Issue #44: Immunoinformatics and molecular docking reveal potential multi-epitope vaccine against Pseudomonas aeruginosa.

Signal of the Day

Immunoinformatics and molecular docking reveal potential multi-epitope vaccine against Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a common opportunistic pathogen and a leading cause of hospital-acquired pneumonia, yet there is currently no approved vaccine to prevent its infections. This study utilizes immunoinformatics to identify cytotoxic T-lymphocyte (CTL) epitopes derived from conserved regions of 6 key virulence factors: Pili, FliD, AlgF, PelG, Exoenzyme T, and XcpQ. Conserved peptide fragments were identified using the Protein Variability Server. The CTL epitopes were evaluated for immunogenicity, antigenicity, post-translational modifications, allergenicity, cross-reactivity, toxicity, and population coverage analysis. Molecular docking between human leukocyte antigens (HLAs) and the corresponding CTL epitopes, along with binding affinity analysis, was also conducted. A multi-epitope vaccine (PaMEV) construct was designed using selected epitopes, and its secondary and tertiary structures were predicted, refined, and validated. All selected epitopes were highly conserved (Shannon index ≤0.1) and showed strong HLA binding (half maximal inhibitory concentration ≤500 nM). They were predicted to be non-allergenic, non-toxic, and non-cross-reactive. Molecular docking revealed stable HLA-epitope complexes with 8-14 hydrogen bonds and high binding affinity (values of the binding free energy <0 and dissociation constant <100 nM). A PaMEV was designed using the 6 CTL epitopes, and structure analysis confirmed its stability and effective epitope presentation. The selected epitopes showed strong potential for inclusion in a peptide-based PaMEV, with favorable immunogenicity and docking results supporting its design. The final construct exhibited structural stability and strong HLA interactions, suggesting it as a promising vaccine candidate against P. aeruginosa. Experimental validation through in vitro and in vivo studies is recommended.

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Also Worth Reading

Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds.

Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy for drug development. In this study, we utilized AlphaFold to predict the structures of target proteins from Schistosoma mansoni and S. japonicum, followed by virtual molecular screening to identify potential inhibitors. Among 202 potential therapeutic targets, we identified 37 proteins with high-accuracy structural predictions suitable for molecular docking with 14,600 compounds. This screening yielded 268 candidate compounds, which were further evaluated ex vivo for activity against both adult and juvenile S. mansoni and S. japonicum. Seven compounds exhibited strong anti-schistosomal activity, with HY-B2171A (Carubicin hydrochloride, CH) emerging as the most potent. CH was predicted to target the splicing factor U2AF65, and knockdown of its coding gene Smp_019690 resulted in a phenotype similar to CH treatment. RNA sequencing revealed that both CH treatment and Smp_019690 RNA interference (RNAi) disrupted splicing events in the parasites. Further studies demonstrated that CH impairs parasite viability by inhibiting U2AF65 function in mRNA splicing regulation. By integrating RNAi-based target identification with structure-based virtual screening, alongside ex vivo phenotypic and molecular analyses of compound-treated schistosomes, our study provides a comprehensive framework for anti-schistosomal drug discovery and identifies promising candidates for further preclinical development.

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.

Advancing Drug Repurposing for Rheumatoid Arthritis: Integrating Protein-Protein Interaction, Molecular Docking, and Dynamics Simulations for Targeted Therapeutic Approaches.

Background : Rheumatoid arthritis (RA) is a systemic chronic inflammatory autoimmune disease causing progressive joint destruction, resulting in significant morbidity and increased mortality. Despite advances in treatment, current pharmacological options, including NSAIDs, DMARDs, and biological agents, have limitations in tissue repair and can lead to severe side effects. Objectives : This study aims to explore drug repurposing as a viable approach to identify novel therapeutic agents for RA by utilizing existing FDA-approved drugs. Methods : We applied an integrated computational strategy that uniquely combines network pharmacology with molecular docking and dynamics simulations. The process began with the construction of a protein-protein interaction (PPI) network from 2723 RA-associated genes, which identified five central targets: TNF-α, IL-6, IL-1β, STAT3, and AKT1. We then built protein-drug interaction (PDI) networks by screening 2637 FDA-approved drugs against these targets. Critically, the top candidates from this network analysis were not just docked but were further validated using 100 ns molecular dynamics simulations to thoroughly evaluate binding affinity, complex stability, and interaction dynamics. Results : This multi-tiered computational workflow identified Rifampicin, Telmisartan, Danazol, and Pimozide as the most promising repurposing candidates. They demonstrated strong binding affinities and, importantly, formed stable complexes with TNF-α, IL-6, IL-1β, and STAT3, respectively, in dynamic simulations. The key innovation of this study is this sequential funnel approach, which integrates large-scale network data with atomic-level simulation to prioritize high-confidence drug candidates for RA. Conclusions : In conclusion, this study highlights the potential of repurposing FDA-approved drugs to target key proteins involved in RA, offering a cost-effective and time-efficient strategy to discover new therapies.

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Pseudomonas aeruginosa is a common opportunistic pathogen and a leading cause of hospital-acquired pneumonia, yet there is currently no approved vaccine to prevent its infections. Read more →

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Resources & Tools

• Dataset: BFD - Big Fantastic Database for deep learning protein modeling.
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• Tool: AlphaFold2 - Deep learning system for high-accuracy protein structure prediction. View all tools →
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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman