Peptidylarginine deiminase (PAD): A promising target for chronic diseases treatment.

In 1958, the presence of citrulline in the structure of the proteins was discovered for the first time. Several years later they found that Arginine converted to citrulline during a post-translational modification process by PAD enzyme. Each PAD is expressed in a certain tissue developing a series of diseases such as inflammation and cancers. Among these, PAD2 and PAD4 play a role in the development of rheumatoid arthritis (RA) by producing citrullinated autoantigens and increasing the production of inflammatory cytokines. PAD4 is also associated with the formation of NET structures and thrombosis. In the crystallographic structure, PAD has several calcium binding sites, and the active site of the enzyme consists of different amino acids. Various PAD inhibitors have been developed divided into pan-PAD and selective PAD inhibitors. F-amidine, Cl-amidine, and BB-Cl-amidine are some of pan-PAD inhibitors. AFM-30a and JBI589 are selective for PAD2 and PAD4, respectively. There is a need to evaluate the effectiveness of existing inhibitors more accurately in the coming years, as well as design and production of novel inhibitors targeting highly specific isoforms.

Design of a novel multi-epitope vaccine against Marburg virus using immunoinformatics studies.

Marburg virus (MARV) is a highly contagious and virulent agent belonging to Filoviridae family. MARV causes severe hemorrhagic fever in humans and non-human primates. Owing to its highly virulent nature, preventive approaches are promising for its control. There is currently no approved drug or vaccine against MARV, and management mainly involves supportive care to treat symptoms and prevent complications. Our aim was to design a novel multi-epitope vaccine (MEV) against MARV using immunoinformatics studies. In this study, various proteins (VP35, VP40 and glycoprotein precursor) were used and potential epitopes were selected. CTL and HTL epitopes covered 79.44% and 70.55% of the global population, respectively. The designed MEV construct was stable and expressed in Escherichia coli (E. coli) host. The physicochemical properties were also acceptable. MARV MEV candidate could predict comprehensive immune responses such as those of humoral and cellular in silico. Additionally, efficient interaction to toll-like receptor 3 (TLR3) and its agonist (ß-defensin) was predicted. There is a need for validation of these results using further in vitro and in vivo studies.

Designing of a new multi-epitope vaccine against Leishmania major using Leish-F1 epitopes: An In-silico study.

Cutaneous leishmaniasis (CL) is the most common form of the disease which can cause malignant lesions on the skin. Vaccination for the prevention and treatment of leishmaniasis can be the most effective way to combat this disease. In this study, we designed a novel multi-epitope vaccine against Leishmania major (L. major) using immunoinformatics tools to assess its efficacy in silico. Sequences of Leish-F1 protein (TSA, Leif, and LMSTI1) of L. major were taken from GenBank. The helper T (Th) and cytotoxic T (Tc) epitopes of the protein were predicted. The final multi-epitope consisted of 18 CTL epitopes joined by AAY linker. There were also nine HTL epitopes in the structure of the vaccine construct, joined by GPGPG linker. The profilin adjuvant (the toll-like receptor 11 agonist) was also added into the construct by AAY Linker. There were 613 residues in the structure of the vaccine construct. The multi-epitope vaccine candidate was stable and non-allergic. The data obtained from the binding of final multi-epitope vaccine-TLR11 residues (band lengths and weighted scores) unveiled the ligand and the receptor high score of binding affinity. Moreover, in silico assessment of the vaccine construct cloning achieved its suitable expression in E. coli host. Based on these results, the current multi-epitope vaccine prevents L. major infection in silico, while further confirmatory assessments are required.

Identifying potent inhibitory phytocompounds from Lagerstroemia speciosa against SARS-Coronavirus-2: structure-based virtual screening.

The ongoing spillover of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) calls for expedited countermeasure through developing therapeutics from natural reservoirs and/or the use of less time-consuming drug discovery methodologies. This study aims to apply these approaches to identify potential blockers of the virus from the longstanding medicinal herb, Lagerstroemia speciosa, through comprehensive computational-based screening. Nineteen out of 22 L. speciosa phytochemicals were selected on the basis of their pharmacokinetic properties. SARS-CoV-2 Main protease (Mpro), RNA-directed RNA polymerase (RdRp), Envelope viroporin protein (Evp) and receptor-binding domain of Spike glycoprotein (S-RBD), as well as the human receptor Angiotensin-converting enzyme-2 (hACE2) were chosen as targets. The screening was performed by molecular docking, followed by 100-ns molecular dynamic simulations and free energy calculations. 24-Methylene cycloartanol acetate (24MCA) was found as the best inhibitor for both Evp and RdRp, and sitosterol acetate (SA) as the best hit for Mpro, S-RBD and hACE2. Dynamic simulations, binding mode analyses, free energy terms and share of key amino acids in protein-drug interactions confirmed the stable binding of these phytocompounds to the hotspot sites on the target proteins. With their possible multi-targeting capability, the introduced phytoligands might offer promising lead compounds for persistent fight with the rapidly evolving coronavirus. Therefore, experimental verification of their safety and efficacy is recommended.

Identification of novel metallo-ß-lactamases inhibitors using ligand-based pharmacophore modelling and structure-based virtual screening.

Metallo-ß-lactamases (MBLs) are a group of enzymes that hydrolyze the most commonly used ß-lactam-based antibiotics, leading to the development of multi-drug resistance. The three main clinically relevant groups of these enzymes are IMP, VIM, and NDM. This study aims to introduce potent novel overlapped candidates from a ZINC database retrieved from the 200,583-member natural library against the active sites of IMP-1, VIM-2, and NDM-1 through a straightforward computational workflow using virtual screening approaches. The screening pipeline started by assessing Lipinski's rule of five (RO5), drug-likeness, and pan-assay interference compounds (PAINS) which were used to generate a pharmacophore model using D-captopril as a standard inhibitor. The process was followed by the consensus docking protocol and molecular dynamic (MD) simulation combined with the molecular mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method to compute the total binding free energy and evaluate the binding characteristics. The absorption, distribution, metabolism, elimination, and toxicity (ADMET) profiles of the compounds were also analyzed, and the search space decreased to the final two inhibitory candidates for B1 subclass MBLs, which fulfilled all criteria for further experimental evaluation.Communicated by Ramaswamy H. Sarma.

Design of a novel multi-epitope vaccine candidate against Chlamydia trachomatis using structural and nonstructural proteins: an immunoinformatics study.

Chlamydia trachomatis (C. trachomatis) is an obligate intracellular bacterium which causes eye and sexually transmitted infections. During pregnancy, the bacterium is associated with preterm complications, low weight of neonates, fetal demise and endometritis leading to infertility. The aim of our study was design of a multi-epitope vaccine (MEV) candidate against C. trachomatis. After protein sequence adoption from the NCBI, potential epitopes toxicity, antigenicity, allergenicity, MHC-I and MHC-II binding, cytotoxic T lymphocytes (CTLs), Helper T lymphocytes (HTLs) and interferon-γ (IFN-γ)- induction were predicted. The adopted epitopes were fused together using appropriate linkers. In the next step, the MEV structural mapping and characterization, three-dimensional (3D) structure homology modeling and refinement were also performed. The MEV candidate interaction with the toll-like receptor 4 (TLR4) was also docked. The immune responses simulation was assessed using the C-IMMSIM server. Molecular dynamic (MD) simulation verified the structural stability of the TLR4-MEV complex. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach demonstrated the MEV high affinity of binding to the TLR4, MHC-I and MHC-II. The MEV construct was also stable and water soluble and had enough antigenicity and lacked allergenicity with stimulation of T cells and B cells and INF-γ release. The immune simulation confirmed acceptable responses of both the humoral and cellular arms. It is proposed that in vitro and in vivo studies are needed to evaluate the findings of this study.Communicated by Ramaswamy H. Sarma.

Advanced simulations and screening to repurposing a 3C protease inhibitor against the rupintrivir-resistant human norovirus-induced gastroenteritis.

Human norovirus (HuNoV) causes acute viral gastroenteritis in all age groups, and dehydration and severe diarrhea in the elderly. The World Health Organization reports ∼1.45 million deaths from acute gastroenteritis annually in the world. Rupintrivir, an inhibitory medicine against the human rhinovirus C3 protease, has been reported to inhibit HuNoV 3C protease. However, several HuNoV 3C protease mutations have been revealed to reduce the susceptibility of HuNoV to rupintrivir. The structural details behind rupintrivir-resistance of these single-point mutations (A105V and I109V) are not still clear. Hence, in this study, a combination of computational techniques were used to determine the rupintrivir-resistance mechanism and to propose an inhibitor against wild-type and mutant HuNoV 3C protease through structure-based virtual screening. Dynamic structural results indicated the unstable binding of rupintrivir at the cleft binding site of the wild-type and mutant 3C proteases, leading to its detachment. Our findings presented that the domain II of the HuNoV 3C protease had a critical role in binding of inhibitory molecules. Binding energy computations, steered molecular dynamics and umbrella sampling simulations confirmed that amentoflavone, the novel suggested inhibitor, strongly binds to the cleft site of all protease models and has a good structural stability in the complex system along the molecular dynamic simulations. Our in silico study proposed the selected compound as a potential inhibitor against the HuNoV 3C protease. However, additional experimental and clinical studies are required to corroborate the therapeutic efficacy of the compound.

Designing a Multi-Epitope Vaccine against Toxoplasma gondii: An Immunoinformatics Approach.

Infection with the intracellular apicomplexan parasite Toxoplasma gondii causes serious clinical outcomes in both human and veterinary settings worldwide. Although approximately one-third of the world's population is infected with T. gondii, an effective human vaccine for this disease remains unavailable. We aimed to design a potential T. gondii vaccine candidate that consisted of the B- and T-lymphocyte epitopes of three parasite immunogenic antigens. Firstly, the immunodominant epitopes expressed within the ROP2, MIC3, and GRA7 proteins of T. gondii were identified. Subsequently, six B-cell epitopes, five CTL epitopes, and five HTL epitopes were combined to generate a multi-epitope vaccine, and the 50S ribosomal protein L7/L12 was added as an adjuvant to boost the vaccine's immunogenicity. All these epitopes were found to be antigenic, nonallergenic, nontoxic, and nonhuman homologs. The designed vaccine construct has a molecular weight of 51 kDa, an antigenicity score of 0.6182, and a solubility of 0.903461. Likewise, the candidate vaccine was immunogenic, nonallergenic, and stable. Molecular docking analysis revealed stable interactions between the vaccine construct and the TLR-4 immune receptor. Meanwhile, the stability of the developed vaccine was validated using molecular dynamics simulation. In silico, the vaccine construct was able to trigger primary immune responses. However, further laboratory-based assessments are needed to confirm its efficacy and safety.

The structural, dynamic, and thermodynamic basis of darunavir resistance of a heavily mutated HIV-1 protease using molecular dynamics simulation.

The human immunodeficiency virus type 1 protease (HIV-1 PR) is an important enzyme in the life cycle of the HIV virus. It cleaves inactive pre-proteins of the virus and changes them into active proteins. Darunavir (DRV) suppresses the wild-type HIV-1 PR (WT-Pr) activity but cannot inhibit some mutant resistant forms (MUT-Pr). Increasing knowledge about the resistance mechanism can be helpful for designing more effective inhibitors. In this study, the mechanism of resistance of a highly MUT-Pr strain against DRV was investigated. For this purpose, complexes of DRV with WT-Pr (WT-Pr-D) and MUT-Pr (MUT-Pr-D) were studied by all-atom molecular dynamics simulation in order to extract the dynamic and energetic properties. Our data revealed that mutations increased the flap-tip flexibility due to the reduction of the flap-flap hydrophobic interactions. So, the protease's conformation changed from a closed state to a semi-open state that can facilitate the disjunction of DRV from the active site. On the other hand, energy analysis limited to the final basins of the energy landscape indicated that the entropy of binding of DRV to MUT-Pr was more favorable than that of WT-Pr. However, the enthalpy penalty overcomes it and makes binding more unfavorable relative to the WT-Pr. The unfavorable interaction of DRV with R8, I50, I84, D25', and A28' residues in MUT-Pr-D relative to WT-Pr-D is the reason for this enthalpy penalty. Thus, mutations drive resistance to DRV. The hydrogen bond analysis showed that compared with WT-Pr, the hydrogen bonds between DRV and the active-site residues of MUT-Pr were disrupted.

Design of a novel multi-epitope vaccine candidate against hepatitis C virus using structural and nonstructural proteins: An immunoinformatics approach.

Hepatitis C virus (HCV) infects the liver and causes chronic infection. Several mutations in the viral genome have been associated with drug resistance development. Currently, there is no approved vaccine against the HCV. The employment of computational biology is the primary and crucial step for vaccine design or antiviral therapy which can substantially reduce the duration and cost of studies. Therefore, in this study, we designed a multi-epitope vaccine using various immunoinformatics tools to elicit the efficient human immune responses against the HCV. Initially, various potential (antigenic, immunogenic, non-toxic and non-allergenic) epitope segments were extracted from viral structural and non-structural protein sequences using multiple screening methods. The selected epitopes were linked to each other properly. Then, toll-like receptors (TLRs) 3 and 4 agonists (50S ribosomal protein L7/L12 and human ß-defensin 2, respectively) were added to the N-terminus of the final vaccine sequence to increase its immunogenicity. The 3D structure of the vaccine was modeled. Molecular dynamics simulations studies verified the high stability of final free vaccines and in complex with TLR3 and TLR4. These constructs were also antigenic, non-allergenic, nontoxic and immunogenic. Although the designed vaccine traits were promising as a potential candidate against the HCV infection, experimental studies and clinical trials are required to verify the protective traits and safety of the designed vaccine.