Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2’s active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.

The lumpy skin disease (LSD) virus of the Poxviridae family is a serious threat that mostly affects cattle and causes significant economic loss. LSD has the potential to spread widely and its rapidly across borders. Despite the availability of information, there is still no competitive vaccine available for LSD. Therefore, the current study was conducted to develop an epitope-based LSD vaccine that is efficient, secure, and biocompatible and stimulates both innate and adaptive immune responses using immunoinformatics techniques. Initially, putative virion core proteins were manipulated; B-cell and T-cell epitopes have been predicted and connected with the help of adjuvants and linkers. Numerous bioinformatics methods, including antigenicity testing, transmembrane topology screening, allergenicity assessment, conservancy analysis, and toxicity evaluation, were employed to find superior epitopes. Based on promising vaccine candidates and immunogenic potential, the vaccine design was selected. Strong interactions between TLR4 and TLR9 and the anticipated vaccine design were revealed by molecular docking. Finally, based on the high docking score, computer simulations were performed in order to assess the stability, efficacy, and compactness of the constructed vaccine. The simulation outcomes showed that the polypeptide vaccine design was remarkably stable, with high expression, stability, immunogenic qualities, and considerable solubility. Additionally, computer-based research shows that the constructed vaccine provides adequate population coverage, making it a promising candidate for use in the design of vaccines against other viruses within the Poxviridae family and potentially other virus families as well. These outcomes suggest that the epitope-based vaccine developed in this study will be a significant candidate against LSD to control and prevent LSDV-related disorders if further investigated experimentally.

In the current era, our experience is full of pandemic infectious agents that no longer threaten the major local source but the whole globe. One such infectious agent is HPV, a sexually transmitted disease that can cause various clinical disorders, including benign lesions and cervical cancer. Since available vaccines still need further improvements in order to enhance efficacy, our goal was to design a chimeric vaccine against HPV using an immunoinformatics approach. For designing the vaccine, the sequence of HPV was retrieved, and then phylogenetic analysis was performed. Several CTL epitopes, HTL epitopes, and LBL epitopes were all predicted using bioinformatics tools. After checking the antigenicity, allergenicity, and toxicity scores, the best epitopes were selected for vaccine construction, and then physicochemical and immunological properties were analyzed. Subsequently, vaccine 3D structure prediction, refinement, and validation were performed. Molecular docking and dynamics simulation techniques were used to explore the interactions between the Toll-like receptor 2 and the modeled vaccine construct. To ensure the vaccine protein was expressed at a higher level, the construct was computationally cloned into the pET28a (+) plasmid. The molecular docking and simulation results showed that our designed vaccine is stable, of immunogenic quality, and has considerable solubility. Through in silico immune simulation, it was predicted that the designed polypeptide vaccine construct would trigger both humoral and cellular immune responses. The developed vaccine showed significant affinity for the TLR2 receptor molecule. However, additional laboratory research is required to evaluate its safety and efficacy.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Pseudomonas aeruginosa infections are frequently associated with the development of biofilms, which give the bacteria additional drug resistance and increase their virulence. The goal of this study was to find strong compounds that block the Anthranilate-CoA ligase enzyme made by the pqsA gene. This would stop the P. aeruginosa quorum signaling system. This enzyme plays a crucial role in the pathogenicity of P. aeruginosa by producing autoinducers for cell-to-cell communication that lead to the production of biofilms. Pharmacophore-based virtual screening was carried out utilizing a library of commercially accessible enzyme inhibitors. The most promising hits obtained during virtual screening were put through molecular docking with the help of MOE. The virtual screening yielded 7/160 and 10/249 hits (ZINC and Chembridge). Finally, 2/7 ZINC hits and 2/10 ChemBridge hits were selected as potent lead compounds employing diverse scaffolds due to their high pqsA enzyme binding affinity. The results of the pharmacophore-based virtual screening were subsequently verified using a molecular dynamic simulation-based study (MDS). Using MDS and post-MDS, the stability of the complexes was evaluated. The most promising lead compounds exhibited a high binding affinity towards protein-binding pocket and interacted with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.