Issue #50: Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis.

Signal of the Day

Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis.

The protozoan parasite Leishmania donovani is a major causative agent of visceral leishmaniasis (VL), a lethal disease posing significant public health challenges globally. Existing anti-VL drugs have become increasingly ineffective due to rising drug resistance, underscoring the urgent need for novel and effective therapeutic candidates. Computational approaches offer rapid and systematic methods for identifying potential drug targets and supporting rational drug design. This review discusses in silico molecular docking studies targeting various Leishmania proteins and their inhibitors, alongside the in vitro and in vivo validation of selected compounds, emphasizing their crucial roles in advancing antileishmanial drug discovery. In the review, we have focused on a molecular docking study and explored potential compounds with high binding energy toward protein targets of Leishmania. Following the in silico screening, our review highlights compounds that exhibit both in vitro and in vivo antileishmanial properties, allowing for an assessment of their therapeutic efficacy. Different Software is available for molecular docking, has been mentioned in the review. Overall conclusion of this review supports the computational approach in drug discovery before the in vitro and in vivo study, which can save cost and time efficiency as well.

Why this matters:

Also Worth Reading

Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Psoriasis is a chronic inflammatory skin disease with limited safe and effective treatments. Methylparaben, a widely used preservative in cosmetics, pharmaceuticals, and food, is an emerging environmental pollutant linked to immune-related skin disorders, but its role and mechanism in psoriasis remain unclear. This study explored its potential mechanism using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms. Methylparaben targets were retrieved from GeneCards and TCMSP, and psoriasis-related targets from CTD and GeneCards. Overlapping targets were screened with Venny 2.1.0. A PPI network was constructed via STRING, and core targets identified using Cytoscape 3.10.2. GO and KEGG enrichment analyses were performed on DAVID. Molecular docking evaluated the binding affinity of methylparaben with key targets. A total of 138 compound-related and 5,592 psoriasis-related targets were identified. Core targets such as INS, HIF1A, and PPARG are involved in regulating immune-inflammatory responses, keratinocyte proliferation and differentiation, and oxidative stress. GO analysis revealed enrichment in xenobiotic metabolism, lipopolysaccharide response, and metal ion binding. KEGG analysis highlighted pathways related to cancer, chemical carcinogenesis from reactive oxygen species, and drug metabolism via cytochrome P450 enzymes. Molecular docking showed stable binding of methylparaben to INS (-4.5 kcal/mol), HIF1A (-5.9 kcal/mol), and PPARG (-5.5 kcal/mol), primarily through hydrogen bonds and hydrophobic interactions. Methylparaben may exert its effects on psoriasis via multi-target and multi-pathway mechanisms, influencing inflammation, oxidative stress, and cellular regulation. These findings provide valuable insight into its toxicological mechanism and potential therapeutic application.

Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer’s Disease.

Introduction Alzheimer’s disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development. Methods This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins. Results Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with in vitro and in vivo experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets. Discussion The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement. Conclusion Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer’s disease.

UHPLC - QTOF-ESI-/MS characterised methanolic leaves extract of Momordica foetida Schumach. (Cucurbitaceae: Cucurbitales) possesses anti-Salmonella properties: In vitro, in vivo, molecular docking and molecular dynamics simulations approach.

Ethnopharmacological practices have long utilized Momordica foetida Schumach. (Cucurbitaceae: Cucurbitales) in traditional medicine. Notwithstanding its extensive utilization, the scientific validation of its therapeutic effects, particularly in the context of typhoid diseases, remains scarce. This study aimed to evaluate the chemical composition, curative effect and in silico prediction of M. foetida extract against S. typhimurium-induced typhoid fever in rats. UHPLC-QTOF-ESI/MS analysis examined the chemical composition of M. foetida. Then, in vitro anti-Salmonella properties of the extract were evaluated on Salmonella strains and isolates. DPPH, ABTS and FRAP assays evaluated its antioxidant properties. S. typhimurium-induced typhoid fever in rats was used to evaluate the anti-Salmonella properties of the extract. Molecular dynamics simulations validated the binding interactions between the identified compounds against SseK3 and DNA gyrase B. UHPLC-QTOF-ESI/MS analysis revealed the presence of nine compounds. In vitro anti-Salmonella test of the extract showed inhibitory activity against bacteria used with MIC between 64 and 512 μg/mL. Crude extract of M. foetida leaves showed variable activities on DPPH (RSa50: 157.73 ± 0.02 μg/mL), ABTS (RSa50: 143.49 ± 0.02 μg/mL), and FRAP (RSa50: 125.35 ± 3.4 μg/mL). The extract restored haematological and biochemical parameters close to the normal control. Molecular docking and dynamics simulations further supported these findings, demonstrating strong and stable binding interactions between momordicoside E with SseK3 and quercetin with DNA gyrase B. Finally, toxicity prediction revealed an acceptable safety profile for quercetin and momordicoside E. These results indicate clear evidence supporting the anti-Salmonella activity of M. foetida, and may be good potential drugs in typhoid treatment.

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Quick Reads

Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis.

The protozoan parasite Leishmania donovani is a major causative agent of visceral leishmaniasis (VL), a lethal disease posing significant public health challenges globally. Read more →

Validating the potential mechanism and therapeutic effect of Qinlian Jiangxia decoction (芩连姜夏汤) in the treatment of type 2 diabetes mellitus complicated with hyperlipidemia through network pharmacology, molecular docking, molecular dynamics simulation, and in vivo experiments

Subtimizer: Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates.

Kinases are pivotal cell signaling regulators and prominent drug targets. Read more →

Fusion of pyrene and phenanthrene through 5H-imidazo[1,2-a]azepine scaffolds: structural tuning for fluorescence labeling and bacterial imaging.

This study investigates polycyclic aromatic fluorophores featuring pyrene and phenanthrene fused through either an imidazo[1,2-a]azepinone or an imidazo[1,2-a]azepinol central ring. Read more →

ClpK as a Chaperone Hub: Computational Exploration of its Protein Interaction Landscape

Protein-protein interaction networks provide a foundation for understanding the molecular mechanisms within cells and are increasingly recognized as promising targets for therapeutic intervention. Read more →

Integrated network toxicology and molecular simulations uncover the molecular mechanism and core targets of DNBaP-induced lung cancer.

This study aims to comprehensively apply network toxicology and molecular dynamics simulation techniques to investigate the molecular mechanisms of DNBaP-induced lung cancer. Read more →

In silico design of novel multi-epitope peptide vaccine against Neospora caninum induced cattle abortion targeting extracellular GRA2 and Nc-p43 protein.

Neospora caninum (NC) is a protozoan infection causing neosporosis, a disease that leads to substantial economic loss in livestock, especially in cattle, sheep, and goats. Read more →

Structure-Based Design and In Silico Evaluation of a Lipophilic Cyclooctanoyl- Derivative as a Renin Inhibitor: Lessons from Withdrawn Aliskiren.

Renin, a key aspartic protease central to the renin-angiotensin-aldosterone system (RAAS), remains a therapeutic target for hypertension despite the withdrawal of the only approved direct renin inhibitor, Aliskiren, due to unfavorable drug-drug interactions and safety concerns. Read more →

Pipeline Tip

Verify FASTA headers for special characters that break Rosetta pipelines.

Resources & Tools

• Dataset: PDB-REDO - Optimized protein structure database with refined models.
• Dataset: CATH - Hierarchical protein domain classification for structure and function.
• Tool: HHSuite - Remote homology detection with HMM-HMM comparison. View all tools →
• Tool: MAFFT - Multiple sequence alignment with high speed and accuracy. View all tools →
• Event: Structural Biology Events (Open)
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Job: 5 Computational Biology jobs at Karolinska Institutet - Academic Positions at Academic Positions
• Job: COLCOM - Postdoctoral Researcher: Genomic Language Models for Bacterial Genomes - Academic Positions at Academic Positions

Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman