In Silico Identification of a β2-Adrenoceptor Allosteric Site That Selectively Augments Canonical β2AR-Gs Signaling and Function

Activation of β2-adrenoceptors (β2ARs) causes airway smooth muscle (ASM) relaxation and bronchodilation, and β2AR agonists (β-agonists) are front-line treatments for asthma and other obstructive lung diseases. However, the therapeutic efficacy of β-agonists is limited by agonist-induced β2AR desensitization and noncanonical β2AR signaling involving β-arrestin that is shown to promote asthma pathophysiology. Accordingly, we undertook the identification of an allosteric site on β2AR that could modulate the activity of β-agonists to overcome these limitations. We employed the site identification by ligand competitive saturation (SILCS) computational method to comprehensively map the entire 3D structure of in silico-generated β2AR intermediate conformations and identified a putative allosteric binding site. Subsequent database screening using SILCS identified drug-like molecules with the potential to bind to the site. Experimental assays in HEK293 cells (expressing recombinant wild-type human β2AR) and human ASM cells (expressing endogenous β2AR) identified positive and negative allosteric modulators (PAMs and NAMs) of β2AR as assessed by regulation of β-agonist-stimulation of cyclic AMP generation. PAMs/NAMs had no effect on β-agonist-induced recruitment of β-arrestin to β2AR- or β-agonist-induced loss of cell surface expression in HEK293 cells expressing β2AR. Mutagenesis analysis of β2AR confirmed the SILCS identified site based on mutants of amino acids R131, Y219, and F282. Finally, functional studies revealed augmentation of β-agonist-induced relaxation of contracted human ASM cells and bronchodilation of contracted airways. These findings identify a allosteric binding site on the β2AR, whose activation selectively augments β-agonist-induced Gs signaling, and increases relaxation of ASM cells, the principal therapeutic effect of β-agonists

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

Molecular dynamics and virtual screening approaches in drug discovery

Computer-aided drug discovery (CADD) methods are now routinely used in the preclinical phase of drug development. Powerful high-performance computing facilities and the extremely fast CADD methods constantly scale up the coverage of drug-like chemical space achievable in rational drug development. In this thesis, CADD approaches were applied to address several early-phase drug discovery problems. Namely, small molecule binding site detection on a novel target protein, virtual screening (VS) of molecular databases, and characterization of small molecule interactions with metabolic enzymes were studied. Various CADD methods, including molecular dynamics (MD) simulations in mixed solvents, molecular docking, and binding free energy calculations, were employed. Co-solvent MD simulations detected biologically relevant binding sites and provided guidance for screening potential protein-protein interaction inhibitors for a crucial protein of the SARS-CoV-2. VS with fragment- and negative image-based (F-NIB) models identified three active and structurally novel inhibitors of the putative drug target phosphodiesterase 10A. MD simulations and docking provided detailed insights on the effects of active site structural flexibility and variation on the binding and resultant metabolism of small molecules with the cytochrome P450 enzymes. The results presented in this thesis contribute to the increasing evidence that supports employment and further development of CADD approaches in drug discovery. Ultimately, rational drug development coupled with CADD may enable higher quality drug candidates to the human studies in the future, reducing the risk of financially and temporally costly clinical failure. KEYWORDS: Structure-based drug development, Computer-aided drug discovery (CADD), Molecular dynamics (MD) simulation, Virtual screening (VS), Fragmentand negative image-based (F-NIB) model, Structure-activity relationship (QSAR), Cytochrome P450 ligand binding predictionMolekyylidynamiikka- ja virtuaaliseulontamenetelmät lääkeaine-etsinnässä Tietokoneavusteista lääkeaine-etsintää käytetään nykyisin yleisesti prekliinisessä lääketutkimuksessa. Suurteholaskenta ja äärimmäisen nopeat tietokoneavusteiset lääkeaine-etsintämenetelmät mahdollistavat jatkuvasti kattavamman lääkkeenkaltaisten molekyylien kemiallisen avaruuden seulonnan. Tässä väitöskirjassa tietokonepohjaisia menetelmiä hyödynnettiin lääketutkimuksen prekliiniseen vaiheeseen liittyvissä tyypillisissä tutkimusongelmissa. Työhön kuului pienmolekyylien sitoutumisalueiden tunnistus uuden kohdeproteiinin rakenteesta, molekyylitietokantojen virtuaaliseulonta sekä pienmolekyylien ja metabolian entsyymien välisten vuorovaikutusten tietokonemallinnus. Työssä käytettiin useita tietokoneavusteisen lääkeaine-etsinnän menetelmiä, sisältäen molekyylidynamiikkasimulaatiot (MD-simulaatiot) vaihtuvissa liuottimissa, molekulaarisen telakoinnin ja sitoutumisenergian laskennan. Orgaanisen liuottimen ja veden sekoituksessa tehdyt MD-simulaatiot tunnistivat biologisesti merkittäviä sitoutumisalueita SARS-CoV-2:n tärkeästä proteiinista ja ohjasivat infektioon liittyvän proteiini-proteiinivuorovaikutuksen potentiaalisten estäjien etsintää. Virtuaaliseulonnalla tunnistettiin kolme rakenteellisesti uudenlaista tunnetun lääkekehityskohteen, fosfodiesteraasi 10A:n, estäjää hyödyntäen fragmentti- ja negatiivikuvamalleja. MD-simulaatiot ja telakointi tuottivat yksityiskohtaista tietoa sytokromi P450 entsyymien aktiivisen kohdan rakenteen jouston ja muutosten vaikutuksesta pienmolekyylien sitoutumiseen ja metaboliaan. Tämän väitöskirjan tulokset tukevat kasvavaa todistusaineistoa tietokoneavusteisen lääkeaine-etsinnän käytön ja kehityksen hyödyllisyydestä prekliinisessä lääketutkimuksessa. Tietokoneavusteinen lääkeaine-etsintä voi lopulta mahdollistaa korkeampilaatuisten lääkekandidaattien päätymisen ihmiskokeisiin, pienentäen taloudellisesti ja ajallisesti kalliin kliinisen tutkimuksen epäonnistumisen riskiä. AVAINSANAT: Rakennepohjainen lääkeainekehitys, Tietokoneavusteinen lääkeaine-etsintä, Molekyylidynamiikkasimulaatio (MD-simulaatio), Virtuaaliseulonta, Fragmentti- ja negatiivikuvamalli, Rakenne-aktiivisuussuhde, Sytokromi P450 ligandien sitoutumisen ennustu

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 (De)-Solvation Effects and Protein Flexibility in Protein-Ligand Binding using Molecular Dynamics Simulations

Water is a crucial participant in virtually all cellular functions. Evidently, water molecules in the binding site contribute significantly to the strength of intermolecular interactions in the aqueous phase by mediating protein-ligand interactions, solvating and de-solvating both ligand and protein upon protein-ligand dissociation and association. Recently many published studies use water distributions in the binding site to retrospectively explain and rationalize unexpected trends in structure-activity relationships (SAR). However, traditional approaches cannot quantitatively predict the thermodynamic properties of water molecules in the binding sites and its associated contribution to the binding free energy of a ligand. We have developed and validated a computational method named WATsite to exploit high-resolution solvation maps and thermodynamic profiles to elucidate the water molecules’ potential contribution to protein-ligand and protein-protein binding. We have also demonstrated the utility of the computational method WATsite to help direct medicinal chemistry efforts by using explicit water de-solvation. In addition, protein conformational change is typically involved in the ligand-binding process which may completely change the position and thermodynamic properties of the water molecules in the binding site before or upon ligand binding. We have shown the interplay between protein flexibility and solvent reorganization, and we provide a quantitative estimation of the influence of protein flexibility on desolvation free energy and, therefore, protein-ligand binding. Different ligands binding to the same target protein can induce different conformational adaptations. In order to apply WATsite to an ensemble of different protein conformations, a more efficient implementation of WATsite based on GPU-acceleration and system truncation has been developed. Lastly, by extending the simulation protocol from pure water to mixed water-organic probes simulations, accurate modeling of halogen atom-protein interactions has been achieved

Activation and Inhibition of Biological Function through Design of Novel Protein-Ligand Interactions

Virtually every process within a cell involves a protein. They serve as cellular workhorses carrying out functions such as catalysis of essential metabolites, to regulating which genes get turned on or off, to forming the structural scaffolding to retain rigidity of a cell. Proteins form the link between the genetic information encoded in DNA to the observable phenotype of an organism. The way proteins communicate is by direct physical contact with another molecule that alters its shape and dynamics to carry out a particular function. For example, G protein-coupled receptors are membrane imbedded proteins that bind to a small molecule or peptide in the extracellular environment and translate the binding event into an internal signal to regulate processes such as heart rate and even mood. The ability to selectively modulate such fundamental systems offers huge potential with broad applications from the ability to interrogate unknown cellular mechanisms to developing therapeutics when these interactions become aberrant. The scope of this dissertation encompasses determining what properties dictate protein-ligand interactions and the application of these principles to the design of new ones. In particular, chapter 1 covers the design of a molecular switch that is turned on by small molecules. I follow this up in chapter 2 by investigating how to turn off protein function with small molecules in aberrant disease states. In chapter 3 we expand from the world of small molecule ligands to design a protein to turn off function of a protein involved in bacterial pathogenesis

The Automatic Detection of Small Molecule Binding Hotspots on Proteins: Applying Hotspots to Structure-Based Drug Design

Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods typically assess the pocket as a whole, doing little to suggest which regions and interactions are the most important for binding. This thesis introduces Fragment Hotspot Maps, a grid-based method that samples atomic propensities derived from interactions in the Cambridge Structural Database (CSD) with simple molecular probes. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores, offering more precision over other binding site prediction methods. The method is validated by scoring the positions of 21 fragment and lead pairs. Fragment atoms are found in the highest scoring parts of the map corresponding to their atom type, with a median percentage rank of 98%. This is reduced to 72% for lead atoms, showing that the method can differentiate between the hotspots, and the warm spots later used during fragment elaboration. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. These calculations are easily accessible through a simple to use web application, which only requires an input PDB structure or code. High scoring specific interactions predicted by the Fragment Hotspot Maps can be used to guide existing computer aided drug discovery methods. The Hotspots Python API has been created to allow these work flows to be executed programmatically through a single Python script. Two of the functions use scores from the Fragment Hotspot Maps to guide virtual screening methods, docking and field-based ligand screening. Docking virtual screening performance is improved by using a constraint selected from the highest scoring polar interaction. The field-based ligand screener uses modified versions of the Fragment Hotspot Maps directly to predict and score the binding pose. This workflow gave comparable results to docking, and for one target, Glucocorticoid receptor (GCR), showed much better results, highlighting its potential as an orthogonal approach. Fragment Hotspot Maps can be used at multiple stages of the drug discovery process, and research into these applications is ongoing. Their utility in the following areas are currently being explored: to assess ligandability for both individual structures and across proteomes, to aid in library design, to assess pockets throughout a molecular dynamics trajectory, to prioritise crystallographic fragment hits and to guide hit-to-lead development.Funded by the BBSRC and UC

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Using molecular dynamics and enhanced sampling techniques to find cryptic druggable pockets in proteins of pharmaceutical interest

Cryptic pockets are sites on protein targets that are hidden in the unliganded form and only become apparent when drugs bind. These sites provide a promising alternative to classical substrate binding sites for drug development, especially when the latter are not druggable. In this thesis I investigate the nature and dynamical properties of cryptic sites in a number of pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. I found that the studied cryptic sites do not correspond to local minima in the computed conformational free-energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as parallel tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. The observed mechanism of cryptic site formation is suggestive of interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, I developed a novel Hamiltonian replica exchange-based method SWISH (sampling water interfaces through scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. In addition, we revisit the rather ill-defined concept of the cryptic pockets in order to propose an alternative measurable interpretation. I outline how the new practical definition can be applied to the ligandable targets reported in the PDB, in order to provide a consistent data-driven view on crypticity and how it may impact the drug discovery. This thesis presents a comprehensive study of the cryptic pocket phenomenon: from understanding the nature of their formation to novel detection methodology, and towards understanding their global significance in drug discovery