Strategy to Target the Substrate Binding site of SET Domain Protein Methyltransferases

Protein methyltransferases (PMTs) are a novel gene family of therapeutic relevance involved in chromatin-mediated signaling and other biological mechanisms. Most PMTs are organized around the structurally conserved SET domain that catalyzes the methylation of a substrate lysine. A few potent chemical inhibitors compete with the protein substrate, and all are anchored in the channel recruiting the methyl-accepting lysine. We propose a novel strategy to design focused chemical libraries targeting the substrate binding site, where a limited number of warheads each occupying the lysine-channel of multiple enzymes would be decorated by different substituents. A variety of sequence and structure-based approaches used to analyze the diversity of the lysine channel of SET domain PMTs support the relevance of this strategy. We show that chemical fragments derived from published inhibitors are valid warheads that can be used in the design of novel focused libraries targeting other PMTs

Computational Modeling of Protein Structure, Function, and Binding Hotspots

Mixed-solvent molecular dynamics (MixMD) is a cosolvent mapping technique for structure-based drug design. MixMD simulations are performed with a solvent mixture of small molecule probes and water, which directly compete for binding to the protein’s surface. MixMD has previously been shown to identify active and allosteric sites based on the time-averaged occupancy of the probe molecules over the course of the simulation. Sites with the highest maximal occupancy identified known biologically relevant sites for a wide range of targets. This is consistent with previous experimental work identifying hotspots on protein surfaces based on the occupancy of multiple organic-solvent molecules. However, previous MixMD analysis required extensive manual interpretation to identify and rank sites. MixMD Probeview was introduced to automate this analysis, thereby facilitating the application of MixMD. Implemented as a plugin for the freely available, open-source version of PyMOL, MixMD Probeview successfully identified binding sites for several test systems using three different cosolvent simulation procedures. Following identification of binding sites, the occupancy maps from the MixMD simulations can be converted into pharmacophore models for prospective screening of inhibitors. We have developed a pharmacophore generation procedure to convert MixMD occupancy maps into pharmacophore models. Validation of this procedure on ABL kinase showed good performance. Additionally, we have identified characteristic occupancy levels for non-displaceable water molecules so that these sites may be incorporated into structure-based drug design efforts. Lastly, we have explored the potential for accelerated sampling methods to be used in tandem with MixMD to simultaneously capture conformational changes while mapping favorable interactions within binding sites. These developments greatly extend the utility of MixMD while also simplifying its application. In addition, two exploratory studies were completed. First, traditional MD simulations were performed to understand the dynamics of NSD1. Crystal structures of NSD1 capture the post-SET loop in an autoinhibitory position. MD simulations allow conformational sampling of this loop, yielding insight into its dynamic behavior in solution. Second, an epidemiological study was conducted which was aimed at understanding the transmission and sequence variation of CTX-M-type β-lactamases, in fulfillment of the clinical research component of the MICHR Translational Research Education Certificate.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138744/1/sarahgra_1.pd

Investigating the mechanism of action of the chemical probe QC6352

Glioblastoma, despite being the most aggressive form of brain cancer, has seen no significant advancement in patient survival rates since the introduction of the Stupp protocol in 2005. The stagnation in therapeutic progress highlights the complex challenges inherent to glioblastoma treatment, namely, a highly heterogeneous tumour population, an immunosuppressive microenvironment, and the restrictive blood-brain barrier. These challenges are compounded by a critical lack of robust data in preclinical studies, leading to suboptimal drug candidates that ultimately fail in clinical trials. In a comprehensive systematic review, we assessed the adherence to expert-recommended practices in the use of chemical probes. Our findings revealed that only 4% of the analysed publications complied with the established guidelines. Improper use of chemical probes results in biased conclusions about the importance of certain proteins in disease models, embedding inaccurate information as the foundation for further drug development studies. The chemical probe QC6352, a known KDM4 inhibitor, was identified through phenotypic screening of a library of epigenetic inhibitors to have potent antiproliferative efficacy in glioblastoma cells. While validating the anti-proliferative efficacy of QC6352 in glioblastoma, discrepancies arose regarding the importance of KDM4 in QC6352’s mechanism of action. By integrating diverse state-of-the-art investigations at the genomic, transcriptomic, and proteomic levels, a phenotype reflecting an activated MAPK pathway was identified and considered to result from the inhibition of PP5 by QC6352. This PhD thesis highlights the critical need to follow best practice guidelines for preclinical mechanistic studies to establish robust findings and eliminate biased and potentially incorrect conclusions. To obtain improved glioblastoma therapies, the foundational research surrounding the novel target must be derived from robust and well-controlled experiments