Medicinal Chemistry Projects Requiring Imaginative Structure-Based Drug Design Methods

Abstract

Computational methods for docking small molecules to proteins are prominent in drug discovery. There are hundreds, if not thousands, of documented examples-and several pertinent cases within our research program. Fifteen years ago, our first docking-guided drug design project yielded nanomolar metalloproteinase inhibitors and illustrated the potential of structure-based drug design. Subsequent applications of docking programs to the design of integrin antagonists, BACE-1 inhibitors, and aminoglycosides binding to bacterial RNA demonstrated that available docking programs needed significant improvement. At that time, docking programs primarily considered flexible ligands and rigid proteins. We demonstrated that accounting for protein flexibility, employing displaceable water molecules, and using ligand-based pharmacophores improved the docking accuracy of existing methods-enabling the design of bioactive molecules. The success prompted the development of our own program, Fitted, implementing all of these aspects. The primary motivation has always been to respond to the needs of drug design studies; the majority of the concepts behind the evolution of Fitted are rooted in medicinal chemistry projects and collaborations. Several examples follow: (1) Searching for HDAC inhibitors led us to develop methods considering drug-zinc coordination and its effect on the pKa of surrounding residues. (2) Targeting covalent prolyl oligopeptidase (POP) inhibitors prompted an update to Fitted to identify reactive groups and form bonds with a given residue (e.g., a catalytic residue) when the geometry allows it. Fitted-the first fully automated covalent docking program-was successfully applied to the discovery of four new classes of covalent POP inhibitors. As a result, efficient stereoselective syntheses of a few screening hits were prioritized rather than synthesizing large chemical libraries-yielding nanomolar inhibitors. (3) In order to study the metabolism of POP inhibitors by cytochrome P450 enzymes (CYPs)-for toxicology studies-the program Impacts was derived from Fitted and helped us to reveal a complex metabolism with unforeseen stereocenter isomerizations. These efforts, combined with those of other docking software developers, have strengthened our understanding of the complex drug-protein binding process while providing the medicinal chemistry community with useful tools that have led to drug discoveries. In this Account, we describe our contributions over the past 15 years-within their historical context-to the design of drug candidates, including BACE-1 inhibitors, POP covalent inhibitors, G-quadruplex binders, and aminoglycosides binding to nucleic acids. We also remark the necessary developments of docking programs, specifically Fitted, that enabled structure-based design to flourish and yielded multiple fruitful, rational medicinal chemistry campaigns.

Modeling covalent-modifier drugs

“…Algorithms for predicting covalent-binding poses and the programs they are implemented in.Support for covalent docking has been implemented into the FITTED modeling suite[51,52,45]. Compounds containing appropriate reactive warheads are identified from the ligand database.…”

Docking of Covalent Ligands: Challenges and Approaches

“…An interesting variant of the direct‐linking approach has been incorporated in the docking program FITTED , and initially reported in 2009 as the first fully automated covalent docking program . The approach was described more in detail in 2012 and applied again more recently, in all three cases for the development of covalent prolyl oligopeptidase inhibitors.…”

Acoplamiento Molecular: Avances Recientes y Retos

“…Because ligand-zinc interaction may be quasi-covalent its modeling requires different considerations i.e., its acidic behavior allows for "special" hydrogen bonding (Moitessier et al, 2016). This lead to force field reparametrization as implemented by Autodock (Santos-Martins, Forli, Ramos & Olson, 2014), which when compared with other programs yielded a more accurate solution to pose and scoring (Bai et al, 2015;Chen et al, 2007).…”