformatics tools have been used for developing a multi epitope subunit vaccine. In the current study we have subjected these four surface antigenic proteins Ply, PsaA, PspA and PspK to construct vaccine designs. We have predicted different B-cell and T-cell epitopes by using NetCTL 1.2, IEDB (Immune Epitope Databases) and ABCpred. An adjuvant (griselimycin) has been added to the vaccine construct sequence in order to improve its immunogenicity. The vaccine construct has been evaluated for its antigenicity, allergenicity, toxicity and different physio-chemical properties. The bioinformatic tools have been used for prediction, refinement and validation of the 3 D structure. Further, the vaccine structure has been docked with a toll-like receptor (TLR-4) by ClusPro 2.0. In conclusion, the proposed multi-epitope vaccine designs could potentially activate both humoral and cellular immune responses and has a potential to be a vaccine candidate against S.pneumoniae, and requires experimental validation for ensuring immunogenicity and safety profile.Communicated by Ramaswamy H. Sarma.

Chain-Engineering-Based De Novo Drug Design against MPXVgp169 Virulent Protein of Monkeypox Virus: A Molecular Modification Approach

The unexpected appearance of the monkeypox virus and the extensive geographic dispersal of cases have prompted researchers to concentrate on potential therapeutic approaches. In addition to its vaccine build techniques, there should be some multiple integrated antiviral active compounds because of the MPV (monkeypox virus) outbreak in 2022. This study offers a computational engineering-based de novo drug discovery mediated by random antiviral active compounds that were screened against the virulent protein MPXVgp169, as one of the key players directing the pathogenesis of the virus. The screening of these candidates was supported by the use of 72 antiviral active compounds. The top candidate with the lowest binding affinity was selected for the engineering of chains or atoms. Literature assisted to identify toxic chains or atoms that were impeding the stability and effectiveness of antiviral compounds to modify them for enhanced efficacy. With a binding affinity of -9.4 Kcal/mol after chain, the lipophilicity of 0.41, the water solubility of 2.51 as soluble, and synthetic accessibility of 6.6, chain-engineered dolutegravir was one of the best active compounds, as proved by the computational engineering analysis. This study will revolutionize the era of drug engineering as a potential therapeutic strategy for monkeypox infection.

Bioinformatics Designing and Molecular Modelling of a Universal mRNA Vaccine for SARS-CoV-2 Infection

At this present stage of COVID-19 re-emergence, designing an effective candidate vaccine for different variants of SARS-CoV-2 is a study worthy of consideration. This research used bioinformatics tools to design an mRNA vaccine that captures all the circulating variants and lineages of the virus in its construct. Sequences of these viruses were retrieved across the six continents and analyzed using different tools to screen for the preferable CD8⁺ T lymphocytes (CTL), CD4⁺ T lymphocytes (HTL), and B-cell epitopes. These epitopes were used to design the vaccine. In addition, several other co-translational residues were added to the construct of an mRNA vaccine whose molecular weight is 285.29686 kDa with an estimated pI of 9.2 and has no cross affinity with the human genome with an estimated over 68% to cover the world population. It is relatively stable, with minimal deformability in its interaction with the human innate immune receptor, which includes TLR 3 and TLR 9. The overall result has proven that the designed candidate vaccine is capable of modulating cell-mediated immune responses by activating the actions of CD4⁺ T cells, natural killer cells, and macrophages, and displayed an increased memory T cell and B cell activities, which may further be validated via in vivo and in vitro techniques.

Explicit versus implicit consideration of binding partners in protein-protein complex to elucidate intrinsic dynamics

The binding of many proteins to their protein partners is tightly regulated via control of their relative intrinsic dynamics during the binding process, a phenomenon which can in turn be modulated. Therefore, investigating the intrinsic dynamics of proteins is necessary to understand function in a comprehensive way. By intrinsic dynamics herein, we principally refer to the vibrational signature of a protein molecule popularly obtained from normal modes or essential modes. For normal modes, one often considers that the molecule under investigation is a collection of springs in a solvent-free or implicit-solvent medium. In the context of a protein-binding partner, the analysis of vibration of the target protein is often complicated due to molecular interaction within the complex. Generally, it is assumed that the isolated bound conformation of the target protein captures the implicit effect of the binding partner on the intrinsic dynamics, therefore suggesting that any influence of the partner molecule is also already integrated. Such an assumption allows large-scale studies of the conservation of protein flexibility. However, in cases where a partner protein directly influences the vibration of the target via critical contacts at the protein-protein interface, the above assumption falls short of providing a detailed view. In this review article, we discuss the implications of considering the dynamics of a protein in a protein-protein complex, as modelled implicitly and explicitly with methods dependent on elastic network models. We further propose how such an explicit consideration can be applied to understand critical protein-protein contacts that can be targeted in future studies.

Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation

The Elastic Network Contact Model (ENCoM) is a coarse-grained normal mode analysis (NMA) model unique in its all-atom sensitivity to the sequence of the studied macromolecule and thus to the effect of mutations. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules, benchmarked its performance against other popular NMA models and used it to study the 3D structural dynamics of human microRNA miR-125a, leveraging high-throughput experimental maturation efficiency data of over 26 000 sequence variants. We also introduce a novel way of using dynamical information from NMA to train multivariate linear regression models, with the purpose of highlighting the most salient contributions of dynamics to function. ENCoM has a similar performance profile on RNA than on proteins when compared to the Anisotropic Network Model (ANM), the most widely used coarse-grained NMA model; it has the advantage on predicting large-scale motions while ANM performs better on B-factors prediction. A stringent benchmark from the miR-125a maturation dataset, in which the training set contains no sequence information in common with the testing set, reveals that ENCoM is the only tested model able to capture signal beyond the sequence. This ability translates to better predictive power on a second benchmark in which sequence features are shared between the train and test sets. When training the linear regression model using all available data, the dynamical features identified as necessary for miR-125a maturation point to known patterns but also offer new insights into the biogenesis of microRNAs. Our novel approach combining NMA with multivariate linear regression is generalizable to any macromolecule for which relatively high-throughput mutational data is available.

Immunoinformatics-guided designing and in silico analysis of epitope-based polyvalent vaccines against multiple strains of human coronavirus (HCoV)

OBJECTIVES

The group of human coronaviruses (HCoVs) consists of some highly pathogenic viruses that have caused several outbreaks in the past. The newly emerged strain of HCoV, the SARS-CoV-2 is responsible for the recent global pandemic that has already caused the death of hundreds of thousands of people due to the lack of effective therapeutic options.

METHODS

In this study, immunoinformatics methods were used to design epitope-based polyvalent vaccines which are expected to be effective against four different pathogenic strains of HCoV i.e., HCoV-OC43, HCoV-SARS, HCoV-MERS, and SARS-CoV-2.

RESULTS

The constructed vaccines consist of highly antigenic, non-allergenic, nontoxic, conserved, and non-homologous T-cell and B-cell epitopes from all the four viral strains. Therefore, they should be able to provide strong protection against all these strains. Protein-protein docking was performed to predict the best vaccine construct. Later, the MD simulation and immune simulation of the best vaccine construct also predicted satisfactory results. Finally, in silico cloning was performed to develop a mass production strategy of the vaccine.

CONCLUSION

If satisfactory results are achieved in further in vivo and in vitro studies, then the vaccines designed in this study might be effective as preventative measures against the selected HCoV strains.