Morpholine-modified thiosemicarbazones and thiazolidin-4-ones against Alzheimer's key enzymes: From synthesis to inhibition.

To discover a novel succinate dehydrogenase (SDH) inhibition fungicide, a series of 4-(3,4,5-trifluorophenyl)but-3-en-2-amides were designed by a combination of 3D-QSAR and substructure splicing of fluxapyroxad and pydiflumetofen. In vitro fungicidal bioassays demonstrated that 5a exhibited a broad-spectrum of fungicidal activity against Alternaria solani, Cercospora arachidicola, Fusarium graminearum, Fusarium verticillioides, Rhizoctonia solani, and Sclerotinia sclerotiorum, with EC50 values falling between 3.74 and 19.8 μg/mL. Enzymatic activity assays revealed that the inhibition of 5a was lower than that of fluxapyroxad, with an IC50 of 12.5 vs 0.04 μg/mL, respectively. Scanning electron microscopy showed that 5a was effective in inhibiting fungal hyphae growth. Molecular docking and molecular dynamics (MD) simulations showed that 5a exhibited binding patterns similar to those of fluxapyroxad and pydiflumetofen. Binding free energy calculations and decomposition analyses provided insight into the residue interactions of 5a during the MD simulation. These findings suggest that 5a is a novel fungicidal candidate for further investigation.

Immunoinformatics-driven design of a multi-epitope vaccine targeting non-typhoidal Salmonella's type IV secretion system VirB4, VirB5, and VirB6 proteins.

Shared and compound-specific pathways of bisphenol A and its analogues in male infertility: An integrative approach combining network toxicology, omics analysis, and molecular docking.

Cyclin-dependent kinase 9 (CDK9) is a serine/threonine kinase crucial for transcriptional elongation via phosphorylation of the C-terminal domain (CTD) of RNA polymerase II, thereby enabling productive mRNA synthesis. Given its pivotal role in gene expression, CDK9 represents a validated therapeutic target in cancer research. This study employed a sequential bioisosteric replacement strategy to identify novel CDK9 inhibitors. Initially, a library of 17,633 compounds was generated by replacing the core scaffold of 134 known inhibitors. Pharmacophore-based virtual screening reduced this set to 3,754 candidates, from which a highly predictive QSAR model identified compound 50224760_85 as the most promising lead. A second round of bioisosteric substitution yielded 66,966 novel structures, increasing chemical diversity and predicted bioactivity. QSAR analysis highlighted compounds 9550, 9724, and 31801 as representative molecules with favorable predicted properties and structural novelty. Density functional theory (DFT) calculations further revealed distinct electronic and chemical reactivity profiles across these top-ranked ligands. Molecular dynamics simulations demonstrated that all three ligands maintained enhanced stability within the ATP-binding pocket of CDK9 relative to the parent compound. Consistently, binding free energy and per-residue decomposition analyses confirmed robust interactions with catalytically relevant residues, supporting their potential as potential CDK9 inhibitors. Overall, this integrative strategy identified a rich dataset of CDK9-targeting ligands with predicted Ki values comparable to the most potent experimental compounds reported to date.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-31230-8.

Increasing concern about highly pathogenic avian influenza A (H5N1) is prompting the development of new antivirals directed toward conserved viral entities that are resistant to mutational escape. Here, at a multi-scale and precision-guided computational level, we employed a set of procedures to identify potential small-molecule inhibitors of the influenza virus PA endonuclease, a central component of the viral RNA polymerase complex responsible for cap-snatching of mRNA transcription. Through the structurally diverse drug-like dataset, we initiated structure-based virtual screens against the PA catalytic domain and received 1,500 high-affinity candidates. Top-scoring candidates were optimized using quantum mechanical density functional theory (DFT) computations and electron reactivity/orbital distribution analyses. Through re-docking of optimized geometries using DFT, lead molecules were subjected to exhaustive 1-microsecond molecular dynamics (MD) simulations and MM/GBSA binding free energy decomposition and principal component analysis (PCA) sampling of dynamic conformational topographies. Free energy surface mapping of low-energy basins and superimposition validation of pose stabilities verified sub-angstrom deviations. Significantly, 24782939 registered the least thermodynamic profile (ΔG = -45.8kcal/mol), greatest H-bond persistence, and computed pIC50 of 8.17 using a machine-learned predictive model trained against structurally diverse chemical scaffolds. This multi-scale, integrated framework, involving atomic, energetic, and predictive scales, holds promise for translational applications of computational pipelines in antiviral discovery. Our findings nominate 24,782,939 as a highly promising inhibitor of PA endonuclease and have the potential to be developed into a next-gen therapeutic candidate against influenza A viruses.

The machine learning-based QSAR modeling procedure, molecular generations, and molecular dynamic simulations were applied to virtually screen the DNA polymerase theta inhibitors. The DNA polymerase theta (Polθ or POLQ) is an attractive target for treatments of homologous recombination deficient (such as BRCA deficient) cancers. There are no approved drugs for targeting POLQ, and only one inhibitor is in Phase II clinical trials; thus, it is necessary to develop novel POLQ inhibitors. To build machine learning models that predict the bioactivities of POLQ inhibitors. To build molecular generation models that generate diverse molecules. To virtually screen the generated molecules by the machine learning models. To analyze the binding modes of the screening results by molecular dynamic simulations. In the present work, 325 inhibitors with POLQ polymerase domain bioactivities were collected. Two machine learning methods, random forest and deep neural network, were used for building the ligand- and structure-based quantitative structure-activity relationship (QSAR) models. The substructure replacement-based method and transfer learning-based deep recurrent neural network method were used for molecular generations. Molecular docking and consensus QSAR models were carried out for virtual screening. The molecular dynamic simulations and MM/GBSA binding free energy calculation and decomposition were used to further analyze the screening results. The MCC values of the best ligand- and structure-based consensus QSAR models reached 0.651 and 0.361 for the test set, respectively. The machine learning-based docking scores had better-predicted ability to distinguish the highly and weakly active poses than the original docking scores. The 96490 molecules were generated by both molecular generation methods, and 10 molecules were retained by virtual screening. Four favorable interactions were concluded by molecular dynamic simulations. We hope that the screening results and the binding modes are helpful for designing the highly active POLQ polymerase inhibitors and the models of the molecular design workflow can be used as reliable tools for drug design.

Linker-dependent modulation of anti-CD22 scFv antibody stability and avidity: Combined structural and experimental insights.

Deciphering the mucin defense mechanisms against SARS-CoV-2 using molecular simulations.

Structure-guided discovery of tau-phosphorylating kinase DYRK1A inhibitors for therapeutic targeting of neuroinflammatory diseases: Insights from microsecond MD simulation and MMPBSA analyses.

A flavoenzyme, UDP-galactopyranose mutase (UGM), serves as a pivotal enzyme catalysing the conversion of UDP-galactopyranose (galP) into UDP-galactofuranose (galF), a metabolite exclusively present in pathogenic microorganisms, including filarial parasites. The galF plays a critical role in various pathogenic processes, like cell wall biosynthesis, virulence enhancement, and cuticle formation in filarial parasites. Notably, the absence of galF in humans renders, UGM an attractive and promising drug target for developing potent antifilarial therapeutics. In this study, we employed advanced bioinformatics approaches to identify effective antifilarial drug candidates. The UGM enzyme fromBrugia malayi (BmUGM) was meticulously modelled and subsequently utilized for molecular docking studies against 20 triazolothiadiazine analogues using the AutoDock program. Among these, eight compounds exhibiting high binding affinities, ranging from - 8.7 to - 10.5kcal/mol, were selected for further protein-ligand MD simulations. Post-simulation analyses, encompassing MM-PBSA and binding free energy decomposition, demonstrated that two triazolothiadiazine analogues, namely D4 and D8, exhibited exceptionally high binding free energies of - 29.76kcal/mol and - 27.50kcal/mol, respectively. These values exceeded the binding free energy of the natural substrate galP, which was calculated at - 20.01kcal/mol. Furthermore, binding free energy decomposition analysis pinpointed critical binding site residues Tyr168, Trp184, Tyr326, Tyr335, Arg336, Tyr405, and Gln475 as essential mediators of the protein-ligand interactions. Additionally, ADMET and DFT quantum mechanical calculations confirmed that the triazolothiadiazine analogues exhibit low toxicity profiles and favourable chemical reactivity. Based on these findings, we propose that the identified ligand molecules hold potential as potent inhibitors of BmUGM, with broad-spectrum efficacy against all life stages of filarial parasites.

Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors that could target mIDH1, which may provide more effective treatment options for patients with related tumors. In present study, combines machine learning-based QSAR models and structure-based virtual screening to screen a series of potential IDH1 inhibitors from the Coconut databases. The QSAR model predictions indicate that the hit compounds have high binding affinity to the target protein, and its pIC50 value was found to be considerably larger than that of AGI-5198. The RMSD and Rg analysis demonstrated that all of the ligand-protein complexes exhibited a stable state throughout the simulation period. Furthermore, the binding free energy decomposition and per-residue contribution of the IDH1R132H-inhibitor complex revealed key fragments of the inhibitor interacting with residues ALA-111, PRO-118, ARG-119, LE-128, ILE-130, ITRP-267, VAL-281, and TYR-285 in the binding site of IDH1R132H. This investigation indicates that CNP0047068, CNP0029964, and CNP0025598 have the potential to be targeted inhibitors of IDH1R132H mutants through further optimization, providing new insights for discovering novel lead scaffolds in this domain.

Streptococcus pneumoniae (S. pneumoniae) Sortase A (SrtA) anchors virulence proteins to the surface of the cell wall by recognizing and cleaving the LPXTG motif. These toxins help bacteria adhere to and colonize host cells, promote biofilm formation, and trigger host inflammatory responses. Therefore, SrtA is an ideal target for the development of new preparations for S. pneumoniae. In this study, we found that phloretin (pht) and phlorizin (phz) exhibited excellent affinities for SrtA based on virtual screening experiments. We analyzed the interactive mechanism between pht, phz, and alnusone (aln, a reported S. pneumoniae SrtA inhibitor) and SrtA based on molecular dynamics simulation experiments. The results showed that these inhibitors bound to the active pocket of SrtA, and the root mean square deviation (RMSD) and distance analyses showed that these compounds and SrtA maintained stable configuration and binding during the assay. The binding free energy analysis showed that both electrostatic forces (ele), van der Waals forces (vdw), and hydrogen bonds (Hbonds) promoted the binding between pht, phz, and SrtA; however, for the binding of aln and SrtA, the vdw force was much stronger than ele, and Hbonds were not found. The binding free energy decomposition showed that HIS141, ILE143, and PHE119 contributed more energy to promote pht and SrtA binding; ARG215, ASP188, and LEU210 contributed more energy to promote phz and SrtA binding; and HIS141, ASP209, and ARG215 contributed more energy to promote aln and SrtA binding. Finally, the transpeptidase activity of SrtA decreased significantly when treated with different concentrations of pht, phz, or aln, which inhibited S. pneumoniae biofilm formation and adhesion to A549 cells without affecting normal bacterial growth. These results suggest that pht, phtz, and aln are potential materials for the development of novel inhibitors against S. pneumoniae infection.

Background: Human African trypanosomiasis (HAT), caused by Trypanosoma brucei, is a neglected tropical disease with limited treatments, highlighting the pressing need for new drugs. Cell division cycle-2-related kinase 12 (CRK12), a pivotal protein involved in the cell cycle regulation of T. brucei, has emerged as a promising therapeutic target for HAT, yet effective CRK12 inhibitors remain lacking. Methods: An integrated strategy combining computational modeling, virtual screening, molecular dynamics (MD) simulations, and experimental validation was adopted to discover potential inhibitors against CRK12. By using the predicted and refined 3D structure of CRK12 from AlphaFold2 and MD simulation, over 1.5 million compounds were screened based on multiple-scale molecular docking, and 26 compounds were selected for evaluation of biological activity based on anti-T. brucei bioassays. Dose–response curves were generated for the most potent inhibitors, and the interaction mechanism between the top four compounds and CRK12 was explored by MD simulations and MM/GBSA binding free energy analysis. Results: Of the 26 compounds, six compounds demonstrated sub-micromolar to low-micromolar IC50 values (0.85–3.50 µM). The top four hits, F733-0072, F733-0407, L368-0556, and L439-0038, exhibited IC50 values of 1.11, 1.97, 0.85, and 1.66 µM, respectively. Binding free energy and energy decomposition analyses identified ILE335, VAL343, PHE430, ALA433, and LEU482 as hotspot residues for compound binding. Hydrogen bonding analysis demonstrated that these compounds can form stable hydrogen bonds with the hinge residue ALA433, ensuring their stable binding within the active site. Conclusions: This study establishes a robust and cost-effective pipeline for CRK12 inhibitor discovery, identifying several novel inhibitors demonstrating promising anti-HAT activity. The newly discovered scaffolds exhibit structural diversity distinct from known CRK12 inhibitors, providing valuable lead compounds for anti-trypanosomal drug development.

Osteoporosis, characterized by excessive osteoclast activation, is mediated through the RANKL/RANK/OPG signaling axis. While flavonoids from Eucommia ulmoides (EU) have demonstrated anti-osteoclastogenic activity, their atomic-level mechanisms remain elusive. Here, we investigated six EU-derived flavonoids (cyrtominetin, quercetin, syringetin, genistein, ombuin, and kaempferol) targeting RANKL using integrated computational approaches. Molecular docking revealed strong binding affinities (Total_Score > 4.0) for all compounds, with cyrtominetin exhibiting the highest affinity (-50.205 kJ/mol via MM-PBSA), primarily through hydrogen bonds with Gly178, His180, Lys181, and Asn295. Moreover, most flavonoids interacted with RANKL by forming strong hydrogen bonds with Gly178 and Asn295, exhibiting higher binding affinity that was identified as essential for the activity. All-atom molecular dynamics simulations (100 ns) confirmed complex stability, demonstrating: low RMSD fluctuations (< 4.0 Å) and compact Rg values (16.0–17.0 Å). Notably, binding free energy decomposition identified both electrostatic and van der Waals contributions as critical for stabilization. These results identify cyrtominetin as a promising lead compound for RANKL inhibition, providing structural insights for designing flavonoid-based therapeutics against osteoporosis.

NLRP3 (Nucleotide-binding oligomerization domain, LRR and pyrin domain-containing protein 3) is a pivotal regulator of inflammation, with strong implications in gout, neurodegenerative diseases, and various inflammatory conditions. Consequently, the exploration of NLRP3 inhibitors is of great significance for the treatment of diseases. MCC950, NP3-146, compound (3), and YQ128 are four highly bioactive NLRP3 inhibitors that show great potential; however, their mechanism of action is currently limited to targeting the ATP binding region (NACHT site) of the NLRP3 protein. To gain deeper insights into the defining features of NLRP3 inhibitors and to develop more potent inhibitors, it is imperative to elucidate the interaction mechanism between NLRP3 and these inhibitors. In this study, we employ a comprehensive computational approach to investigate the binding mechanism between NLRP3 and representative inhibitors. Utilizing the molecular mechanics/generalized Born surface area (MM/GBSA) method, we calculate the binding free energy and pinpoint the key residues involved in the binding of the four inhibitors to NLRP3. The decomposition of binding free energy by the MM/GBSA method reveals that the residues Val71, Arg195, Ile255, Phe419, Arg422, and Met505, situated around the binding pocket, play a crucial role in conferring the high bioactivity of NLRP3 inhibitors. Furthermore, pharmacophore analysis of the four NLRP3 complexes indicates that the primary interaction between the inhibitors and NLRP3 was mainly hydrophobic interaction. Our study provides a profound understanding of the interaction mechanism between NLRP3 and its inhibitors, identifies the key residues involved, and provides theoretical guidance for the design of more efficient NLRP3 inhibitors.

Abstract The binding pocket of the main protease (M pro ) is highly conserved and recognized as a promising target for designing anti‐COVID‐19 inhibitors. Given the significant role of traditional Chinese medicine in combating SARS‐CoV‐2, over 20,000 small molecules derived from traditional Chinese medicine were virtually screened against M pro , resulting in the identification of TCM11135 ( Hc1 ), TCM20595 ( Hc2 ), TCM22179 ( Hc3 ), and TCM22701 ( Hc4 ) in the present study. Subsequent binding free energy calculations based on molecular dynamics simulations indicated that Hc1 and Hc4 exhibit favorable binding affinities for M pro . By employing binding free energy decomposition and hydrogen bond analysis, this study uncovered the key residues in M pro that contribute significantly to the binding energy or form hydrogen bonds with Hc1 and Hc4 , thereby providing valuable insights for the optimization of these compounds. Additionally, the predicted ADME/T properties of Hc1 and Hc4 were found to be satisfactory, exhibiting favorable pharmacokinetic characteristics and no apparent toxicity. Therefore, Hc1 and Hc4 should be novel potent M pro inhibitor lead compounds.

Identification of Salvianolic acid A as a potent inhibitor of PDEs to enhance proliferation of human neural stem cells

Abstract In recent years, hepatitis B virus (HBV) core protein allosteric modulators (CpAMs) have become a hot spot to develop new anti‐HBV drugs. Sulfonamide analogues are a new class of CpAMs targeting HBV core protein (Cp), while their biological activity is still needed to be improved and the binding mechanism is unclear still. In this study, we utilized molecular docking and molecular dynamics simulation to explore the binding mode between the novel compounds and HBV Cp. Combining with binding free energy calculation and decomposition, we inferred that TyrE132, ValE124, ThrE128, ThrD109, and IleD105 were key residues during the binding process, and especially, the conformational change of TryE132 was responsible for the stable binding. Besides, reasonable CoMFA ( q 2 = 0.515, r 2 ncv = 0.994, and r 2 pred = 0.628) and CoMSIA ( q 2 = 0.602, r 2 ncv = 0.988, and r 2 pred = 0.681) models were constructed to explore the 3D structure‐activity relationship of the novel compounds. The results suggested that it was favorable to increase the biological activity of sulfonamide analogues when introducing hydrophobic groups to R 1 and electronegative groups to R 2 . In conclusion, we explored the binding mode and structure‐activity relationship between sulfonamides and HBV Cp and provided a theoretical basis for the optimization of this series of compounds.

Pin1 (protein interacting with never-in-mitosis akinase-1) is a member of the family of peptidylprolyl cis-trans isomerases (PPIases) that specifically recognize and isomerize substrates containing phosphorylated Ser/Thr-Pro sequences. Pin1 is involved in many cellular processes and plays a key role in the cell cycle, transcriptional regulation, cell metabolism, proliferation and differentiation, and its abnormalities lead to degenerative and neoplastic diseases. Pin1 is highly expressed in human cancers and promotes the development of tumors by activating multiple oncogenes and inactivating multiple tumor suppressor genes, making it an attractive target for cancer therapy. In this study, we investigated the binding mechanism and conformational relationship between benzimidazole Pin1 inhibitors and Pin1 proteins by molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, binding free energy calculations and decomposition, and molecular dynamics simulations. Molecular docking and molecular dynamics simulations disclosed the most likely binding pose of benzimidazoles with the Pin1 protein. The results of 3D-QSAR modeling indicated that electrostatic fields, hydrophobic fields and hydrogen bonding play important roles in the binding process of inhibitors to proteins. The binding free energy calculations and energy decomposition indicated that Lys63, Arg69, Cys113, Leu122, Met130, and Ser154 may be key residues in the binding of benzimidazole-based inhibitors to the Pin1 protein. This study provides an important theoretical basis for the design and optimization of benzimidazole compounds.