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Item Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries(Wiley, 2018-12-20) Si, Yubing; Xu, David; Bum-Erdene, Khuchtumur; Ghozayel, Mona K.; Yang, Baocheng; Clemons, Paul A.; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of MedicineThere is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR·uPA, TEAD4·Yap1, and CaVα·CaVβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR·uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4·Yap1. Virtual screening of conformationally restricted compounds targeting uPAR·uPA and TEAD4·Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.Item A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein–Protein Interaction Interfaces(ACS, 2017-08) Xu, David; Bum-Erdene, Khuchtumur; Si, Yubing; Zhou, Donghui; Liu, Degang; Ghozayel, Mona; Meroueh, Samy; Biochemistry and Molecular Biology, School of MedicineThe binding affinity of a protein–protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein–protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein–protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein–protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein–protein interactions do not optimally mimic protein–ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials.Item Computational Methods to Identify and Target Druggable Binding Sites at Protein-Protein Interactions in the Human Proteome(2019-09) Xu, David; Wu, Huanmei; Meroueh, Samy; Liu, Xiaowen; Janga, Sarath Chandra; Liu, YunlongProtein-protein interactions are fundamental in cell signaling and cancer progression. An increasing prevalent idea in cancer therapy is the development of small molecules to disrupt protein-protein interactions. Small molecules impart their action by binding to pockets on the protein surface of their physiological target. At protein-protein interactions, these pockets are often too large and tight to be disrupted by conventional design techniques. Residues that contribute a disproportionate amount of energy at these interfaces are known as hot spots. The successful disruption of protein-protein interactions with small molecules is attributed to the ability of small molecules to mimic and engage these hot spots. Here, the role of hot spots is explored in existing inhibitors and compared with the native protein ligand to explore how hot spot residues can be leveraged in protein-protein interactions. Few studies have explored the use of interface residues for the identification of hit compounds from structure-based virtual screening. The tight uPAR•uPA interaction offers a platform to test methods that leverage hot spots on both the protein receptor and ligand. A method is described that enriches for small molecules that both engage hot spots on the protein receptor uPAR and mimic hot spots on its protein ligand uPA. In addition, differences in chemical diversity in mimicking ligand hot spots is explored. In addition to uPAR•uPA, there are additional opportunities at unperturbed protein-protein interactions implicated in cancer. Projects such as TCGA, which systematically catalog the hallmarks of cancer across multiple platforms, provide opportunities to identify novel protein-protein interactions that are paramount to cancer progression. To that end, a census of cancer-specific binding sites in the human proteome are identified to provide opportunities for drug discovery at the system level. Finally, tumor genomic, protein-protein interaction, and protein structural data is integrated to create chemogenomic libraries for phenotypic screening to uncover novel GBM targets and generate starting points for the development of GBM therapeutic agents.Item Covalent Fragment Screening Identifies Rgl2 RalGEF Cysteine for Targeted Covalent Inhibition of Ral GTPase Activation(Wiley, 2022) Bum-Erdene, Khuchtumur; Ghozayel, Mona K.; Xu, David; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of MedicineRal GTPases belong to the RAS superfamily, and they are directly activated by K-RAS. The RalGEF pathway is one of the three major K-RAS signaling pathways. Ral GTPases do not possess a cysteine nucleophile to develop a covalent inhibitor following the strategy that led to a K-RAS G12C therapeutic agent. However, several cysteine amino acids exist on the surface of guanine exchange factors that activate Ral GTPases, such as Rgl2. Here, we screen a library of cysteine electrophile fragments to determine if covalent bond formation at one of the Rgl2 surface cysteines could inhibit Ral GTPase activation. We found several chloroacetamide and acrylamide fragments that inhibited Ral GTPase exchange by Rgl2. Site-directed mutagenesis showed that covalent bond formation at Cys-284, but not other cysteines, leads to inhibition of Ral activation by Rgl2. Follow-up time- and concentration-dependent studies of derivatives identified by substructure search of commercial libraries further confirmed Cys-284 as the reaction site and identified the indoline fragments as the most promising series for further development. Cys-284 is located outside of the Ral•Rgl2 interface on a loop that has several residues that come in direct contact with Ral GTPases. Our allosteric covalent fragment inhibitors provide a starting point for the development of small-molecule covalent inhibitors to probe Ral GTPases in animal models.Item Design and Synthesis of Fragment Derivatives with a Unique Inhibition Mechanism of the uPAR·uPA Interaction(American Chemical Society, 2020) Bum-Erdene, Khuchtumur; Liu, Degang; Xu, David; Ghozayel, Mona K.; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of Medicine;There is substantial interest in the development of small molecules that inhibit the tight and highly challenging protein-protein interaction between the glycophosphatidylinositol (GPI)-anchored cell surface receptor uPAR and the serine protease uPA. While preparing derivatives of a fragment-like compound that previously emerged from a computational screen, we identified compound 5 (IPR-3242), which inhibited binding of uPA to uPAR with submicromolar IC50s. The high inhibition potency prompted us to carry out studies to rule out potential aggregation, lack of stability, reactivity, and nonspecific inhibition. We designed and prepared 16 derivatives to further explore the role of each substituent. Interestingly, the compounds only partially inhibited binding of a fluorescently labeled α-helical peptide that binds to uPAR at the uPAR·uPA interface. Collectively, the results suggest that the compounds bind to uPAR outside of the uPAR·uPA interface, trapping the receptor into a conformation that is not able to bind to uPA. Additional studies will have to be carried out to determine whether this unique inhibition mechanism can occur at the cell surface.Item Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries(ACS, 2016-06-27) Xu, David; Meroueh, Samy O.; Department of Biochemistry & Molecular Biology, IU School of MedicineVirtual screening consists of docking libraries of small molecules to a target protein followed by rank-ordering of the resulting structures using scoring functions. The ability of scoring methods to distinguish between actives and inactives depends on several factors that include the accuracy of the binding pose during the docking step and the quality of the three-dimensional structure of the target. Here, we build on our previous work to introduce a new scoring approach (SVMGen) that uses machine learning trained with features from statistical pair potentials obtained from three-dimensional crystal structures. We use SVMGen and GlideScore to explore how enrichment or rank-ordering is affected by binding pose accuracy. To that end, we create a validation set that consists strictly of proteins whose crystal structure was solved in complex with their inhibitors. For the rank-ordering studies, we use crystal structures from PDBbind along with corresponding binding affinity data provided in the database. In addition to binding pose, we investigate the effect of using modeled structures for the target on the enrichment performance of SVMGen and GlideScore. To accomplish this, we generated homology models for protein kinases in DUD-E for which crystal structures are available to enable comparison of enrichment between modeled and crystal structure. We also generate homology models for kinases in SARfari for which there are many known small-molecule inhibitors but no known crystal structure. These models are used to assess the ability of SVMGen and GlideScore to distinguish between actives and decoys. We focus our work on protein kinases considering the wealth of structural and binding affinity data that exists for this family of proteins.Item Exploring a structural protein-drug interactome for new therapeutics in lung cancer(Royal Society of Chemistry, 2014-03-04) Peng, Xiaodong; Wang, Fang; Li, Liwei; Bum-Erdene, Khuchtumur; Xu, David; Wang, Bo; Sinn, Tony; Pollok, Karen; Sandusky, George; Li, Lang; Turchi, John; Jalal, Shadia I.; Meroueh, Samy; Department of Biochemistry & Molecular Biology, IU School of MedicineThe pharmacology of drugs is often defined by more than one protein target. This property can be exploited to use approved drugs to uncover new targets and signaling pathways in cancer. Towards enabling a rational approach to uncover new targets, we expand a structural protein-ligand interactome () by scoring the interaction among 1000 FDA-approved drugs docked to 2500 pockets on protein structures of the human genome. This afforded a drug-target network whose properties compared favorably with previous networks constructed using experimental data. Among drugs with the highest degree and betweenness two are cancer drugs and one is currently used for treatment of lung cancer. Comparison of predicted cancer and non-cancer targets reveals that the most cancer-specific compounds were also the most selective compounds. Analysis of compound flexibility, hydrophobicity, and size showed that the most selective compounds were low molecular weight fragment-like heterocycles. We use a previously-developed screening approach using the cancer drug erlotinib as a template to screen other approved drugs that mimic its properties. Among the top 12 ranking candidates, four are cancer drugs, two of them kinase inhibitors (like erlotinib). Cellular studies using non-small cell lung cancer (NSCLC) cells revealed that several drugs inhibited lung cancer cell proliferation. We mined patient records at the Regenstrief Medical Record System to explore the possible association of exposure to three of these drugs with occurrence of lung cancer. Preliminary in vivo studies using the non-small cell lung cancer (NCLSC) xenograft model showed that losartan- and astemizole-treated mice had tumors that weighed 50 (p < 0.01) and 15 (p < 0.01) percent less than the treated controls. These results set the stage for further exploration of these drugs and to uncover new drugs for lung cancer therapy.Item Mimicking Intermolecular Interactions of Tight Protein–Protein Complexes for Small-Molecule Antagonists(Wiley, 2017-11) Xu, David; Bum-Erdene, Khuchtumur; Si, Yubing; Zhou, Donghui; Ghozayel, Mona; Meroueh, Samy; Biochemistry and Molecular Biology, School of MedicineTight protein–protein interactions (Kd<100 nm) that occur over a large binding interface (>1000 Å2) are highly challenging to disrupt with small molecules. Historically, the design of small molecules to inhibit protein–protein interactions has focused on mimicking the position of interface protein ligand side chains. Here, we explore mimicry of the pairwise intermolecular interactions of the native protein ligand with residues of the protein receptor to enrich commercial libraries for small-molecule inhibitors of tight protein–protein interactions. We use the high-affinity interaction (Kd=1 nm) between the urokinase receptor (uPAR) and its ligand urokinase (uPA) to test our methods. We introduce three methods for rank-ordering small molecules docked to uPAR: 1) a new fingerprint approach that represents uPA′s pairwise interaction energies with uPAR residues; 2) a pharmacophore approach to identify small molecules that mimic the position of uPA interface residues; and 3) a combined fingerprint and pharmacophore approach. Our work led to small molecules with novel chemotypes that inhibited a tight uPAR⋅uPA protein–protein interaction with single-digit micromolar IC50 values. We also report the extensive work that identified several of the hits as either lacking stability, thiol reactive, or redox active. This work suggests that mimicking the binding profile of the native ligand and the position of interface residues can be an effective strategy to enrich commercial libraries for small-molecule inhibitors of tight protein–protein interactions.Item Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data(ACS, 2020-06) Xu, David; Zhou, Donghui; Bum-Erdene, Khuchtumur; Bailey, Barbara J.; Sishtla, Kamakshi; Liu, Sheng; Wan, Jun; Aryal, Uma K.; Lee, Jonathan A.; Wells, Clark D.; Fishel, Melissa L.; Corson, Timothy W.; Pollok, Karen E.; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of MedicineLike most solid tumors, glioblastoma multiforme (GBM) harbors multiple overexpressed and mutated genes that affect several signaling pathways. Suppressing tumor growth of solid tumors like GBM without toxicity may be achieved by small molecules that selectively modulate a collection of targets across different signaling pathways, also known as selective polypharmacology. Phenotypic screening can be an effective method to uncover such compounds, but the lack of approaches to create focused libraries tailored to tumor targets has limited its impact. Here, we create rational libraries for phenotypic screening by structure-based molecular docking chemical libraries to GBM-specific targets identified using the tumor’s RNA sequence and mutation data along with cellular protein–protein interaction data. Screening this enriched library of 47 candidates led to several active compounds, including 1 (IPR-2025), which (i) inhibited cell viability of low-passage patient-derived GBM spheroids with single-digit micromolar IC50 values that are substantially better than standard-of-care temozolomide, (ii) blocked tube-formation of endothelial cells in Matrigel with submicromolar IC50 values, and (iii) had no effect on primary hematopoietic CD34+ progenitor spheroids or astrocyte cell viability. RNA sequencing provided the potential mechanism of action for 1, and mass spectrometry-based thermal proteome profiling confirmed that the compound engages multiple targets. The ability of 1 to inhibit GBM phenotypes without affecting normal cell viability suggests that our screening approach may hold promise for generating lead compounds with selective polypharmacology for the development of treatments of incurable diseases like GBM.Item Small Molecules Engage Hot Spots through Cooperative Binding To Inhibit a Tight Protein–Protein Interaction(ACS, 2017-03) Liu, Degang; Xu, David; Liu, Min; Knabe, William Eric; Yuan, Cai; Zhou, Donghui; Huang, Mingdong; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of MedicineProtein–protein interactions drive every aspect of cell signaling, yet only a few small-molecule inhibitors of these interactions exist. Despite our ability to identify critical residues known as hot spots, little is known about how to effectively engage them to disrupt protein–protein interactions. Here, we take advantage of the ease of preparation and stability of pyrrolinone 1, a small-molecule inhibitor of the tight interaction between the urokinase receptor (uPAR) and its binding partner, the urokinase-type plasminogen activator uPA, to synthesize more than 40 derivatives and explore their effect on the protein–protein interaction. We report the crystal structure of uPAR bound to previously discovered pyrazole 3 and to pyrrolinone 12. While both 3 and 12 bind to uPAR and compete with a fluorescently labeled peptide probe, only 12 and its derivatives inhibit the full uPAR·uPA interaction. Compounds 3 and 12 mimic and engage different hot-spot residues on uPA and uPAR, respectively. Interestingly, 12 is involved in a π–cation interaction with Arg-53, which is not considered a hot spot. Explicit-solvent molecular dynamics simulations reveal that 3 and 12 exhibit dramatically different correlations of motion with residues on uPAR. Free energy calculations for the wild-type and mutant uPAR bound to uPA or 12 show that Arg-53 interacts with uPA or with 12 in a highly cooperative manner, thereby altering the contributions of hot spots to uPAR binding. The direct engagement of peripheral residues not considered hot spots through π–cation or salt-bridge interactions could provide new opportunities for enhanced small-molecule engagement of hot spots to disrupt challenging protein–protein interactions.