Poly(ADP-ribose) polymerases (PARPs) are a family of enzymes that use NAD+ as a substrate to synthesize polymers of ADP-ribose (PAR) as post-translational modifications of proteins. PARPs have important cellular roles that include preserving genomic integrity, telomere maintenance, transcriptional regulation, and cell fate determination. The diverse biological roles of PARPs have made them attractive therapeutic targets, which have fueled the pursuit of small molecule PARP inhibitors. The design of PARP inhibitors has matured over the past several years resulting in several lead candidates in clinical trials. PARP inhibitors are mainly used in clinical trials to treat cancer, particularly as sensitizing agents in combination with traditional chemotherapy to reduce side effects. An exciting aspect of PARP inhibitors is that they are also used to selectivity kill tumors with deficiencies in DNA repair proteins (e.g. BRCA1/2) through an approach termed “synthetic lethality.” In the midst of the tremendous efforts that have brought PARP inhibitors to the forefront of modern chemotherapy, most clinically used PARP inhibitors bind to conserved regions that permits cross-selectivity with other PARPs containing homologous catalytic domains. Thus, the differences between therapeutic effects and adverse effects stemming from pan-PARP inhibition compared to selective inhibition are not well understood. In this review, we discuss current literature that has found ways to gain selectivity for one PARP over another. We furthermore provide insights into targeting other domains that make up PARPs, and how new classes of drugs that target these domains could provide a high degree of selectivity by affecting specific cellular functions. A clear understanding of the inhibition profiles of PARP inhibitors will not only enhance our understanding of the biology of individual PARPs, but may provide improved therapeutic options for patients.

The 2019 emergence of, SARS-CoV-2 has tragically taken an immense toll on human life and far reaching impacts on society. There is a need to identify effective antivirals with diverse mechanisms of action in order to accelerate preclinical development. This study focused on five of the most established drug target proteins for direct acting small molecule antivirals: Nsp5 Main Protease, Nsp12 RNA-dependent RNA polymerase, Nsp13 Helicase, Nsp16 2’-O methyltransferase and the S2 subunit of the Spike protein. A workflow of solvent mapping and free energy calculations was used to identify and characterize favorable small-molecule binding sites for an aromatic pharmacophore (benzene). After identifying the most favorable sites, calculated ligand efficiencies were compared utilizing computational fragment screening. The most favorable sites overall were located on Nsp12 and Nsp16, whereas the most favorable sites for Nsp13 and S2 Spike had comparatively lower ligand efficiencies relative to Nsp12 and Nsp16. Utilizing fragment screening on numerous possible sites on Nsp13 helicase, we identified a favorable allosteric site on the N-terminal zinc binding domain (ZBD) that may be amenable to virtual or biophysical fragment screening efforts. Recent structural studies of the Nsp12:Nsp13 replication-transcription complex experimentally corroborates ligand binding at this site, which is revealed to be a functional Nsp8:Nsp13 protein-protein interaction site in the complex. Detailed structural analysis of Nsp13 ZBD conformations show the role of induced-fit flexibility in this ligand binding site and identify which conformational states are associated with efficient ligand binding. We hope that this map of over 200 possible small-molecule binding sites for these drug targets may be of use for ongoing discovery, design, and drug repurposing efforts. This information may be used to prioritize screening efforts or aid in the process of deciphering how a screening hit may bind to a specific target protein.

Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target (“distant off-targets”). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target (“distant off-target”).

Docking simulations are commonly used to understand drug binding and require the search of a large space of proteinligand conformations. Cloud and volunteer computing enable computationally expensive docking simulations at a rate never seen before but at the same time require scientists to deal with larger datasets. When analysing these datasets, a common practice is to reduce the resulting number of candidates up to 10 to 100 conformations based on energy values and then leave the scientists with the tedious task of subjectively selecting a possible near-native ligand. Scientists normally perform this task manually by using visual tools. Not only the manual process still depends on inaccurate energy scoring but also can be highly error-prone. The contributions of this paper are twofold: First, we address the problem of extensively searching large spaces of protein-ligand docking conformations, supported by the volunteer computing project Docking@Home (D@H). Second, we address the problem of accurately, and automatically, selecting near-native ligand conformations from the large number of D@H results by using a probabilistic hierarchical clustering based on ligand geometry. Our method holds up even when we test for a search that is not biased by starting from near-native ligand conformations and clearly outperforms energy-based scoring methods.

Umifenovir (Arbidol) has been reported to exhibit some degree of efficacy in multiple clinical trials for the treatment of COVID-19 as a monotherapy. It has also demonstrated synergistic inhibition of SARS-CoV-2 with other direct-acting antivirals such as Remdesivir. A computational approach was used to identify the most favorable binding site to the SARS-CoV-2 Spike S2 segment and to perform virtual screening. Compounds selected from modeling were evaluated in a live SARS-CoV-2 infection assay. An Arbidol (ARB) derivative with substitutions at both the C-4 and C-6 positions was found to exhibit a modest improvement in activity and solubility properties in comparison to ARB. However, all of the derivatives were found to only be partial inhibitors, rather than full inhibitors in a virus-induced cytopathic effect-based assay. The binding mode is also corroborated by parallel modeling of a series of oleanolic acid trisaccharide saponin fusion inhibitors shown to bind to the S2 segment. Recently determined experimental structures of the Spike protein allowed atomic resolution modeling of fusion inhibitor binding as a function of pH, and the implications for the molecular mechanism of direct-acting fusion inhibitors targeting the S2 segment are discussed.