Human structural proteome-wide characterization of Cyclosporine A targets.

MOTIVATION: Off-target interactions of a popular immunosuppressant Cyclosporine A (CSA) with several proteins besides its molecular target, cyclophilin A, are implicated in the activation of signaling pathways that lead to numerous side effects of this drug. RESULTS: Using structural human proteome and a novel algorithm for inverse ligand binding prediction, ILbind, we determined a comprehensive set of 100+ putative partners of CSA. We empirically show that predictive quality of ILbind is better compared with other available predictors for this compound. We linked the putative target proteins, which include many new partners of CSA, with cellular functions, canonical pathways and toxicities that are typical for patients who take this drug. We used complementary approaches (molecular docking, molecular dynamics, surface plasmon resonance binding analysis and enzymatic assays) to validate and characterize three novel CSA targets: calpain 2, caspase 3 and p38 MAP kinase 14. The three targets are involved in the apoptotic pathways, are interconnected and are implicated in nephrotoxicity.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity.

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model.

Ensemble-based virtual screening reveals dual-inhibitors for the p53-MDM2/MDMX interactions.

The p53 protein, a guardian of the genome, is inactivated by mutations or deletions in approximately half of human tumors. While in the rest of human tumors, p53 is expressed in wild-type form, yet it is inhibited by over-expression of its cellular regulators MDM2 and MDMX proteins. Although the p53-binding sites within the MDMX and MDM2 proteins are closely related, known MDM2 small-molecule inhibitors have been shown experimentally not to bind to its homolog, MDMX. As a result, the activity of these inhibitors including Nutlin3 is compromised in tumor cells over-expressing MDMX, preventing these compounds from fully activating the p53 protein. Here, we applied the relaxed complex scheme (RCS) to allow for the full receptor flexibility in screening for dual-inhibitors that can mutually antagonize the two p53-regulator proteins. First, we filtered the NCI diversity set, DrugBank compounds and a derivative library for MDM2-inhibitors against 28 dominant MDM2-conformations. Then, we screened the MDM2 top hits against the binding site of p53 within the MDMX target. Results described herein identify a set of compounds that have been computationally predicted to ultimately activate the p53 pathway in tumor cells retaining the wild-type protein.