This research work included bioinformatics modeling of the dipotassium-trioxohydroxytetrafluorotriborate-halogenated boroxine molecule, as well as simulation and prediction of structural interactions between the halogenated boroxine molecule, human carbonic anhydrase, and human catalase structures. Using computational methods, we tried to confirm the inhibitory effect of halogenated boroxine on the active sites of these previously mentioned enzymes. The three-dimensional crystal structures of human catalase (PDB ID: 1DGB) and human carbonic anhydrase (PDB ID: 6FE2) were retrieved from RCSB Protein Data Bank and the protein preparation was performed using AutoDock Tools. ACD/ChemSketch and ChemDoodle were used for creating the three-dimensional structure of halogenated boroxine. Molecular docking was performed using AutoDock Vina, while the results were visualized using PyMOL. Results obtained in this research are showing evidence that there are interactions between the halogenated boroxine molecule and both previously mentioned proteins (human carbonic anhydrase and human catalase) in their active sites, which led us to the conclusion that the inhibitory function of halogenated boroxine has been confirmed. These findings could be an important step in determining the exact mechanisms of inhibitory activity and will hopefully serve in further research purposes of complex pharmacogenomics studies.

Essential role in replication and transcription of coronavirus makes the main protease of SARS-CoV-2 a great traget for drug design. The aim of this study was to predict structural interactions of compounds isolated from the Bosnian-Herzegovinian endemic plant Knautia sarajevensis (G. Beck) Szabó against the 3CLpro of SARS-CoV-2 virus. The three-dimensional crystal structure of SARS-CoV-2 main protease was retrieved from the RCSB Protein Data Bank and the three-dimensional structures of isolated compounds were obtained from the PubChem database. Active site was predicted using PrankWeb, while the preparation of protease and compounds was performed using AutoDock Tools and OpenBabel. Molecular docking was carried out using AutoDock Vina. Structural interactions are visualised and analyzed using PyMOL, LigPlus and UCSF Chimera. Apigenin, kaempferol, myricetin and quercetin showed the highest binding affinity for SARS-CoV-2 main protease and formed significant hydrogen bonds with the given protein. Results obtained in this study are in accordance with previous studies and showed that these compounds could potentially have antiviral effects against SARS-CoV-2. These findings indicate that K. sarajevensis could be potentially utilized as an adjuvant in the treatment of coronavirus disease 2019, but further pharmacological studies are required in order to prove the potential medicinal use of the plant.

Introduction: This study investigates the molecular docking of 306 phytochemicals from Iris, Daphne, and Chrysosplenium species against three key proteins of the H5N1 influenza virus: neuraminidase, polymerase, and hemagglutinin. Phytochemicals are recognized for their antiviral po-tential, but interactions between compounds from these genera and H5N1 proteins remain underex-plored. Given the ongoing threat of H5N1, identifying novel inhibitors is essential. The main intent is to evaluate the binding affinities of selected phytochemicals through molecular docking and assess the drug-likeness of top candidates using pharmacokinetic and physicochemical filters. Methods: Molecular docking was performed for 306 phytochemicals against the three H5N1 pro-teins. Fourteen promising compounds were further screened for physicochemical properties, compli-ance with Lipinski's Rule of Five, Veber';s Rule, and PAINS alerts. Results: All compounds exhibited no PAINS alerts, with several conforming to Lipinski's Rule of Five and Veber';s Rule. Edgeworoside A emerged as the top-performing compound, showing strong binding affinity across all three targets and favorable interaction profiles. Triumbellin and daphnogi-rin A exhibited significant binding affinity for hemagglutinin and neuraminidase, as well as for pol-ymerase, respectively. Compounds such as 3-isobutenylquercetin, irisoid E, junipegenin A, daphne-toxin, and excoecariatoxin exhibited high binding potential without violating drug-likeness criteria. Conclusion: Several phytochemicals, particularly edgeworoside A, demonstrate promising multi-target potential against H5N1 influenza proteins. These findings highlight the therapeutic relevance of compounds from underexplored plant genera and support their further development through in vitro, in vivo, and preclinical studies.

Rectal carcinoma (RC) represents approximately 30% of all colorectal carcinomas (CRC) and is considered a distinct clinical entity. Vascular invasion (VI) is recognized as an independent predictor of poor outcomes in RC. In this study, we applied bioinformatics methods to identify gene pathways most likely associated with VI in rectal carcinoma. As ADAMTS8 showed statistically significant negative relations with the VI in RC patients, we further analyzed its top co-dependent genes—DNAL4, EVI2B, PPP1R35, PTGR3, RPL21, SOX4, and ZNF3—for the experimentally proven molecular modulators. We identified a total of 23 compounds from the Comparative Toxicogenomics Database based on previously reported data for all eight target genes. The search was expanded to include additional chemical agents by structure similarity using the PubChem database, which revealed 9661 additional compounds. These were subsequently used for molecular interaction analysis against target proteins co-expressed with, or associated with, ADAMTS8 in RC with VI. Ultimately, we identified four high-affinity compounds—cyanoginosin LR, doxorubicin, benzo[a]pyrene, and dibenzo(a,e)pyrene—that interacted with all target proteins. These compounds show potential for further assessment of their role in modulating processes related to vascular invasion, which is a strong negative predictor of RC outcomes.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, driven by profound molecular heterogeneity and resistance to current therapy. To support systematic target identification, we established a proteomics-anchored knowledge discovery framework integrating cross-model proteomics harmonization, network topology, high-confidence structural modeling, and large-scale in silico docking. From 1,975 proteins consistently detected across murine and human PDAC models, 32 immunohistochemically confirmed candidates were prioritized for structure-based screening against 7,509 clinically characterized compounds. Blind docking, refined pose sampling, ligand-efficiency scoring, and ADME filtering identified EIF2A, STAM, ANXA2, and AHNAK2 as robustly druggable targets. These proteins exhibited high-affinity interactions with zavegepant (a clinically approved CGRP receptor antagonist), omilancor, bemcentinib, conivaptan, and APTO-253. Docking validation (RMSD 1.98 to 2.56 Å) confirmed methodological reliability, and network analyses placed the 4 proteins within modules linked to endosomal/membrane trafficking and invasive phenotypes. Survival analyses in 176 PDAC patients further supported their clinical relevance. Thus, we suggest a systems-level platform for nominating ligandable PDAC targets and clinically actionable compounds. The framework highlights opportunities for rational drug repurposing and motivates future mechanistic studies at the intersection of proteomics and structure-based screening for targets to PDAC.