Mapping major SARS-CoV-2 drug targets and assessment of druggability using computational fragment screening: Identification of an allosteric small-molecule binding site on the Nsp13 helicase.

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\u27-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

Computational method development for drug discovery

Protein-small molecule interactions play a central role in various aspects of the structural and functional organization of the cell and are therefore integral for drug discovery. The most comprehensive structural characterization of small molecule binding sites is provided by X-ray crystallography. However, it is often time-consuming and challenging to perform direct experimental analysis. Therefore, it is necessary to have computational methods that can predict binding site locations on unbound structures with accuracy close to that provided by X-ray crystallography. This thesis details four projects which involve the development of a fragment benchmark set, evaluation of allosteric sites in G Protein-Coupled Receptors (GPCRs), computational modeling of binding pocket dynamics, and the development of an Application Program Interface (API) framework for High-Performance Computing (HPC) centers. The first project provides a benchmark set for testing hot spot identification methods, emphasizing application to fragment-based drug discovery. Using the solvent mapping server, FTMap, which finds small molecule binding hot spots on proteins, we compared our benchmark set to an existing benchmark set that with a different method of construction. The second project details the effort to identify allosteric binding sites on GPCRs. We demonstrate that FTMap successfully identifies structurally determined allosteric sites in bound crystal structures and unbound structures. The project was further expanded to evaluate the conservation of allosteric sites across different classes, families, and types of GPCRs. The third project provides a structure-based analysis of cryptic site openings. Cryptic sites are pockets formed in ligand-bound proteins but not observed in unbound protein structures. Through analysis of crystal structures supplemented by molecular dynamics (MD) with enhanced sampling techniques, it was shown that cryptic sites can be grouped into three types: 1) “genuine” cryptic sites, which do not form without ligand binding, 2) spontaneously forming cryptic sites, and 3) cryptic sites impacted by mutations or off-site ligand binding. The fourth project presents an API framework for increasing the accessibility of HPC resources

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

Pharmacophore Modeling Using Site-Identification by Ligand Competitive Saturation (SILCS) with Multiple Probe Molecules

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design