Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest

Revealing the Mechanism of Thiopeptide Antibiotics at Atomistic Resolution : Implications for Rational Drug Design

For decades drug design has primarily focused on small molecules that bind to well-formed tight binding pockets, such as the catalytic centers of enzymes. Recently, there is increasing interest to design compounds that disrupt or stabilize biomacromolecular interfaces (e.g. protein–protein, protein–DNA, protein–RNA, protein–lipid interfaces). These non-traditional drug targets hold great therapeutic potential as they govern cellular pathways. In contrast to traditional drug targets, where computational methods are now routinely and productively used to complement experiments, the use of computer-based approaches for the study and design of interfacial modulators is still in its infancy. The current thesis is a first detailed study into understanding the effects of modulators of a protein–RNA interface and developing computer-based approaches for their design. This work focuses on the 23S-L11 subunit of the ribosomal GTPase-associated region (GAR), a prototypic protein–RNA interface of high relevance in the development of novel antibacterials. The GAR is the target of naturally occuring thiopeptide antibiotics. These unique molecules are effective inhibitors of bacterial protein synthesis, but are currently unused in human antibacterial therapy due to their low aqueous solubility. Their mechanism of action is explored in the current thesis, enabling the design and proposition of new chemical scaffolds targeting their binding site. The specific challenges associated with the 23-SL11-thiopeptide system, such as the inherent flexibility of the protein–RNA composite environment and the size and structural complexity of the thiopeptide ligands, are addressed by a combination of computational chemistry approaches at different levels of granularity and a steady feedback with experimental data to validate and improve the computational techniques. These approaches range from quantummechanics for deriving optimized intramolecular parameters and partial atomic charges for the thiopeptide compounds, to molecular dynamics simulations accounting for the binding site’s flexibility, to molecular docking studies for predicting the binding modes of different thiopeptides and derivatives. All-atom molecular dynamics simulations were conducted, providing a detailed understanding of the effect of thiopeptide binding at a previously unmet resolution. The findings of this work, coupled with previous experimental knowledge, strongly support the hypothesis that restricting the binding site’s conformational flexibility is an important component of the thiopeptide antibiotics’ mode of action. With the help of an MD-docking-MD workflow and an energy decomposition analysis crucial residues of the binding site and pharmacologically relevant moieties within the ligand structures could be identified. A 4D-pharmacophore model is presented that was derived from a refined 23S-L11-thiopeptide complex and additionally accounts for the dynamic stability of molecular interactions formed between the antibiotic and the ribosomal binding site as the fourth dimension. The results of this thesis revealed, for the first time, a plausable description of the thiopeptide antibiotics’ mode of action, down to the details of their pharmacologically relevant parts and provide a computational framework for the design of new ligands

Quantifying the Role of Water in Ligand-Protein Binding Processes

The aim of this thesis is to quantify the contributions of water thermodynamics to the binding free energy in protein-ligand complexes. Various computational tools were directly applied, implemented, benchmarked and discussed. An own implementation of the IFST formulation was developed to facilitate easy integration in workflows that are based on Schrödinger software. By applying the tool to a well-defined test set of congeneric ligand pairs, the potential of IFST for quantitative predictions in lead-optimization was assessed. Furthermore, FEP calculations were applied to an extended test set to validate if these simulations can accurately account for solvent displacement in ligand modifications. As a fast tool that has applications in virtual screening problems, we finally developed and validated a new scoring function that incorporates terms for protein and ligand desolvation. This resulted in total in three distinct studies, that all elucidated different aspects of water thermodynamics in CADD. These three studies are presented in the next section. In the conclusion, the results and implications of these studies are discussed jointly, as well with possible future developments. An additional study was focused on virtual screening and toxicity prediction at the androgen receptor, where distinguishing agonists and antagonists poses difficulties. We proposed and validated an approach based on MD simulations and ensemble docking to improve predictions of androgen agonists and antagonists

A computational study on the role of solvents and conformational fluctuation of macromolecules towards drug design

A Thesis Submitted in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Life Sciences of the Nelson Mandela African Institution of Science and TechnologyHeat shock protein 90 (Hsp90) represents an important chemotherapeutic target in the treatment of various ailments including cancer and neurodegenerative diseases. The protein is responsible for controlling and regulating the growth of nearly 200 client proteins known to overexpress in tumour or cancer cells. Targeting Hsp90 and inhibiting its chaperone machinery function results in proteasome degradation of the client protein and hence treatment of the disease. In this thesis, different computational docking protocols, the role of water and conformational fluctuations in drug design for the discovery and identification of new Hsp90 inhibitors are reported. In particular, the sensitivity of different docking protocols to crystal structure with and without water, relaxed complex scheme (RCS) or ensemble-based structures holo and apo structures with and without water, the effects of including a different amount of water in the protein active site on the thermodynamics of ligand binding to protein structures are reported. There is sensitivity of results to different docking protocols, RCS lowers the binding energy in comparison to crystal structure, holo ensemble with strong ligand bound improves the docking results. Since biological activities of small molecules highly depends on the conformation, molecular structure, charge distribution and non-trivial response to solvents. The thesis further explored the role of different solvents viz polar protic, polar aprotic, and non-polar on the conformation of curcumin as a model drug/natural product. Well-tempered metadynamics (WT-MetaD), an enhanced sampling method employing OPLS-AA force field in an isobaricisothermal (NPT) ensemble was used to investigate the related solvent effects. The orientation and conformational of curcumin was solvent dependent, the free energy for curcumin in solvents and vacuum portrayed a different behaviour. Curcumin exists in different configuration and conformations in different solvents. The trans-conformation was more stable in polar aprotic solvents capable of solubilizing curcumin whereas the cis-conformation was more stable in polar protic solvents i.e water where it has marginal solubility. Finally, the thesis reports on the influence of solvents on kinetics and residence time of drug unbinding in host-guest complexes. The effect of polar aprotic and polar protic solvents on kinetics and residence time of drug unbinding from a nanoparticle was investigated using chitosan-toussantine-A as a model system. WT-MetaD was used to study the kinetics and residence time. Results show that the kinetics and residence time of drug unbinding was affected by solvents. Slow unbinding kinetics of k off = 0.045 ms 1 was observed for the system formulated with water, a polar protic solvent, while fast unbinding kinetics with k off = 1000 ms 1 was observed in system formulated with DMSO solvent. Furthermore, the interaction of chitosan-toussantine-A complex in water was observed to be stable than in DMSO. The approaches used in this thesis pave the ways and can further be extended to investigate more problems in drug design ranging from protein-ligand interaction, solution conformation of small molecules and host-guest kinetics. Since the new reported small molecules as Hsp90 inhibitors are approved for other indication, the inhibitors are recommended for further pre-clinical and clinical testing as new Hsp90 inhibitors for cancer treatment

Understanding Molecular Interactions: Application of HINT-based Tools in the Structural Modeling of Novel Anticancer and Antiviral Targets, and in Protein-Protein Docking

Computationally driven drug design/discovery efforts generally rely on accurate assessment of the forces that guide the molecular recognition process. HINT (Hydropathic INTeraction) is a natural force field, derived from experimentally determined partition coefficients that quantifies all non-bonded interactions in the biological environment, including hydrogen bonding, electrostatic and hydrophobic interactions, and the energy of desolvation. The overall goal of this work is to apply the HINT-based atomic level description of molecular systems to biologically important proteins, to better understand their biochemistry – a key step in exploiting them for therapeutic purposes. This dissertation discusses the results of three diverse projects: i) structural modeling of human sphingosine kinase 2 (SphK2, a novel anticancer target) and binding mode determination of an isoform selective thiazolidine-2,4-dione (TZD) analog; ii) structural modeling of human cytomegalorvirus (HCMV) alkaline nuclease (AN) UL98 (a novel antiviral target) and subsequent virtual screening of its active site; and iii) explicit treatment of interfacial waters during protein-protein docking process using HINT-based computational tools. SphK2 is a key regulator of the sphingosine-rheostat, and its upregulation /overexpression has been associated with cancer development. We report structural modeling studies of a novel TZD-analog that selectively inhibits SphK2, in a HINT analysis that identifies the key structural features of ligand and protein binding site responsible for isoform selectivity. The second aim was to build a three-dimensional structure of a novel HCMV target – AN UL98, to identify its catalytically important residues. HINT analysis of the interaction of 5’ DNA end at its active site is reported. A parallel aim to perform in silico screening with a site-based pharmacophore model, identified several novel hits with potentially desirable chemical features for interaction with UL98 AN. The majority of current protein-protein docking algorithms fail to account for water molecules involved in bridging interactions between partners, mediating and stabilizing their association. HINT is capable of reproducing the physical and chemical properties of such waters, while accounting for their energetic stabilizing contributions. We have designed a solvated protein-protein docking protocol that explicitly models the Relevant bridging waters, and demonstrate that more accurate results are obtained when water is not ignored

An enhanced-sampling MD-based protocol for molecular docking

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g. from molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the unbound (apo) protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables able to sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. In this work, we assessed the method on re-docking and cross-docking calculations. In first case, we selected three different protein targets undergoing different extent of conformational changes upon binding and, for each of them, we docked the experimental ligand conformation into an ensemble of receptor structures generated by EDES. In the second case, in the contest of a blind docking challenge, we generated the 3D structures of a set of different ligands of the same receptor and docked them into a set of EDES-generated conformations of that receptor. In all cases, for both re-docking and cross-docking experiments, our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry of the receptor. Moreover, ensemble docking calculations using those conformations yielded in almost all cases to native-like poses among the top-ranked ones. Finally, we also tested an improved EDES recipe on a further target, known to be extremely challenging due to its extended binding region and the large extent of conformational changes accompanying the binding of its ligands

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

Characterizing HIV-1 Transactivation Response Element Dynamics that Govern Ligand Recognition: Direct Applications to Drug Discovery.

RNAs regulate and affect numerous cellular processes, making them highly sought therapeutic targets. One mechanism to inhibit functional RNAs is to alter their structural-dynamics using small molecule binders. While structure based drug design is often used to discover small molecule inhibitors, difficulties arise because, unlike proteins, RNAs undergo large conformational changes between the free and ligand-bound-states that cannot be determined a priori. The spatial and temporal complexity of these conformational changes precludes accurate characterization that would allow one to visualize the conformational changes. Using NMR and MD we aim to uncover the biophysical principles governing TAR-mediated ligand recognition and discover new TAR-binding small molecules. First, we present a Sample and Select (SAS) method, which combines NMR residual dipolar couplings (RDCs) and MD to provide an accurate all-atom description of RNA dynamics over sub-millisecond timescales. RDCs measured on elongated TAR molecules are used to separate internal and overall motions and impose a helix-anchored reference frame. Using the SAS approach, refined RNA ensembles that re-capitulate experimental RDCs are generated from an MD trajectory. Specific snapshots of the ensemble closely agree with previously determined ligand-bound TAR structures, suggesting that the bound-state conformations are sampled in the absence of ligand. In a second study we investigate the sequence dependence of TAR dynamics and show that a modest mutation greatly perturbs global and local dynamics giving rise to changes in small molecule binding affinity while still forming the same bound-state TAR conformation. Lastly, the SAS ensemble structures are used in RNA structure-based drug discovery. Computational docking simulations are used to discover 11 TAR-binding small molecules, 8 of which have never before been shown to bind TAR and 2 never before been shown to bind RNA. NMR chemical shift perturbations and fluorescence polarization measurements verify that the small molecules bind TAR and inhibit the TAR-Tat interaction with inhibition constants ranging 0.627-300 μM. RDCs measured on TAR bound to the small molecule netilmicin suggest that docking against the SAS structures accurately re-capitulates the bound-state. Remarkably, netilmicin also inhibits TAR-mediated HIV-1 LTR expression and HIV-1 replication in an indicator cell line with an IC50 of 23.1 μM.Ph.D.Chemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/76006/1/stelzer_1.pd

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