Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Quest for discovering novel CDK12 inhibitor

CDK12 is essential for cellular processes like RNA processing, transcription, and cell cycle regulation, inhibiting cancer cell growth and facilitating macrophage invasion. CDK12 is a significant oncogenic factor in various cancers, including HER2-positive breast cancer, Anaplastic thyroid carcinoma, Hepatocellular carcinoma, prostate cancer, and Ewing sarcoma. It is also regarded as a potential biomarker, emphasizing its broader significance in oncology. Targeting CDK12 offers a promising strategy to develop therapy. Various monoclonal antibodies have drawn wide attention, but they are expensive compared to small-molecule inhibitors, limiting their accessibility and affordability for patients. Consequently, this research aims to identify effective CDK12 inhibitors using comprehensive high-throughput virtual screening. RASPD protocol has been employed to screen three different databases against the target followed by drug-likeness, molecular docking, ADME, toxicity, Consensus molecular docking, MD Simulation, and in-vitro studies MTT assay. The research conducted yielded one compound ZINC11784547 has demonstrated robust binding affinity, favorable ADME features, less toxicity, remarkable stability, and cytotoxic effect. The identified compound holds promise for promoting cancer cell death through CDK12 inhibition.

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Extracting "high ranking" or "prime protein targets" (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations - ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository ( http://DUD-E.decoys.org ) are matched against 1040 ligands using "forward modeling" machine learning for initial "data mining and modeling" (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called "reverse modeling" (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.