Nelfinavir is a potent HIV-protease inhibitor with pleiotropic effects in cancer cells. Experimental studies connect its anti-cancer effects to the suppression of the Akt signaling pathway, but the actual molecular targets remain unknown. Using a structural proteome-wide off-target pipeline, which integrates molecular dynamics simulation and MM/GBSA free energy calculations with ligand binding site comparison and biological network analysis, we identified putative human off-targets of Nelfinavir and analyzed the impact on the associated biological processes. Our results suggest that Nelfinavir is able to inhibit multiple members of the protein kinase-like superfamily, which are involved in the regulation of cellular processes vital for carcinogenesis and metastasis. The computational predictions are supported by kinase activity assays and are consistent with existing experimental and clinical evidence. This finding provides a molecular basis to explain the broad-spectrum anti-cancer effect of Nelfinavir and presents opportunities to optimize the drug as a targeted polypharmacology agent.
The traditional approach to drug discovery of “one drug – one target – one disease” is insufficient, especially for complex diseases, like cancer. This inadequacy is partially addressed by accepting the notion of polypharmacology – one drug is likely to bind to multiple targets with varying affinity. However, to identify multiple targets for a drug is a complex and challenging task. We have developed a structural proteome-wide off-target determination pipeline by integrating computational methods for high-throughput ligand binding site comparison and binding free energy calculations to predict potential off-targets for known drugs. Here this method is applied to identify human off-targets for Nelfinavir, an antiretroviral drug with anti-cancer behavior. We propose inhibition by Nelfinavir of multiple protein kinase targets. We suggest that broad-spectrum low affinity binding by a drug or drugs to multiple targets may lead to a collective effect important in treating complex diseases such as cancer. The challenge is to understand enough about such processes so as to control them.