Identification of novel NLRP3 inhibitors as therapeutic options for epilepsy by machine learning-based virtual screening, molecular docking and biomolecular simulation studies.

The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic target for the treatment of epilepsy and has been reported to regulate inflammation in several neurological diseases. In this study, a machine learning-based virtual screening strategy has investigated candidate active compounds that inhibit the NLRP3 inflammasome. As machine learning-based virtual screening has the potential to accurately predict protein-ligand binding and reduce false positives outcomes compared to traditional virtual screening. Briefly, classification models were created using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) machine learning methods. To determine the most crucial features of a molecule's activity, feature selection was carried out. By utilizing 10-fold cross-validation, the created models were analyzed. Among the generated models, the RF model obtained the best results as compared to others. Therefore, the RF model was used as a screening tool against the large chemical databases. Molecular operating environment (MOE) and PyRx software's were applied for molecular docking. Also, using the Amber Tools program, molecular dynamics (MD) simulation of potent inhibitors was carried out. The results showed that the KNN, SVM, and RF accuracy was 0.911 %, 0.906 %, and 0.946 %, respectively. Moreover, the model has shown sensitivity of 0.82 %, 0.78 %, and 0.86 % and specificity of 0.95 %, 0.96 %, and 0.98 % respectively. By applying the model to the ZINC and South African databases, we identified 98 and 39 compounds, respectively, potentially possessing anti-NLRP3 activity. Also, a molecular docking analysis produced ten ZINC and seven South African compounds that has comparable binding affinities to the reference drug. Moreover, MD analysis of the two complexes revealed that the two compounds (ZINC000009601348 and SANC00225) form stable complexes with varying amounts of binding energy. The in-silico studies indicate that both compounds most likely display their inhibitory effect by inhibiting the NLRP3 protein.

In silico design of peptide inhibitors for Dengue virus to treat Dengue virus-associated infections.

Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness. However, DENV can occasionally cause more severe cases and even death. There is no specific treatment for dengue virus infections. Therapeutic peptides have several important advantages over proteins or antibodies: they are small in size, easy to synthesize, and have the ability to penetrate the cell membranes. They also have high activity, specificity, affinity, and less toxicity. Based on the known peptide inhibitor, the current study designs peptide inhibitors for dengue virus envelope protein using an alanine and residue scanning technique. By replacing I21 with Q21, L14 with H14, and V28 with K28, the binding affinity of the peptide inhibitors was increased. The newly designed peptide inhibitors with single residue mutation improved the binding affinity of the peptide inhibitors. The inhibitory capability of the new promising peptide inhibitors was further confirmed by the utilization of MD simulation and free binding energy calculations. The molecular dynamics simulation demonstrated that the newly engineered peptide inhibitors exhibited greater stability compared to the wild-type peptide inhibitors. According to the binding free energies MM(GB)SA of these developed peptides, the first peptide inhibitor was the most effective against the dengue virus envelope protein. All peptide derivatives had higher binding affinities for the envelope protein and have the potential to treat dengue virus-associated infections. In this study, new peptide inhibitors were developed for the dengue virus envelope protein based on the already reported peptide inhibitor.

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches.

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant.