Identification of CYP1B1-specific candidate inhibitors using combination of in silico screening, integrated knowledge-based filtering, and molecular dynamics simulations.

CYP1B1 is a promising drug target for developing novel drugs against hormonal cancers and hypertension. The development of CYP1B1-specific inhibitors is hindered mainly due to non-specific action of known CYP inhibitors. The active site of CYP1B1 is similar to other cytochromes with different substrate preferences rendering a scope to develop specific inhibitors. We have developed a novel in silico approach for design of selective CYP1B1 inhibitors. The approach consists of deriving details of CYP1B1-specific molecular interactions from prior studies, which is used to perform screening of CYP1B1 with NCI compounds. The conventional compound screening is also complemented with the concept of cutoff distance between heme (Fe) and compounds. The binding free energies and HB percentage occupancy calculations of 94 compounds of cluster 1 have verified the docking results using MD. The docking interactions in the active-site cavity of 7 clusters are also taken into account for optimal binding. Hence, we used knowledgebase filtering and MD simulations to enable discovery of selective CYP1B1 inhibitors. The final filtered lead candidates consist of compounds sandwiched between phenylalanine π-π stacking and less than 6 Å from heme (Fe) for enzymatic action. The findings in the study can help development of novel CYP1B1 selective inhibitors.