Structural and dynamics insights into the GBA variants associated with Parkinson's disease

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-D-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

In vivo analgesic, anti-inflammatory and molecular docking studies of S-naproxen derivatives

In the current studies two naproxen derivatives (NPD) were evaluated for analgesic and anti-inflammatory properties. The acetic acid and hot plate animal models were used to screen the compounds for analgesic potential. While the anti-inflammatory potential was evaluated through animal paw edema, induced by several inflammatory mediators (carrageenan, bradykinin, and prostaglandin E2), the xylene-induced ear edema was also used as an inflammatory model. Both NPDs showed significant (p < 0.001) antinociceptive effects in the acetic acid-induced writhing paradigm. In the case of the hot plate, the NPD 1 at the tested dose of 5 mg/kg enhanced the latency time after 60 min of injection, which remained significant (p < 0.001) up to the end of the experiment duration. The maximum percent inhibition of NPD 1 was 87.53. The naloxone injection significantly lowered the latency time of NPD 1 as compared to NPD 2. Regarding the anti-inflammatory effect, both of the tested NPDs demonstrated a significant reduction in paw edema against various inflammatory mediators, as mentioned above; however, the anti-inflammatory effect of NPD 1 was better. The maximal percent inhibition by NPD 1 and 2 was 43.24 (after 60 min) and 45.93 (after 90 min). A considerable effect also resulted from xylene-induced ere edema. Further, a molecular docking study was carried out to investigate the binding modes of the NPD. The docking analysis revealed that the NPD significantly interacted with the COX2 enzyme. Furthermore, molecular dynamics simulation was carried out for the docked complexes. The MD simulation analysis revealed the high stability of the two naproxen derivatives.

Structure-based virtual screening and molecular docking approaches to identify potential inhibitors against KIF2C to combat glioma

Glioma, a kind of malignant brain tumor, is extremely lethal. Kinesin family member 2C (KIF2C) was found to have an aberrant expression in several cancer types, including lung cancer and glioma. KIF2C may therefore be a useful therapeutic target for the treatment of glioma. In the current study, new drug candidates that may function as KIF2C enzyme inhibitors were discovered. MTi OpenScreen was used to carry out the structure-based virtual screening of an inbuilt drug library containing 150,000 compounds. These compounds belong to different classes, such as natural product-based compounds (NP-lib), purchasable approved drugs (Drugs-lib), and food constituents compound collection (FOOD-lib). Based on their binding affinities, a total of 84 compounds were further pushed to calculate ADMET properties. The compounds (16) meeting the ADMET cutoff ranges were then further docked to the receptor to find their plausible binding modes using the Glide tool's standard precision (SP) technique. The docking results were examined using the Glide gscore, and the best binding compounds (Rimacalib and Sarizotan) were chosen to test their stability with KIF2C protein through molecular dynamics (MD) simulation. Similarly, Principal Component Analysis and cross-correlation matrix were also examined. The MM/GBSA binding free energies showed a considerable energy contribution in the binding of hits with the KIF2C. Collectively, these findings strongly suggest the potential of the lead compounds to inhibit the biological function of KIF2C, emphasizing the need for further investigation in this area.Communicated by Ramaswamy H. Sarma.