Browsing by Subject "chemical libraries"

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    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 Medicine
    Virtual 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.
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    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 Medicine
    Like 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.