Computational biology and chemistry最新文献

Antimicrobial resistance (AMR) poses a growing global threat, with antibiotic-resistant infections becoming a leading cause of death worldwide. The present study explores natural cyanobacterial compounds as possible inhibitors of Escherichia coli DNA gyrase B (GyrB) which is a verified antibacterial target that is not present in higher eukaryotes. Because of the urgent need for novel antibacterial drugs, we identified nine drug-like candidates using lipinski's rule of five and ADMET profiling. Molecular docking revealed that Biselyngbyaside B and Smenamide A exhibited greater binding affinities in comparison to the co-crystallized inhibitor EOF, with a binding energy of -9.03 kcal/mol. Further molecular dynamics simulations revealed that the Biselyngbyaside B-DNA gyrase B complex surpassed both EOF and Smenamide A in terms of structural stability, compactness, and strong hydrogen bonding. Umbrella sampling was employed to estimate the binding free energy from thirty sampling simulations, and Biselyngbyaside B exhibited a significantly favourable ΔG bind of -91.66 kJ/mol, outperforming EOF (-68.93 kJ/mol) and Smenamide A (-36.4 kJ/mol). These findings clearly indicate a stronger and more stable interaction between Biselyngbyaside B and GyrB. Biselyngbyaside B continuously showed better pharmacokinetic characteristics, non-hepatotoxicity, and a greater binding affinity than previously documented DNA gyrase B inhibitors. This study emphasizes the integration of molecular dockings, molecular dynamics simulation, umbrella sampling, and ADMET analysis provided crucial quantitative insights into the identification of potent drug-like candidates for further validation. Overall, the Biselyngbyaside B was found to be the most promising lead compound for novel antibacterial drug development targeting DNA gyrase B.

Acid sphingomyelinases (aSMases) are enzymes involved in the repair of the plasma membrane in eukaryotic cells. However, neutral sphingomyelinases (nSMases) have also been shown to possess other roles in bacteria and eukaryotic microorganisms, especially as virulence factors. These enzymes exhibit structural conservation but are characterized by elusive homology and the lack of sequence signatures or motifs. In a previous study, we reported the structural features of the complete set of sphingomyelinases (SMases) in Entamoeba histolytica and Trichomonas vaginalis, showing structural homology and functional differences in two aSMases from E. histolytica (EhSMase). However, the approach was limited due to the AlphaFold3 source code not being publicly available at the time. In this report, the structural transitions in the aSMases from T. vaginalis (TvSMase) were measured using open-source AlphaFold3 and collective motions of proteins via Normal Mode Analysis in internal coordinates. They compared them with the models from aSMase4 (EHI_100080) and aSMase6 (EHI_125660) from E. histolytica, containing different combinations of ligands. Using full-length sphingomyelin and the Mg²⁺ and Co²⁺ ions, where Co²⁺ was shown to inhibit the enzymes of both organisms, we demonstrate that the enzymes exhibit limited flexibility and deformability, except for the T. vaginalis TVAG_271580 enzyme, which displays high structural deformability. This contrasts with the inhibitory mechanism elicited by Co²⁺ as shown previously. TVSMase3 (TVAG_222460) could not be modelled with the sphingomyelin in the active site pocket, suggesting a regulatory role rather than a functional active enzyme. Additional physicochemical parameters calculated for T. vaginalis enzymes suggest unstable structures and high internal mobility (estimated using the Internal Coordinate method), which may be associated with the functional role of these enzymes. The results presented here open an avenue for searching for novel inhibitors of aSMases that target their physical properties, which could potentially complement treatment to control the parasite burden. These inhibitors could be valuable for further studying the role of these enzymes in parasite pathobiology and, potentially, as therapeutic targets.

Drug repurposing represents a promising approach towards drug discovery that has the potential to improve patient outcomes and address unmet medical needs. This study attempted to repurpose existing sulfonamide drugs in search of novel anticancer drugs because of their effectiveness in treating bacterial infections. A search was made in DrugBank for Sulfonamide, and 25 drugs with functional groups like SH, OSO, CS, and -S- were chosen for our study. The drug properties, such as dipole moment, volume, polarisability, highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and electrostatic potential map, were analysed through a quantum mechanical approach at different functionals: M062X, M06HF, and B3LYP with basis sets (6-31 +G*, LANL2DZ). The electrostatic potential map was analyzed to determine the magnitude, size, and distribution of the electron cloud surrounding the sulfur atoms. Analysis of NBO (Natural Bond Orbital) and NCI (Non-Covalent Interaction) plots confirmed the presence of intramolecular hydrogen bonding in the sulfonamide drugs. Furthermore, the frontier molecular orbitals (HOMO and LUMO) and the band gap were thoroughly examined for all drugs to identify the best electron acceptors and donors. Docking analysis was performed to have a lock-and-key model of 25 sulfonamide drugs with the most promising cancer-targeted protein (1ZZ1): histone deacetylases (HDACs). The best drug orientation (optimal position) was discussed and compared with the control ligand SHH based on the analysis of binding affinity and root mean square deviation (RMSD). Binding affinity of control ligand SHH is -8.1 kcal/mol for the 2nd pose, which matches exactly with 1ZZ1 SHH ligand. The drugs Tolazamide, Fezolinetant, Ensulizole, Taurolidine, Acetohexamide, Isoxicam, Sulfamethizole, Sulfamethoxazole, Sulfapyridine, Sulfaphenazole, and Dodecyl sulphate were observed to exhibit high molecular volume, polarizability, dipole moment and significant HOMO, LUMO values, which are recommended for further quantum mechanical calculations. The findings of this study will be essential for evaluating the properties of sulfonamide drugs from a drugbank using a variety of analyses in order to repurpose them as novel anticancer drugs. Quantum mechanical calculations will be performed on the optimal docking poses in future work. Keywords: Sulfonamide drugs, Docking, Histone deacetylases, Lipinsk's rule, Binding affinity.

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel essential for cardiac action potential repolarization. Drug-induced hERG inhibition can prolong the QT interval, causing severe heart diseases like torsade de pointes and fatal arrhythmias. In pharmaceutical chemistry, early prediction of hERG blockers is crucial to mitigate cardiotoxicity risks, minimizing drug withdrawals and economic losses in discovery. To address this, an interpretable multi-modal molecular representation cross-learning framework (MMRCL) is developed, integrating multi-dimensional molecular fingerprints and molecular graphs to enrich structural features. MMRCL combines a dual-channel message passing neural network (MPNN) for atom- and bond-level structural features with a multi-layer perceptron for molecular fingerprint-based semantics. A multi-head cross-attention mechanism adaptively fuses features across modalities, enabling deep correlation modeling, followed by a fully connected neural network classifier. Extensive evaluation on an internal dataset (12,518 compounds with high-dimensional fingerprints and graph features) and three external test sets demonstrates MMRCL's superior performance compared to seven state-of-the-art baseline models, achieving the best AUC of 0.8895, PRC of 0.9073, and MCC of 0.6146 on the internal set. Interpretability analysis identifies key toxic substructures linked to hERG-blocking activity, aiding structure-activity relationship exploration. Ablation studies further confirm the contributions of multi-modal input and attention-based fusion. MMRCL achieves superior prediction accuracy and generalization, also enhances model interpretability, providing actionable insights for medicinal chemists.

Staphylococcus aureus is a leading cause of both community- and hospital-acquired infections, and the growing prevalence of antimicrobial resistance complicates clinical management worldwide. This study investigated the epidemiology, resistance trends, multidrug resistance (MDR) patterns, and the role of machine learning (ML) in predicting antibiotic susceptibility in Saudi Arabia. A total of 18,003 microbiology reports (2019-2024) were analyzed, identifying 2506 S. aureus isolates. Susceptibility testing included 31 antibiotics representing 11 pharmacological classes. Predictive ML models (Random Forest, Logistic Regression, Gradient Boosting) were trained and evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Wound (24 %) and blood (23 %) were the most frequent sources of S. aureus. High resistance (>70 %) was observed for β-lactams, fluoroquinolones, and macrolides/lincosamides, while glycopeptides, oxazolidinones, and lipopeptides maintained excellent activity (<10 % resistance). MDR occurred in 30 % of isolates, XDR in 0.6 %, and no PDR isolates were detected. Among ML models, Random Forest achieved the best overall performance across most antibiotics, Logistic Regression was optimal for ampicillin, and Gradient Boosting for linezolid. Vancomycin, linezolid, penicillin, and SXT achieved precision and recall above 0.92, demonstrating strong predictive reliability. S. aureus remains a major clinical threat in Saudi Arabia, with high MDR rates but preserved efficacy of last-line antibiotics. This study highlights the value of combining multi-center surveillance with interpretable machine learning approaches to support antimicrobial stewardship, enhance early resistance prediction, and inform data-driven clinical decision-making, particularly in settings where rapid molecular diagnostics may be limited.

Virtual screening has emerged as one of the most impactful in silico approaches for the identification of novel drug candidates, substantially reducing the cost and time associated with high-throughput screening (HTS). Ongoing efforts focus on exploring large-scale libraries of drug-like molecules to identify candidates with favourable pharmacological properties. In this study, we propose an applicability domain-based virtual screening strategy that extends beyond conventional approaches by prioritising compounds with ADMET profiles comparable to marketed drugs. To further enhance predictive performance, we developed a QSAR model on PI3K ligands using Light Gradient Boosting Machine (LGBM), which achieved an R2 value of 0.799, thereby providing an additional layer of validation for compound selection. The phosphoinositide 3-kinase (PI3K) pathway, a critical regulator of cell growth, survival, metabolism, and proliferation, is frequently dysregulated in multiple cancers and other diseases. Repurposing existing drugs that modulate PI3K activity offers the potential to accelerate therapeutic development while mitigating the challenges of de novo drug discovery. To demonstrate the utility of our approach, we screened two compound libraries from Enamine-a hit-like locator library (>400,000 molecules) and a kinase-focused library (>64,000 molecules)-against the PI3K-α isoform. In addition, a set of 1367 FDA-approved drugs was screened to identify potential candidates for repurposing. From these extensive datasets, three small molecules from the Enamine libraries were identified with favourable drug-like properties and synthetic accessibility compared with existing PI3K-α inhibitors. Furthermore, one FDA-approved drug demonstrated potential PI3K-α inhibitory activity. Pharmacophore mapping provided additional validation of their drug-likeness. Importantly, wet-lab evaluation of the FDA-approved drug confirmed its inhibitory activity, thereby supporting the computational predictions. Overall, our integrated in silico and experimental framework highlights promising PI3K-α inhibitors, underscoring the potential of applicability domain-based virtual screening and QSAR modelling for both drug discovery and repurposing.

Background: Immune thrombocytopenia (ITP) is characterized by increased platelet clearance and decreased platelet production. Hematopoietic stem and progenitor cell (HSPCs) abnormalities are a key mechanism of ITP, contributing to megakaryocyte defects and aberrant lymphocyte differentiation. Ningxue Shengban Decoction (NXSBD) has proven to be an effective therapeutic option for ITP.

Aim: This study aimed to explore the therapeutic mechanism of NXSBD in ITP.

Method: First, we evaluated the therapeutic effect of NXSBD on thrombocytopenia. T-lymphocyte subsets were analyzed by flow cytometry, megakaryocyte features were examined by HE staining, serum cytokines were quantified by ELISA, and hepatic/renal safety indices were measured by MS. Following the identification of core targets and pathways via network pharmacology, their expression in bone marrow cells was confirmed by single-cell RNA sequencing analysis. Pseudo-temporal analysis then tracked the dynamics of these targets during HSC lineage commitment, and molecular docking finally confirmed strong binding affinities with NXSBD's constituents.

Results: NXSBD significantly ameliorated thrombocytopenia in ITP mice by rebalancing T-cell subsets and modulating key inflammatory cytokines, while also promoting the generation of thrombocytogenic megakaryocytes in the bone marrow. The core targets of NXSBD (RELA, IKBKB, AKT1, TP53, MAPK1, JUN, and FOS) were found to be present in hematopoietic stem cells (HSCs) and were involved in HSC differentiation into megakaryocytes and T lymphocytes. Molecular docking confirmed strong binding affinities between NXSBD constituents and these core targets. In conclusion, our findings demonstrate that NXSBD alleviates ITP through a multi-target mechanism that may participate in megakaryocyte production from HSCs and contribute to the restoration of peripheral T-cell homeostasis.

Objective: Insomnia is widely recognized as a key risk factor for major depressive disorder (MDD). However, the potential molecular mechanisms and the underlying interactions among them remain to be elucidate.

Methods: In NHANES, an insomnia-like high-risk sleep phenotype was linked to an increased risk of MDD, and Mendelian randomization (MR) provided evidence for a potential causal effect of the genetic liability to insomnia. Core genes were identified via PPI network construction and machine learning analyses, integrated with the GEO database and insomnia-related genes. Subsequently, the clinical relevance of these genes was validated, and the performance of the model was evaluated using external datasets. A functional enrichment analysis of the core genes was conducted to explore the related biological pathways. The impacts of core genes on the immune microenvironment were explored via immune infiltration and cell-cell interaction analyses. Moreover, promising candidate therapeutic compounds were identified through drug enrichment and molecular interaction analyses.

Results: An insomnia-like high-risk sleep phenotype showed a consistent association with an increased MDD risk, and MR suggested a potential causal connection. The diagnostic model, constructed using the core genes ATG7 and JAK2, demonstrated good predictive abilities and showed potential for clinical application. Enrichment analyses highlighted autophagy- and inflammation-related pathways, accompanied by altered immune-cell signatures, supporting the involvement of an autophagy-inflammation axis in MDD. Molecular docking and dynamics indicated that emodin could interact stably with JAK2, which warrants experimental validation.

Conclusion: ATG7 and JAK2 are insomnia-associated genes linked to autophagy- and inflammation-related pathways in MDD. The ATG7/JAK2-based diagnostic model and the in silico-prioritized compound emodin provide testable hypotheses for future mechanistic investigations and translational exploration, pending experimental validation.