Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis (k_VR), capture structure-activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF-aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF-aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

Arrival of the novel SARS-CoV-2 has launched a worldwide effort to identify both pre-approved and novel therapeutics targeting the viral proteome, highlighting the urgent need for efficient drug discovery strategies. Even with effective vaccines, infection is possible, and at-risk populations would benefit from effective drug compounds that reduce the lethality and lasting damage of COVID-19 infection. The CoV-2 MacroD-like macrodomain (Mac1) is implicated in viral pathogenicity by disrupting host innate immunity through its mono (ADP-ribosyl) hydrolase activity, making it a prime target for antiviral therapy. We therefore solved the structure of CoV-2 Mac1 from non-structural protein 3 (Nsp3) and applied structural and sequence-based genetic tracing, including newly determined A. pompejana MacroD2 and GDAP2 amino acid sequences, to compare and contrast CoV-2 Mac1 with the functionally related human DNA-damage signaling factor poly (ADP-ribose) glycohydrolase (PARG). Previously, identified targetable features of the PARG active site allowed us to develop a pharmacologically useful PARG inhibitor (PARGi). Here, we developed a focused chemical library and determined 6 novel PARGi X-ray crystal structures for comparative analysis. We applied this knowledge to discovery of CoV-2 Mac1 inhibitors by combining computation and structural analysis to identify PARGi fragments with potential to bind the distal-ribose and adenosyl pockets of the CoV-2 Mac1 active site. Scaffold development of these PARGi fragments has yielded two novel compounds, PARG-345 and PARG-329, that crystallize within the Mac1 active site, providing critical structure-activity data and a pathway for inhibitor optimization. The reported structural findings demonstrate ways to harness our PARGi synthesis and characterization pipeline to develop CoV-2 Mac1 inhibitors targeting the ADP-ribose active site. Together, these structural and computational analyses reveal a path for accelerating development of antiviral therapeutics from pre-existing drug optimization pipelines.

We present a Chemistry and Structure Screen Integrated Efficiently (CASSIE) approach (named for Greek prophet Cassandra) to design inhibitors for cancer biology and pathogenesis. CASSIE provides an effective path to target master keys to control the repair-replication interface for cancer cells and SARS CoV-2 pathogenesis as exemplified here by specific targeting of Poly(ADP-ribose) glycohydrolase (PARG) and ADP-ribose glycohydrolase ARH3 macrodomains plus SARS CoV-2 nonstructural protein 3 (Nsp3) Macrodomain 1 (Mac1) and Nsp15 nuclease. As opposed to the classical massive effort employing libraries with large numbers of compounds against single proteins, we make inhibitor design for multiple targets efficient. Our compact, chemically diverse, 5000 compound Goldilocks (GL) library has an intermediate number of compounds sized between fragments and drugs with predicted favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles. Amalgamating our core GL library with an approved drug (AD) library, we employ a combined GLAD library virtual screen, enabling an effective and efficient design cycle of ranked computer docking, top hit biophysical and cell validations, and defined bound structures using human proteins or their avatars. As new drug design is increasingly pathway directed as well as molecular and mechanism based, our CASSIE approach facilitates testing multiple related targets by efficiently turning a set of interacting drug discovery problems into a tractable medicinal chemistry engineering problem of optimizing affinity and ADME properties based upon early co-crystal structures. Optimization efforts are made efficient by a computationally-focused iterative chemistry and structure screen. Thus, we herein describe and apply CASSIE to define prototypic, specific inhibitors for PARG vs distinct inhibitors for the related macrodomains of ARH3 and SARS CoV-2 Nsp3 plus the SARS CoV-2 Nsp15 RNA nuclease.