Insights into the Development of Chemotherapeutics Targeting PFKFB Enzymes

The PFKFB enzymes control the primary checkpoint in the glycolytic pathway and are implicated in a multitude of diseases: from cancer, to schizophrenia, to diabetes, and heart disease. The inducible isoform, PFKFB3, is known to be associated with the upregulation of glycolysis in many cancers. The first study within this work investigates the potential for using tier-based approaches of virtual screening to target small molecule kinases, with PFKFB3 serving as a case study. For this investigation, bioactive compounds for PFKFB3 were identified from a compound library of 1364 compounds via high-throughput screening, with bioactive compounds being further characterized as either competitive or non-competitive for F6P. Using the F6P-competitive compounds, several structure based docking programs were assessed individually and in conjunction with a pharmacophore screening. The results showed that the tiered virtual screening approach, using pharmacophore screening in addition to structure-based docking, improved enrichments rates in 80% of cases, reduced CPU costs up to 7-fold, and lessened variability among different structure-based docking methods. The second study investigates the structural and kinetic characteristics of citrate inhibition on the heart PFKFB isoenzyme, PFKFB2. High levels of citrate, an intermediate of the TCA cycle, signify an abundance of biosynthetic precursors and that additional glucose need not be degraded for this purpose. Previous studies have noted that citrate acts as an important negative feed-back mechanism to limit glycolytic activity by inhibiting PFKFB enzymes, yet the structural and mechanistic details of citrate’s inhibition had not been determined. To study the molecular basis for citrate inhibition, the three-dimensional structures of the human and bovine PFKFB2 orthologues were solved, each in complex with citrate. For both cases, citrate primarily occupied the binding site of Fructose-6-phosphate (F6P), competitively blocking F6P from binding. Additionally, a carboxy arm of citrate extended into the γ-phosphate binding site of ATP, sterically and electrostatically blocking the catalytic binding mode for ATP. In the human orthologue, which utilized AMPPNP as an ATP analogue, conformational changes were observed in the 2-kinase domain as well as the binding mode for AMPPNP. This study gives new insights as to how the citrate-mediate negative feedback loop influences glycolytic flux through PFKFB enzymes

A Computational Approach to Finding Novel Targets for Existing Drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects

Next generation 3D pharmacophore modeling

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond

Activation of Hsp90 Enzymatic Activity and Conformational Dynamics through Rationally Designed Allosteric Ligands

Hsp90 is a molecular chaperone of pivotal importance for multiple cell pathways. ATP-regulated internal dynamics are critical for its function and current pharmacological approaches block the chaperone with ATP-competitive inhibitors. Herein, a general approach to perturb Hsp90 through design of new allosteric ligands aimed at modulating its functional dynamics is proposed. Based on the characterization of a first set of 2-phenylbenzofurans showing stimulatory effects on Hsp90 ATPase and conformational dynamics, new ligands were developed that activate Hsp90 by targeting an allosteric site, located 65 æ from the active site. Specifically, analysis of protein responses to first-generation activators was exploited to guide the design of novel derivatives with improved ability to stimulate ATP hydrolysis. The molecules’ effects on Hsp90 enzymatic, conformational, cochaperone and client-binding properties were characterized through biochemical, biophysical and cellular approaches. These designed probes act as allosteric activators of the chaperone and affect the viability of cancer cell lines for which proper functioning of Hsp90 is necessary

Using Molecular Dynamics to Elucidate the Mechanism of Cyclophilin

Cyclophilins are ubiquitous enzymes that are involved in protein folding, signal transduction, viral proliferation, oncogenesis, and regulation of the immune system. Cyclophilin A is the prototype of the cyclophilin family. We use molecular dynamics to describe the catalytic mechanism of cyclophilin A in full atomistic detail by sampling critical points along the reaction coordinate, and use accelerated molecular dynamics to sample cis-trans interconversions. At these critical points, we analyze the conformational space sampled by the active site, flexibility of the enzyme backbone, and modulation of binding interactions.We use Kramer’s rate theory to determine how diffusion and free energy contribute to lowering the activation energy of prolyl isomerization. We also find preferential binding modes of several cyclophiln A inhibitors, and compare the conformational space sampled by inhibited cyclophilin A to the conformational space sampled during wild-type interactions. We also analyze the mechanism of the next family member cyclophilin B in order to probe differences in enzyme dynamics and intermolecular interactions that could possibly be exploited in isoform-specific drug design. Our results indicate that cyclophilin proceeds by a conformational selection binding mechanism that manipulates substrate sterics, electrostatic interactions, and multiple reaction timescales in order to speed up reaction rate. Conformational space sampled by cyclophilin when inhibited and when undergoing wild-type interactions share significant similarity. Cyclophilins A and B do have notable differences in enzyme dynamics, due to variation in intramolecular interactions that arise from variation in primary structures. This work demonstrates how computational methods can be used to clarify catalytic mechanisms

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

Targeting Bacterial Resistance: Rational Design and Evaluation of Antibiotic Combinations with Adjuvants

Antimicrobial resistance (AMR) has become a major global health concern, posing significant challenges to modern medicine. The diminishing pipeline of novel antimicrobial agents compounds the problem, as big pharmaceutical companies have reduced investments in this area. The rise of AMR has rendered traditional treatment approaches less effective, demanding the identification and development of new therapeutic options. Some bacteria employ enzymes to evade the effects of antimicrobial agents, such as FosA, which degrades fosfomycin, and NDM-1, which hydrolyzes carbapenems. These enzymes pose a critical concern as they target drugs with favorable spectra and are often considered last-resort treatment options. To combat AMR, rational drug design aims to develop compounds that inhibit the enzymes responsible for drug inactivation. Computer-aided drug discovery (CADD) techniques, including structure-based and ligand-based approaches, aid in identifying potential inhibitors. In Chapter 2, the study identifies baicalin, alendronate, risedronate, and zoledronate as FosA inhibitors. When combined with fosfomycin, these compounds demonstrate a reduction in the minimum inhibitory concentration (MIC) of fosfomycin and additive effects. Zoledronate shows the most promise as an adjuvant; however, the combination with fosfomycin exhibits a lower killing rate. Further investigation reveals that a chemical reaction occurred within the mixture, resulting in the degradation of fosfomycin, as confirmed by 31P NMR experiments. Chapter 3 explores the synergy between meropenem with bisphosphonates and various inhibitors against carbapenem-resistant E. coli and K. pneumoniae. The bisphosphonates demonstrated potentiation effects through NDM-1 enzyme inhibition, leading to increased bacterial susceptibility to meropenem. Alendronate and linezolid exhibited synergistic bactericidal activity when combined with meropenem against E. coli, while sutezolid showed promising results