Identifying new piperazine-based PARP1 inhibitors using text mining and integrated molecular modeling approaches
One of the important molecular targets for antitumor drug discovery is the polyadenosine diphosphate-ribose polymerase-1 (PARP1) enzyme. It is linked with various biological functions including DNA repair and apoptosis. It is primarily a nuclear enzyme linked to chromatin, which is activated by DNA damage. Improved expression of PARP1 in melanomas, breast cancer, lung cancer and other neoplastic diseases is often observed. A tremendous PARP research concerning cancer and ischemia is progressing very rapidly. There are currently four PARP1 inhibitors approved by the FDA on the market, namely Olaparib, Rucaparib, Niraparib and Talazoparib. All of these molecules are non-selective inhibitors of PARP1. Currently there is an urgent need for novel and selective PARP1 inhibitors. In this work, asmall molecule database (Specs SC) were used to identify the new selective lead inhibitors of PARP1. Piperazine scaffold is an important fragment that is used in many currently used FDA approved drugs in different diseases including PARP1 inhibitor Olaparib. Thus, based on text mining studies, 4674 compounds thatinclude piperazine fragments were identified and virtually screened at the binding pocket of target protein PARP1. Compounds that have high docking scores were used in molecular dynamics (MD) simulations. Free energy calculations were also performed to compare the predicted binding energies with known PARP1 inhibitors. The critical amino acid interactions of these newly identified hits in the binding pocket were also investigated in detail for better understanding of the structural features required for next generation PARP1 inhibitors. Thus, here together with combination of text-mining and integrated molecular modeling approaches, we identified novel piperazine-based hits against PARP1 enzyme.
Communicated by Ramaswamy H. Sarma