E3 ubiquitin ligases, which bind protein targets, leading to their ubiquitination and subsequent degradation, are attractive drug targets due to their exquisite substrate specificity. However, the development of small-molecule inhibitors has proven extraordinarily challenging as modulation of E3 ligase activities requires the targeting of protein–protein interactions. Using rational design, we have generated the first small molecule targeting the von Hippel–Lindau protein (VHL), the substrate recognition subunit of an E3 ligase, and an important target in cancer, chronic anemia, and ischemia. We have also obtained the crystal structure of VHL bound to our most potent inhibitor, confirming that the compound mimics the binding mode of the transcription factor HIF-1α, a substrate of VHL. These results have the potential to guide future development of improved lead compounds as therapeutics for the treatment of chronic anemia and ischemia.

Many limitations of current computer-aided drug design arise from the difficulty of reliably predicting the binding affinity of a small molecule to a biological target. There is thus a strong interest in novel computational methodologies that claim predictions of greater accuracy than current scoring functions, and at a throughput compatible with the rapid pace of drug discovery in the pharmaceutical industry. Notably, computational methodologies firmly rooted in statistical thermodynamics have received particular attention in recent years. Yet free energy calculations can be daunting to learn for a novice user because of numerous technical issues and various approaches advocated by experts in the field. The purpose of this article is to provide an overview of the current capabilities of free energy calculations and to discuss the applicability of this technology to drug discovery.

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