The Marburg virus, a close relative of the Ebola virus, is a menacing Filovirus known for its devastating outbreaks in Germany and recent outbreaks in Guinea and Tanzania. This deadly pathogen triggers severe hemorrhagic fever, posing a grave threat to public health and demanding urgent attention from the global medical community. The amino acid sequence and PDB of the Envelope glycoprotein (GP) were extracted from RCSB for use in predicting epitopes (IEDB server). The construction of the multi-epitope vaccine included an adjuvant and linkers (AAY, EAAAK, GPGPG), which were assessed with the ProtParam tool to characterize their physico-chemical properties. Additionally, modeling was carried out with the Robetta server, and the modeled vaccine was docked with Toll-like receptor 4 (TLR4). Finally, immune and molecular dynamic simulations were implemented using the C-ImmSim and GROMACS packages. The final multi-epitope vaccine consists of 211 amino acids, created with 5 CTL and 4 HTL epitopes that were validated and passed assessments for antigenicity, allergenicity, and toxicity. The modeled multi-epitope vaccine was evaluated and demonstrated high model quality. The best molecular docking candidate was selected and evaluated using PDBsum. Subsequently, by assessing RMSD, RMSF, and Gyration, the molecular dynamic simulation revealed considerable binding with TLR4, and the complex remained stable throughout the simulation. Ultimately, the multi-epitope vaccine can stimulate both humoral and cell-mediated immune responses, validated computationally. The overall implication of this investigation shows the potency of the multi-epitope construct as an efficient protective vaccine against the Marburg virus.
In the quest to combat rising antimicrobial resistance, this study explores the synthesis, characterization, and pharmacological potential of carbamide compounds combined with Butanedioic acid. Leveraging both experimental and computational methodologies, we synthesized Carbamide-Butanedioic Acid (CBA) crystals through a controlled evaporation process. Characterization was conducted using techniques such as FT-IR, UV-visible spectroscopy, powder X-ray diffraction (PXRD), and scanning electron microscopy (SEM). These methods elucidated the crystal structure, molecular interactions, and physicochemical properties of the synthesized compounds. Computational studies, employing Density Functional Theory (DFT) and other quantum chemical calculations, provided insights into the molecular geometry, vibrational spectra, and electronic properties, including the identification of reactive sites and intermolecular interactions. The antibacterial efficacy of the synthesized compounds was assessed using the agar well diffusion method, revealing promising inhibitory effects against bacterial pathogens. This study highlights CBA compounds as promising next-generation antibacterial agents, providing a new approach to tackle antimicrobial resistance.
Virtual screening of biologically active compounds is widely applied for the search of drug leads. The well-known methods of structure-activity relationship (SAR) are based on the chemical structure comparison. In the last years, an approach known as proteochemometrics (PCM) has also gained popularity. PCM extends the capabilities of SAR by incorporating the protein target descriptors into the model. Unlike SAR, PCM can be used to predict new targets with unknown spectra of ligands. As both approaches can be used to predict ligands for the known proteins, several researchers apply PCM to solve this task, without providing compelling reasons to support the superiority of the PCM approach over SAR. To correctly compare the performance of SAR and PCM in the given situation, we have developed a special validation scheme. As a result, we did not find any advantages of PCM over SAR in the prediction of ligands for the protein with an established ligand spectrum. At the same time, the validation procedure commonly used for PCM models considerably inflates the evaluation scores compared to our technique. Widespread use of such validation scheme leads to conclusions that PCM has great advantage over SAR in contrast to our findings. Thus, our study emphasizes that a transparent and correct validation scheme is essential for comparison of different methods.

