Applications of nuclear magnetic resonance spectroscopy: from drug discovery to protein structure and dynamics.

The versatility of nuclear magnetic resonance (NMR) spectroscopy is apparent when presented with diverse applications to which it can contribute. Here, NMR is used i) as a screening/ validation tool for a drug discovery program targeting the Phosphatase of Regenerating Liver 3 (PRL3), ii) to characterize the conformational heterogeneity of p53 regulator, Murine Double Minute X (MDMX), and iii) to characterize the solution dynamics of guanosine monophosphate kinase (GMPK). Mounting evidence suggesting roles for PRL3 in oncogenesis and metastasis has catapulted it into prominence as a cancer drug target. Yet, despite significant efforts, there are no PRL3 small molecule inhibitors currently in clinical trials. This work combines screening of an FDA-approved drug panel and the identification of binders by protein-observed NMR. FDA-approved drugs salirasib and candesartan were identified as potent inhibitors in in vitro inhibition and migration assays while a weak inhibitor, olsalazine, was identified by NMR as the first small molecule inhibitor to directly bind PRL3. NMR was also used to validate the binding of additional compounds identified as experimental PRL3 inhibitors. Thienopyridone, a potent experimental inhibitor, did not show direct binding to PRL3 but instead inhibited phosphatase activity via redox mechanism. NMR also revealed that other experimental inhibitors did not engage PRL3. Thus, there remains a need to identify potent PRL3-directed inhibitors. Meanwhile, molecular modeling revealed a putative druggable site that has not been thoroughly explored before. The current study provides some scaffolds such as candesartan and particularly, olsalazine, the only binder identified, that could be the starting point of further drug discovery efforts, as well as a putative site that can be targeted in silico. MDMX, a negative regulator of p53, is another important therapeutic target in cancer, along with the homologous protein, MDM2. Inhibitors that block the MDM2-p53 interaction have been identified and despite similarities in the binding site of these homologous proteins, these inhibitors are ineffective against MDMX. It is hypothesized that the flexibility of MDMX contributes to this significant difference in response to inhibitors, despite comparable affinity to their endogenous target, p53. Examination of available inhibitor-bound structures of MDMX reveal a conserved pharmacophore but the structures adopt distinct conformations away from the binding site. This implies that global motions of the protein might contribute to molecular recognition. The conformational heterogeneity in MDMX was further confirmed by collecting residual dipolar couplings (RDCs). Further investigations on both MDMX and MDM2 are necessary to uncover whether the flexibility of MDMX contributes to the differential binding to inhibitors. Finally, NMR relaxation methods and state-of-the-art high-power Carr-Purcell-Meiboom Gill (CPMG) relaxation dispersion measurements, the first documented application on an enzyme, were used to characterize the solution dynamics of GMPK and the changes in dynamics upon GMP binding. Substrate binding resulted in restricting the amplitudes of motion for backbone amide bonds within the picosecond-nanosecond timescale. Meanwhile, CPMG showed dispersion in both in the absence and presence of GMP, such that substrate binding did not quench dynamics within the microsecond-millisecond timescale. Interestingly, more residues are observed to have dispersion in the bound form, some near the C-terminal of helix 3, which has previously been proposed to be involved in product release. Current studies show that substrate binding affect different timescales of protein motion. Future work shall follow how motions within different timescales are affected as GMPK processes its substrates – such as, for instance, binding of ATP analogs within the ATP binding site or simultaneous occupancy of both substrate binding pockets. This paves the way for a complete picture of the relationship of function and dynamics in the conformational enzymatic cycle of a bi-substrate enzyme using GMPK as a model. The current work illustrates some of the diverse applications of NMR on three unique systems that are also drug targets. Information collected here can be leveraged on future structure and dynamics studies as well as drug discovery efforts targeting any of these proteins

Structural characterization and selective drug targeting of higher-order DNA G-quadruplex systems.

There is now substantial evidence that guanine-rich regions of DNA form non-B DNA structures known as G-quadruplexes in cells. G-quadruplexes (G4s) are tetraplex DNA structures that form amid four runs of guanines which are stabilized via Hoogsteen hydrogen bonding to form stacked tetrads. DNA G4s have roles in key genomic functions such as regulating gene expression, replication, and telomere homeostasis. Because of their apparent role in disease, G4s are now viewed as important molecular targets for anticancer therapeutics. To date, the structures of many important G4 systems have been solved by NMR or X-ray crystallographic techniques. Small molecules developed to target these structures have shown promising results in treating cancer in vitro and in vivo, however, these compounds commonly lack the selectivity required for clinical success. There is now evidence that long single-stranded G-rich regions can stack or otherwise interact intramolecularly to form G4-multimers, opening a new avenue for rational drug design. For a variety of reasons, G4 multimers are not amenable to NMR or X-ray crystallography. In the current dissertation, I apply a variety of biophysical techniques in an integrative structural biology (ISB) approach to determine the primary conformation of two disputed higher-order G4 systems: (1) the extended human telomere G-quadruplex and (2) the G4-multimer formed within the human telomerase reverse transcriptase (hTERT) gene core promoter. Using the higher-order human telomere structure in virtual drug discovery approaches I demonstrate that novel small molecule scaffolds can be identified which bind to this sequence in vitro. I subsequently summarize the current state of G-quadruplex focused virtual drug discovery in a review that highlights successes and pitfalls of in silico drug screens. I then present the results of a massive virtual drug discovery campaign targeting the hTERT core promoter G4 multimer and show that discovering selective small molecules that target its loops and grooves is feasible. Lastly, I demonstrate that one of these small molecules is effective in down-regulating hTERT transcription in breast cancer cells. Taken together, I present here a rigorous ISB platform that allows for the characterization of higher-order DNA G-quadruplex structures as unique targets for anticancer therapeutic discovery

Structure- and Ligand-Based Design of Novel Antimicrobial Agents

The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

Protein Flexibility in Structure-Based Drug Design: Method Development and Novel Mechanisms for Inhibiting HIV-1 Protease.

Structure-based drug design (SBDD) has emerged as an important tool in drug discovery research. Traditionally, SBDD is based on a static crystal structure of the target protein. However, a protein in solution exists as an ensemble of energetically accessible conformations and is best described when all states are represented. Upon ligand binding, further conformational changes in the receptor can be induced. While ligand flexibility can be accurately reproduced, replicating the innumerable degrees of freedom of the protein is impractical due to limitations in computational power. Previously, Carlson et al. developed a robust method to generate receptor-based pharmacophore models based on an ensemble of protein conformations. The use of multiple protein structures (MPS) allows a range of conformational space that can be assumed by the protein to be sampled and hence, simulates the inherent flexibility of a binding site in a computationally feasible manner. Small molecule probes are used to map energetically favorable regions of each protein active site, and the MPS are then overlaid to identify the most important, chemically relevant features conserved across the conformations. Here, we have refined the MPS method by developing techniques to optimize different steps in the procedure. First, we outline tools to properly overlay flexible proteins based on the rigid regions of the structure by incorporating a Gaussian weight into a standard RMSD alignment. Atoms that barely move between the two conformations will have a greater weighting than those that have a large displacement. Using HIV-1 protease (HIV-1p) as a test case, we next examine the use of various sources of MPS: snapshots of an apo structure across a molecular dynamics simulation, a bound NMR ensemble, and a collection of bound crystal structures. Finally, we implement a simple ranking metric into the MPS method to quantify ligand overlap with a contour-based representation of the pharmacophore model. Overlapping in a region of the active site dense with pharmacophore spheres results in a higher ranking of a ligand pose. The refined MPS method and other computational techniques are then applied to study HIV-1p and investigate a novel inhibition mechanism by modulating its conformational behavior.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57666/2/kdamm_1.pd

Exploring the Role of Molecular Dynamics Simulations in Most Recent Cancer Research: Insights into Treatment Strategies

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer initiation and progression. Molecular dynamics simulations enable researchers to study the behavior of biomolecules at an atomic level, providing insights into the dynamics and interactions of proteins, nucleic acids, and other molecules involved in cancer development. In this review paper, we provide an overview of the latest advances in molecular dynamics simulations of cancer cells. We will discuss the principles of molecular dynamics simulations and their applications in cancer research. We also explore the role of molecular dynamics simulations in understanding the interactions between cancer cells and their microenvironment, including signaling pathways, proteinprotein interactions, and other molecular processes involved in tumor initiation and progression. In addition, we highlight the current challenges and opportunities in this field and discuss the potential for developing more accurate and personalized simulations. Overall, this review paper aims to provide a comprehensive overview of the current state of molecular dynamics simulations in cancer research, with a focus on the molecular mechanisms underlying cancer initiation and progression.Comment: 49 pages, 2 figure

Pocket optimization and its application to identify small-molecule inhibitors of protein-protein interactions

Because of their ubiquitous nature in many cellular processes, modulating protein-protein interactions offers tremendous therapeutic potential. However, protein-protein interactions remain a difficult class of drug targets, as most protein interaction sites have not evolved to bind small molecules. Indeed, some protein interaction sites are thought to be simply not amenable to binding any small molecule at all. Other sites feature small molecule binding pockets that simply are not present in the unbound or protein-bound conformations, making structure-based drug discovery difficult. Sometimes, inhibitors bind to multiple family members with high affinity, causing toxicity. In this dissertation I seek to address many of these challenges, by developing methodologies to assess the druggability of a target, assess the selectivity of known inhibitors, identify conformations that are sampled uniquely by a single protein, and identify inhibitors of protein-protein interactions. To assess druggability, I developed the “pocket optimization” protocol which uses a biasing potential to create an ensemble of conformations that contain pockets at a specified location on the protein surface. I showed that low-resolution, low energy inhibitor shapes are encoded at druggable sites and sampled through low-energy fluctuations, whereas they are not present at random sites on protein surfaces. To assess selectivity and screen for inhibitors, I developed “exemplars”, representations of a pocket based on the perfect “non-physical” complementary ligand, allowing the comparison of pocket shapes independent of protein sequence. I predicted the selectivity of an array of inhibitors to a related family of proteins by comparing the exemplars from the known small-molecule bound conformation to the ensemble of exemplars from a “pocket optimized” ensemble. I identified distinct conformations that could be targeted for identifying selective inhibitors de novo by comparing ensembles of exemplars from related family members to one another. Finally, I developed a screening protocol that uses the speed of exemplar versus small molecule comparisons to screen very large compound libraries against ensembles of distinct, “pocket optimized” pocket conformations

Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti-influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti-influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS-Map) to identify putative ‘hot spots’ within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40-ns MD simulations, CS-Map was utilized to assess the ability of small, solvent-sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150- and 430-loop regions. Our hot spot analysis provides further support for the feasibility of developing high-affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain

Development and optimisation of computational tools for drug discovery

The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds.The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds

Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions.

Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.Work in DRS’s laboratory is supported by the the European Union, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Medical Research Council and Wellcome Trust. Work in ARV’s laboratory is supported by the Medical Research Council and Wellcome Trust. Work in DJH's laboratory is supported by the Medical Research Council under grant ML/L007266/1. All calculations were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/) provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and were funded by the EPSRC under grants EP/F032773/1 and EP/J017639/1. GJM and ARV are affiliated with PhoreMost Ltd, Cambridge. We thank Alicia Higueruelo and John Skidmore for helpful discussions.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.chembiol.2015.04.01