The Wild-type p53-induced phosphatase (Wip1, or PPM1D) is a serine/threonine phosphatase that plays a central role in regulating cellular stress response pathways, including p53 signaling and DNA damage repair. Its overexpression and gain-of-function mutations have been associated with various cancers, making it an emerging therapeutic target. However, efforts to develop selective inhibitors have been limited by a lack of structural information and the inherent challenges of targeting phosphatases, particularly due to the unique and flexible regions characteristic of PPM family members. Here, we report the first crystal structure of the PPM1D catalytic domain to 1.8 Å resolution. The structure provides insights into the active site with two bound Mg2+ ions. The flap subdomain and B-loop, crucial for substrate recognition and catalysis, were also resolved. Unexpectedly, we observed a nitrogen-oxygen-sulfur (NOS) covalent bridge within the crystal structure, which might contribute to structural stability, although its physiological relevance remains unclear. Molecular dynamics simulations and kinetic studies offered further mechanistic insights into the regulation of PPM1D catalytic activity. Leveraging these structural insights, we conducted a structure-based virtual screening of over 21 million compounds and identified the 9 most promising compounds with the highest potential for PPM1D inhibition. We are validating these lead compounds and developing analogs to enhance effectiveness. In summary, this work advances the understanding of PPM1D function and facilitates the further development of specific inhibitors of PPM1D phosphatase.

Cis-aconitate decarboxylase (ACOD1) is a key enzyme converting cis-aconitate to itaconate, which has therapeutic potential for inflammatory diseases. Existing methods to measure ACOD1 activity and itaconate are often expensive and complex. We developed a novel, high-throughput spectrophotometric assay using the Fürth-Herrmann reaction. Our method quantifies ACOD1-catalyzed itaconate production by leveraging distinct absorbance ratios of cis-aconitate and itaconate at 386 nm and 440 nm. We optimized parameters, characterized human ACOD1 kinetics, and determined an IC50 for citraconate consistent with previous reports. This simple, fast, and reliable assay, requiring only a UV-Vis spectrophotometer, will accelerate screening for ACOD1 modulators, speeding up therapeutic development.

To accelerate virtual ligand screening (VLS) and identify potent drug leads from massive chemical libraries, we developed two GPU-accelerated methods: Rapid Docking GPU Engine (RIDGE) for receptor-based screening and Rapid Isostere Discovery Engine (RIDE) for ligand-based 3D similarity screening. RIDGE performance surpassed or was as good as previously described methods when tested on 102 proteins from the Directory of Useful Decoys, Enhanced (DUD-E). We used RIDGE and RIDE to screen ultralarge virtual libraries against challenging cancer targets, PD-L1 and K-Ras G12D. This led to the discovery of novel inhibitors with single-digit to submicromolar affinities (five for PD-L1, three for K-Ras G12D). Docking scores from our methods were better predictors of binding than conventional VLS. These novel GPU-accelerated methods expand screenable chemical space and successfully identify active hits, even for challenging targets. Further optimization and libraries with higher-molecular-weight cutoffs could further improve targeting of nondruggable proteins.

While virtual libraries of synthetically accessible compounds have exploded in size to many billions, our capacity to extract valuable drug leads from these vast databases remains limited by computational resources. To overcome this, we developed SLICE SMARTS and Logic In ChEmistry), a powerful new tool designed for the agile exploration of massive chemical spaces. SLICE enables the fast, “à la carte” generation of virtual compound libraries through chemist-defined reaction chemistries and readily available building blocks. Its user-friendly, no-code graphical interface, the SLICE Designer, allows chemists to easily define SMARTS patterns, configure atom and bond properties, and establish chemical constraints and logic. The resulting XML files are then fed into the SLICE Engine, which generates diverse virtual libraries from specified building blocks at speeds of 0.6–2.5 million compounds per hour. SLICE provides the agility and performance needed to support efficient lead generation within discovery workflows.

Fusion-positive rhabdomyosarcoma (FP-RMS) is an aggressive pediatric sarcoma driven primarily by the PAX3-FOXO1 fusion oncogene, for which therapies targeting PAX3-FOXO1 are lacking. Here, we screen 62,643 compounds using an engineered cell line that monitors PAX3-FOXO1 transcriptional activity identifying a hitherto uncharacterized compound, P3FI-63. RNA-seq, ATAC-seq, and docking analyses implicate histone lysine demethylases (KDMs) as its targets. Enzymatic assays confirm the inhibition of multiple KDMs with the highest selectivity for KDM3B. Structural similarity search of P3FI-63 identifies P3FI-90 with improved solubility and potency. Biophysical binding of P3FI-90 to KDM3B is demonstrated using NMR and SPR. P3FI-90 suppresses the growth of FP-RMS in vitro and in vivo through downregulating PAX3-FOXO1 activity, and combined knockdown of KDM3B and KDM1A phenocopies P3FI-90 effects. Thus, we report KDM inhibitors P3FI-63 and P3FI-90 with the highest specificity for KDM3B. Their potent suppression of PAX3-FOXO1 activity indicates a possible therapeutic approach for FP-RMS and other transcriptionally addicted cancers.

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki–Miyaura, Hiyama and Liebeskind–Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

The inhibition of soluble epoxide hydrolase (sEH) can reduce the level of dihydroxyeicosatrienoic acids (DHETs) effectively maintaining endogenous epoxyeicosatrienoic acids (EETs) levels, resulting in the amelioration of inflammation and pain. Consequently, the development of sEH inhibitors has been a prominent research area for over two decades. In the present study, we synthesized and evaluated sulfonyl urea derivatives for their potential to inhibit sEH. These compounds underwent extensive in vitro investigation, revealing their potency against human and mouse sEH, with 4f showing the most promising sEH inhibitory potential. When subjected to lipopolysaccharide (LPS)-induced acute lung injury (ALI) in studies in mice, compound 4f manifested promising anti-inflammatory efficacy. We investigated the analgesic efficacy of sEH inhibitor 4f in a murine pain model of tail-flick reflex. These results validate the role of sEH inhibition in inflammatory diseases and pave the way for the rational design and optimization of sEH inhibitors based on a sulfonyl urea template.

STAT3 N-terminal domain is a promising molecular target for cancer treatment and modulation of immune responses. However, STAT3 is localized in the cytoplasm, mitochondria, and nuclei, and thus, is inaccessible to therapeutic antibodies. Its N-terminal domain lacks deep pockets on the surface and represents a typical "non-druggable" protein. In order to successfully identify potent and selective inhibitors of the domain, we have used virtual screening of billion structure-sized virtual libraries of make-on-demand screening samples. The results suggest that the expansion of accessible chemical space by cutting-edge ultra-large virtual compound databases can lead to successful development of small molecule drugs for hard-to-target intracellular proteins.

Four Ras guanine nucleotide-releasing proteins (RasGRP1 through 4) belong to the family of guanine nucleotide exchange factors (GEFs). RasGRPs catalyze the release of GDP from small GTPases Ras and Rap and facilitate their transition from an inactive GDP-bound to an active GTP-bound state. Thus, they regulate critical cellular responses via many downstream GTPase effectors. Similar to other RasGRPs, the catalytic module of RasGRP1 is composed of the Ras exchange motif (REM) and Cdc25 domain, and the EF hands and C1 domain contribute to its cellular localization and regulation. RasGRP1 can be activated by a diacylglycerol (DAG)-mediated membrane recruitment and protein kinase C (PKC)-mediated phosphorylation. RasGRP1 acts downstream of the T cell receptor (TCR), B cell receptors (BCR), and pre-TCR, and plays an important role in the thymocyte maturation and function of peripheral T cells, B cells, NK cells, mast cells, and neutrophils. The dysregulation of RasGRP1 is known to contribute to numerous disorders that range from autoimmune and inflammatory diseases and schizophrenia to neoplasia. Given its position at the crossroad of cell development, inflammation, and cancer, RASGRP1 has garnered interest from numerous disciplines. In this review, we outline the structure, function, and regulation of RasGRP1 and focus on the existing knowledge of the role of RasGRP1 in leukemia and other cancers.

Supplemental Figure 1: (A) mRNA level of Kras in wild-type Kras tumor cells (Colo320) overexpressing mutant KrasG12V plasmid (Kras Pd) is significantly higher than cells with a mock plasmid. (B) Representative dot plots showing that the percentage of IL-10+ and TGF-beta1+, CTLA-4+ and CD122+ T-lymphocytes co-cultured with wild-type Kras tumor cells (Colo320) overexpressing mutant KrasG12V plasmid (Kras Pd) is significantly higher than WT-cells.

Proteasome substrate receptor hRpn13 is a promising anti-cancer target. By integrated in silico and biophysical screening, we identified a chemical scaffold that binds hRpn13 with non-covalent interactions that mimic the proteasome and a weak electrophile for Michael addition. hRpn13 Pru domain binds proteasomes and ubiquitin whereas its DEUBAD domain binds deubiquitinating enzyme UCHL5. NMR revealed lead compound XL5 to interdigitate into a hydrophobic pocket created by lateral movement of a Pru β-hairpin with an exposed end for Proteolysis Targeting Chimeras (PROTACs). Implementing XL5 -PROTACs as chemical probes identified a DEUBAD-lacking hRpn13 species (hRpn13 Pru ) present naturally with cell type-dependent abundance. XL5 -PROTACs preferentially target hRpn13 Pru , causing its ubiquitination. Gene-editing and rescue experiments established hRpn13 requirement for XL5 -PROTAC-triggered apoptosis. These data establish hRpn13 as an anti-cancer target for multiple myeloma and introduce an hRpn13-targeting scaffold that can be optimized for preclinical trials against hRpn13 Pru -producing cancer types.

Regulated proteolysis by the proteasome involves ∼800 enzymes for substrate modification with ubiquitin, of which ∼600 are E3 ligases. We report here that E6AP/UBE3A is distinguished from other ubiquitin E3 ligases by having a 12 nM binding site at the proteasome contributed by substrate receptor hRpn10/PSMD4/S5a. Intrinsically disordered by itself, and previously uncharacterized, this domain in hRpn10 locks into a novel well-defined helical structure to form an intermolecular 4-helix bundle with the E6AP AZUL domain, which is unique to this E3. We thus name the h R pn10 AZUL -binding domain RAZUL. We further find in human cells that loss of RAZUL by CRISPR-based gene editing leads to loss of E6AP at the proteasome, where associated ubiquitin is correspondingly reduced, suggesting that E6AP ubiquitinates substrates at or for the proteasome. Altogether, our findings indicate E6AP to be a privileged E3 for the proteasome, with a dedicated, high affinity binding site contributed by hRpn10.

To improve the discovery of more effective and less toxic pharmaceutical agents, large virtual repositories of synthesizable molecules have been generated to increase the explored chemical-pharmacological space diversity. Such libraries include billions of structural formulae of drug-like molecules associated with data on synthetic schemes, required building blocks, estimated physical-chemical parameters, etc. Clearly, such repositories are “Big Data”. Thus, to identify the most promising compounds with the required pharmacological properties (hits) among billions of available opportunities, special computational methods are necessary. We have proposed using a combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. Our approach has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI), a 1.75 billion compound database. Potential inhibitors of HIV-1 protease and reverse transcriptase and agonists of toll-like receptors and STING, affecting innate immunity, were computationally identified. The activity of the three synthesized compounds has been confirmed in a cell-based assay. These compounds belong to the chemical classes, in which the agonistic effect on TLR 7/8 had not been previously shown. Synthesis and biological testing of several dozens of compounds with predicted antiretroviral activity are currently taking place at the NCI/NIH. We also carried out virtual screening among one billion substances to find compounds potentially possessing anti-SARS-CoV-2 activity. The selected hits' information has been accepted by the European Initiative “JEDI Grand Challenge against COVID-19” for synthesis and further biological evaluation. The possibilities and limitations of the approach are discussed.

Synthetic analogs of the second transmembrane domain (TM) containing a portion of the extracellular loop 1 of G-protein-coupled receptors (GPCR) can serve as biased antagonists of the corresponding receptor. Analogs with negative charges added to the extracellular end self-assemble into round structures. Addition of polyethylene glycol chains of defined length to the C-terminus of the peptides prevents super aggregation and results in highly uniform particles that can fuse with cell membranes spontaneously. Added PEG chains slow down cell fusion, while attachment of receptor ligands to the surface of particles results in receptor-mediated membrane fusion and cell-selective delivery. Critical assembly concentration of TM peptide particles is in the nanomolar range and thus requires nontraditional methods of determination. In this chapter, we outline sequence selection and design of self-assembling GPCR antagonists, methods of the preparation of the nanoparticles, and biophysical methods of particle characterization. The protocols allow for straightforward rational design, generation, and characterization of self-assembling GPCR antagonists for a variety of applications.

(1) Background: The hedgehog (HH) signaling pathway is a key regulator of embryonic patterning, tissue regeneration, stem cell renewal, and cancer growth. The smoothened (SMO) protein regulates the HH signaling pathway and has demonstrated oncogenic activity. (2) Methods: To clarify the role of the HH signaling pathway in tumorigenesis, the expression profile of key HH signaling molecules, including SMO, PTCH1, GLI1, GLI2, and GLI3, were determined in 33 cancer cell lines and normal prostate cells and tissues. We performed a computational analysis of the upstream region of the SMO gene to identify the regulatory elements. (3) Results: Three potential CpG islands and several putative SMO promoter elements were identified. Luciferase reporter assays mapped key SMO promoter elements, and functional binding sites for SP1, AP1, CREB, and AP-2α transcription factors in the core SMO promoter region were confirmed. A hypermethylated SMO promoter was identified in several cancer cell lines suggesting an important role for epigenetic silencing of SMO expression in certain cancer cells. (4) Discussion: These results have important implications for our understanding of regulatory mechanisms controlling HH pathway activity and the molecular basis of SMO gene function. Moreover, this study may prove valuable for future research aimed at producing therapeutic downregulation of SMO expression in cancer cells.

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.

Background: Chemokine signaling through CCR3 is a key regulatory pathway for eosinophil recruitment into tissues associated with allergic inflammation and asthma. To date, none of the CCR3 antagonists have shown efficacy in clinical trials. One reason might be their unbiased mode of inhibition that prevents receptor internalization, leading to drug tolerance. Objective: We sought to develop a novel peptide nanoparticle CCR3 inhibitor (R321) with a biased mode of inhibition that would block G protein signaling but enable or promote receptor internalization. Methods: Self-assembly of R321 peptide into nanoparticles and peptide binding to CCR3 were analyzed by means of dynamic light scattering and nuclear magnetic resonance. Inhibitory activity on CCR3 signaling was assessed in vitro by using flow cytometry, confocal microscopy, andWestern blot analysis in a CCR3(+) eosinophil cell line and blood eosinophils. In vivo effects of R321 were assessed by using a triple-allergen mouse asthma model. Results: R321 self-assembles into nanoparticles and binds directly to CCR3, altering receptor function. Half-maximal inhibitory concentration values for eotaxin-induced chemotaxis of blood eosinophils are in the low nanomolar range. R321 inhibits only the early phase of extracellular signal-regulated kinase 1/2 activation and not the late phase generally associated with beta-arrestin recruitment and receptor endocytosis, promoting CCR3 internalization and degradation. In vivo R321 effectively blocks eosinophil recruitment into the blood, lungs, and airways and prevents airway hyperresponsiveness in a mouse eosinophilic asthma model. Conclusions: R321 is a potent and selective antagonist of the CCR3 signaling cascade. Inhibition through a biased mode of antagonism might hold significant therapeutic promise by eluding the formation of drug tolerance.