Computational approach to suggest a new multi-target-directed ligand as a potential medication for Alzheimer’s disease

Discovery of newer 1,3,4-Oxadiazole clubbed Isoindoline-1,3-dione derivatives as potential anticancer agents: Design, machine learning, synthesis, molecular docking, ADMET, DFT and MD simulation

Cancer remains to be the second leading cause of death, since the available drugs and therapies may get failure due to the early-stage drug resistance, metastasis, poor pharmacokinetics, and toxic effects. This gap can be fulfilled by designing potential anticancer agent with the Phthalimide as a prime scaffold. The robust and reliable pharmacophore model was used for the designing of newer Phthalimide derivatives. Additionally, we clubbed 1,3,4-Oxadiazole with Phthalimide to fulfil these features. The predicted IC50 for all the designed compounds are in µM range and DFT study also confirmed the reactive nature of these molecules. The designed compounds were synthesized and characterized by FT-IR, 1H NMR, 13C NMR and Mass spectroscopy. The in-vitro anticancer evaluation was carried out by performing MTT assay on MCF-7 and HCT-116 cancer cell lines. All compounds showed moderate to potent anticancer activity. The compound B19 was found to be the most potent against both the MCF-7 and HCT-116 with IC50 of 3.468 and 4.508 µM respectively. All the compounds showed good docking score in terms of binding affinity, lib dock score, CDOCKER interaction and binding free energy. MD Simulation study reviled good stability, compactness and rigidity of potent compound throughout the 100 ns run. ADMET results supports the good pharmacokinetics and lower toxicity. In conclusion, we suggest the compound B19 is potential drug-like candidate can be utilized in anticancer treatment on further confirmations. This study is widely useful for the medicinal chemists and scientific community.

Lymphocyte antigen 96: A new potential biomarker and immune target in Parkinson's disease

BACKGROUND

Lymphocyte antigen 96 (LY96) plays an important role in innate immunity and has been reported to be associated with various neurological diseases. However, its role in Parkinson's disease (PD) remains unclear.

METHODS

Transcriptome data from a total of 49 patients with PD and 34 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database to analyse the expression pattern of LY96 and its relationship with gene function and immune-related markers. In addition, peripheral blood samples were collected from clinical patients to validate LY96 mRNA expression levels. Finally, an in vitro cell model of PD based on highly differentiated SH-SY5Y cells was constructed, with small interfering RNA-silenced LY96 expression, and LY96 mRNA level, cell viability, flow cytometry, and mitochondrial membrane potential assays were performed.

RESULTS

The results of the analyses of the GEO database and clinical samples revealed significantly abnormally high LY96 expression in patients with PD compared with healthy controls. The results of cell experiments showed that inhibiting LY96 expression alleviated adverse cellular effects by increasing cell viability, reducing apoptosis, and reducing oxidative stress. Gene set enrichment analysis showed that LY96 was positively correlated with T1 helper cells, T2 helper cells, neutrophils, natural killer T cells, myeloid-derived suppressor cells, macrophages, and activated CD4 cells, and may participate in PD through natural killer cell-mediated cytotoxicity pathways and extracellular matrix receptor interaction pathways.

CONCLUSION

These findings suggested that LY96 might be a novel potential biomarker for PD, and offer insights into its immunoregulatory role.

Novel insights into molecular signatures and pathogenic cell populations shared by systemic lupus erythematosus and vascular dementia

Although vascular dementia (VD) and systemic lupus erythematosus (SLE) may share immune-mediated pathophysiologic processes, the underlying mechanisms are unclear. This study investigated shared gene signatures in SLE versus VD, as well as their potential molecular mechanisms. Bulk RNA sequencing (RNAseq) and single-cell or single-nucleus RNAseq (sc/snRNAseq) datasets from SLE blood samples and VD brain samples were obtained from Gene Expression Omnibus. The identification of genes associated with both SLE and VD was performed using the weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. For the sc/snRNAseq data, an unbiased clustering pipeline based on Seurat and CellChat was used to determine the cellular landscape profile and examine intracellular communication, respectively. The results were subsequently validated using a mice model of SLE with cognitive dysfunction (female MRL/lpr mice). WGCNA and machine learning identified C1QA, LY96, CD163, and MS4A4A as key genes for SLE and VD. sc/snRNAseq analyses revealed that CD163 and MS4A4A were upregulated in mononuclear phagocytes (MPs) from SLE and VD samples and were associated with monocyte-macrophage differentiation. Intriguingly, LGALS9-associated molecular pathway, as the only signaling pathway common between SLE and VD via CellChat analysis, exhibited significant upregulation in cortical microglia of MRL/lpr mice. Our analyses identified C1QA, LY96, CD163, and MS4A4A as potential biomarkers for SLE and VD. Moreover, the upregulation of CD163/MS4A4A and activation of LGALS9 signaling in MPs may contribute to the pathogenesis of VD with SLE. These findings offer novel insight into the mechanisms underlying VD in SLE patients.

Use of immunoinformatics and the simulation approach to identify Helicobacter pylori epitopes to design a multi-epitope subunit vaccine for B- and T-cells

BACKGROUND

Helicobacter pylori cause a variety of gastric malignancies, gastric ulcers, and cause erosive diseases. The extreme nature of the bacterium and the implantation of this bacterium protects it against designing a potent drug against it. Therefore, employing a precise and effective design for a more safe and stable antigenic vaccine against this pathogen can effectively control its associated infections. This study, aimed at improving the design of multiple subunit vaccines against H. pylori, adopts multiple immunoinformatics approaches in combination with other computational approaches.

RESULTS

In this regard, 10 HTL, and 11 CTL epitopes were employed based on appropriate adopted MHC binding scores and c-terminal cut-off scores of 4 main selected proteins (APO, LeoA, IceA1, and IceA2). An adjuvant was added to the N end of the vaccine to achieve higher stability. For validation, immunogenicity and sensitization of physicochemical analyses were performed. The vaccine could be antigenic with significantly strong interactions with TOLK-2, 4, 5, and 9 receptors. The designed vaccine was subjected to Gromacs simulation and immune response prediction modelling that confirmed expression and immune-stimulating response efficiency. Besides, the designed vaccine showed better interactions with TLK-9.

CONCLUSIONS

Based on our analyses, although the suggested vaccine could induce a clear response against H. pylori, precise laboratory validation is required to confirm its immunogenicity and safety status.

A multi-epitope vaccine designed against blood-stage of malaria: an immunoinformatic and structural approach

Malaria is a complex disease caused by parasites of the genus Plasmodium and is the leading cause of morbidity and mortality worldwide. The most severe form of malaria disease is caused by Plasmodium falciparum. Thus, a combination of different approaches is needed to control malaria. Resistance to first-line drugs and insecticides, on the other hand, makes the need for an effective vaccination more urgent than ever. Because erythrocyte parasites cause the most clinical symptoms, developing a vaccination for this stage of infection might be highly beneficial. In this research, we employed various bioinformatics methods to create an efficient multi-epitope vaccine that induces antibodies against the blood stage of malaria infection. For this purpose, we selected the malaria PfGARP protein as the target here. The B, HTL epitopes, and epitope conservation were predicted. The predicted epitopes (including 5 B and 5 HTL epitopes) were connected using suitable linkers, and the flagellin molecule was used as an adjuvant to improve its immunogenicity. The final construct vaccine with 414 amino acids long was designed. The vaccine's allergenicity, antigenicity, solubility, physicochemical characteristics, 2D and 3D structure modeling, molecular docking, molecular dynamics simulation, in silico cloning, and immunological simulation were tested. In silico immune simulation results showed significantly elevated IgG1 and IgM and T helper cells, INF γ, IL 2, and B-cell populations after the injection of the designed vaccine. These significant computational analyses indicated that our proposed vaccine candidate might activate suitable immune responses against malaria. However, in vitro and in vivo studies are essential for further validation.

Effects of Linkers and Substitutions on Multitarget Directed Ligands for Alzheimer’s Diseases: Emerging Paradigms and Strategies

Alzheimer’s disease (AD) is multifactorial, progressive and the most predominant cause of cognitive impairment and dementia worldwide. The current “one-drug, one-target” approach provides only symptomatic relief to the condition but is unable to cure the disease completely. The conventional single-target therapeutic approach might not always induce the desired effect due to the multifactorial nature of AD. Hence, multitarget strategies have been proposed to simultaneously knock out multiple targets involved in the development of AD. Herein, we provide an overview of the various strategies, followed by the multitarget-directed ligand (MTDL) development, rationale designs and efficient examples. Furthermore, the effects of the linkers and substitutional functional groups on MTDLs against various targets of AD and their modes of action are also discussed.

HMGB1 augments cognitive impairment in sepsis-associated encephalopathy by binding to MD-2 and promoting NLRP3-induced neuroinflammation

BACKGROUND

Sepsis-associated encephalopathy (SAE) always manifests with severe inflammatory symptoms and cognitive impairment. High mobility group box 1 (HMGB1) is a pro-inflammatory cytokine. In this study we investigated the role of HMGB1 in SAE.

METHODS

An SAE mouse model was established through cecal ligation and puncture surgery and then injected with adenovirus short hairpin RNA (Ad-sh)-HMGB1 or Ad-sh-myeloid differentiation protein (MD-2). The cognitive impairment and pathological injury in mice of different groups were evaluated using the Morris water maze experiment, Y-maze test, tail suspension test, fear conditioning test, and haematoxylin-eosin staining. The expressions of HMGB1 (fully reduced and disulfide (ds)HMGB1), MD-2, and NLRP3 in SAE mice were determined. Then, levels of inflammatory cytokines were measured. The binding relation between HMGB1 and MD-2 was predicted and certified. Additionally, MD-2 was downregulated to verify the role of the binding of HMGB1 and MD-2 in neuroinflammation and cognitive impairment in SAE.

RESULTS

Expressions of HMGB1, MD-2, NLRP3, and inflammatory cytokines were enhanced in the SAE mouse model, which were in parallel with impaired cognitive function. HMGB1 silencing resulted in downregulated NLRP3 expression and alleviated neuroinflammation and cognitive impairment in SAE mice. Mechanically, dsHMGB1 bound to MD-2 to activate NLRP3, thereby exacerbating neuroinflammation and cognitive impairment in SAE mice. The limited binding of HMGB1 and MD-2 downregulated NLRP3 expression to alleviate neuroinflammation and cognitive impairment in SAE mice.

CONCLUSION

HMGB1 was overexpressed in SAE, and dsHMGB1 bound to MD-2 to activate NLRP3 inflammasome, inducing neuroinflammation and cognitive impairment in SAE.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

An in-silico approach: identification of PPAR-γ agonists from seaweeds for the management of Alzheimer's Disease

Alzheimer's Disease is a complex progressive neurodegenerative disorder characterized by neurofibrillary tangles and senile plaques in various parts of the brain particularly cerebral cortex affecting memory and cognition. Nuclear receptors such as Peroxisome proliferator-activated receptor γ [PPAR-γ] is reported to have a role in lipid and glucose homeostasis in the brain, reduces the synthesis of Aβ (beta-amyloid plaques) and also regulates mitochondrial biogenesis and inhibit the neuro-inflammation, which contributes for the improvement in the cognitive function in AD. Hence PPAR-γ is one of the newer targets for the researchers to understand the pathology of AD and to evolve the novel strategy to retard/reverse the progression of AD. PPAR-γ agonists such as Rosiglitazone and Pioglitazone have shown promising results in AD by decreasing neuro-inflammation and restoring glucose dysmetabolism leading to a reduction in neuronal deterioration. These agonists possess poor blood-brain permeability and are poor candidates for clinical use in AD. Therefore, search, design, and development for new PPAR- γ agonists with improved BBB penetration ability are imperative. The present work deals with the use of computational tools and techniques such as molecular docking, molecular dynamics to discover PPAR-γ agonists from the unexplored Seaweed Metabolite Database and predicts it's toxicological and physiochemical profile, thereby saving time and resources. Out of 1,110 seaweed compounds, the hit molecule BS052 displayed a strong binding affinity towards PPAR-γ, which possessed better lipid solubility indicating the potential to be considered as a PPAR-γ agonist, which may be useful in the management of AD.Communicated by Ramaswamy H. Sarma.

Synthesis, biological activity, molecular docking studies of a novel series of 3-Aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives as the acetylcholinesterase inhibitors

The acetylcholinesterase inhibitors play a critical role in the drug therapy for Alzheimer's disease. In this study, twenty-nine novel 3-aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives were synthesized and assayed for their human acetylcholinesterase (hAChE) inhibitory activities. Inhibitory ratio values of seventeen compounds were above 55% with 4c having the highest value as 77.19%. The compounds with the halogen atoms in the aromatic ring, and N,N-diethylamino or N,N-dimethylamino groups in the side chains at C-3 positions exhibited good inhibitory activity. SAR study was carried out by means of molecular docking technique. According to molecular docking results, the common interacting site for all compounds were found to be peripheral anionic site whereas highly active compounds were interacting with the catalytic active site too. HIGHLIGHTSA novel series of 3-aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives were synthesized and assayed for their human acetylcholinesterase (hAChE) inhibitory activities.The SAR study of the target 3-aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives was summarized.The active sites in the acetylcholinesterase were analyzed by molecular docking technique.Communicated by Ramaswamy H. Sarma.

Computational modeling for mutational analysis of nitrilase enzyme towards enhancement of binding empathy

Nitrilase enzyme (a green catalyst) is an industrially important enzyme which hydrolyses various nitrile compounds (containing -CN functional group) into amides and corresponding carboxylic acids. The current study explored the binding affinity and a method to enhance the catalysis activity of the enzyme using computational approaches. Four mutants were generated using sequential site-directed mutagenesis aiming that an increase in hydrogen bonds that will further increase binding efficiency towards the ligand. Molecular dynamics simulation was rigorously performed to check the stability of those mutants followed by docking to verify its interaction with the ligand. Various statistical dynamics analyses were performed to validate the structure. All the studies predict that built mutants are stable. Mutants 2 and 3 showed a better affinity towards acrylamide by forming the highest number of hydrogen bonds implying better catalysis. The binding affinity values of the Mutant 2 and Mutant 3 with acrylamide are -7.44 kcal/mol and -7.17 kcal/mol, respectively. This study may prove useful for the industry to develop efficient nitrilase enzymes with improved catalytic activity.Communicated by Ramaswamy H. Sarma.

Strategy of Fusion Covalent Organic Frameworks and Molecularly Imprinted Polymers: A Surprising Effect in Recognition and Loading of Cyanidin-3-O-glucoside

Seven magnetic covalent organic framework molecularly imprinted polymers with magnetic responsiveness, large specific surface area, and molecular recognition ability were successfully synthesized, suitable for the separation of cyanidin-3-O-glucoside from complex samples. Using a room-temperature synthesis strategy, covalent organic frameworks accompanied by imprinted sites were formed in one step on the functionalized magnetic nanoparticles with 1,6-hexanediamine. The composite materials were easy to prepare and showed high adsorption capacity (127 mg g^-1) together with favorable adsorption selectivity for cyanidin-3-O-glucoside over other flavonoids and phenolic acids. Magnetic covalent organic framework molecularly imprinted polymers exhibited good stability in 10 adsorption-desorption cycles. This work established a new method of extracting and separating high-purity anthocyanins and even unstable natural compounds.

Immunoinformatics approaches to explore Helicobacter Pylori proteome (Virulence Factors) to design B and T cell multi-epitope subunit vaccine

Helicobacter Pylori is a known causal agent of gastric malignancies and peptic ulcers. The extremophile nature of this bacterium is protecting it from designing a potent drug against it. Therefore, the use of computational approaches to design antigenic, stable and safe vaccine against this pathogen could help to control the infections associated with it. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a multi-epitopes subunit vaccine against H. Pylori. A total of 7 CTL and 12 HTL antigenic epitopes based on c-terminal cleavage and MHC binding scores were predicted from the four selected proteins (CagA, OipA, GroEL and cagA). The predicted epitopes were joined by AYY and GPGPG linkers. Β-defensins adjuvant was added to the N-terminus of the vaccine. For validation, immunogenicity, allergenicity and physiochemical analysis were conducted. The designed vaccine is likely antigenic in nature and produced robust and substantial interactions with Toll-like receptors (TLR-2, 4, 5, and 9). The vaccine developed was also subjected to an in silico cloning and immune response prediction model, which verified its efficiency of expression and the immune system provoking response. These analyses indicate that the suggested vaccine may produce particular immune responses against H. pylori, but laboratory validation is needed to verify the safety and immunogenicity status of the suggested vaccine design.

Emerging therapeutic potentials of dual-acting MAO and AChE inhibitors in Alzheimer's and Parkinson's diseases

No drug has been approved to prevent neuronal cell loss in patients suffering from Parkinson's disease (PD) or Alzheimer's disease (AD); despite increased comprehension of the underlying molecular causes, therapies target cognitive functional improvement and motor fluctuation control. Drug design strategies that adopt the "one protein, one target" philosophy fail to address the multifactorial aetiologies of neurodegenerative disorders such as AD and PD optimally. On the contrary, restoring neurotransmitter levels by combined combinatorial inhibition of cholinesterases, monoamine oxidases, and adenosine A_2A A receptors, in conjunction with strategies to counter oxidative stress and beta-amyloid plaque accumulation, would constitute a therapeutically robust, multitarget approach. This extensive review delineates the therapeutic advantages of combining dual-acting molecules that inhibit monoamine oxidases and cholinesterases and/or adenosine A_2A A receptors, and describes the structure-activity relationships of compound classes that include, but are not limited to, alkaloids, coumarins, chalcones, donepezil-propargylamine conjugates, homoisoflavonoids, resveratrol analogs, hydrazones, and pyrazolines. In the wake of recent advances in network biology, in silico approaches, and omics, this review emphasizes the need to consider conceptually informed research strategies for drug discovery, in the context of the mounting burden posed by chronic neurodegenerative diseases with complex aetiologies and pathophysiologies involving multiple signalling pathways and numerous drug targets.