Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase: a potential target to reduce Fusarium head blight disease.

2016

Structural plasticity in Mycobacterium tuberculosis uracil-DNA glycosylase (MtUng) and its functional implications

17 independent crystal structures of family I uracil-DNA glycosylase from Mycobacterium tuberculosis (MtUng) and its complexes with uracil and its derivatives, distributed among five distinct crystal forms, have been determined. Thermodynamic parameters of binding in the complexes have been measured using isothermal titration calorimetry. The two-domain protein exhibits open and closed conformations, suggesting that the closure of the domain on DNA binding involves conformational selection. Segmental mobility in the enzyme molecule is confined to a 32-residue stretch which plays a major role in DNA binding. Uracil and its derivatives can bind to the protein in two possible orientations. Only one of them is possible when there is a bulky substituent at the 50 position. The crystal structures of the complexes provide a reasonable rationale for the observed thermodynamic parameters. In addition to providing fresh insights into the structure, plasticity and interactions of the protein molecule, the results of the present investigation provide a platform for structure-based inhibitor design.

Spectrum of Somatic Hypermutations and Implication on Antibody Function: Case of the anti HIV-1 antibody, b12

Thesis (Ph.D.)--University of Washington, 2015

La dihydrofolate réductase R67, comme une cible d’antibiotiques et biocatalyseur potentiel

La dihyrofolate réductase de type II R67 (DHFR R67) est une enzyme bactérienne encodée par un plasmide donc aisément transmissible. Elle catalyse la réaction de réduction du dihydrofolate (DHF) en tétrahydrofolate (THFA) essentiel pour la prolifération cellulaire. La DHFR R67 est une enzyme qui dépend du cofacteur NADPH. La DHFR R67 est différente, structurellement et génétiquement, de l’enzyme DHFR chromosomale présente chez tous les organismes et elle est résistante au triméthoprime (TMP) qui est largement utilisé dans les traitements antibactériens chez l’Homme. Aucun inhibiteur sélectif contre la DHFR R67 n’est actuellement répertorié. Le but de cette étude a été d’identifier des molécules qui pourront inhiber la DHFR R67 sélectivement, sans affecter la DHFR humaine (DHFRh). La vérification de la qualité des essais enzymatiques en conditions déterminées pour le criblage d’inhibiteurs sur plusieurs lectrices à plaques a identifié des appareils appropriés pour l’analyse. L’étude de l’activité enzymatique de la DHFR R67 et de la DHFRh en présence des solvants organiques et liquides ioniques (LIs), comme des co-solvants pour le criblage rationnel d’inhibiteurs, a montré que certains LIs peuvent servir de milieu alternatif pour les essais enzymatiques. Le criblage rationnel basé sur l’approche du design d’un inhibiteur à partir de petites molécules, a révélé des molécules primaires qui inhibent la DHFR R67 de façon faible, mais sélective. Le test des composés biologiquement actifs qui comprennent des petits fragments, a montré l’augmentation de l’affinité entre la DHFR R67 et les composés testés. Trois composés ont été déterminés comme des inhibiteurs sélectifs prometteurs pour la DHFR R67.

Monoaminergic Neuropathology in Alzheimer's disease

Acknowledgments This work was supported by The Croatian Science Foundation grant. no. IP-2014-09-9730 (“Tau protein hyperphosphorylation, aggregation, and trans-synaptic transfer in Alzheimer’s disease: cerebrospinal fluid analysis and assessment of protective neuroprotective compounds”) and European Cooperation in Science and Technology (COST) Action CM1103 (“Stucture-based drug design for diagnosis and treatment of neurological diseases: dissecting and modulating complex function in the monoaminergic systems of the brain”). PRH is supported in part by NIH grant P50 AG005138.

Use of Vertex Index in Structure-Activity Analysis and Design of Molecules

The last few decades have witnessed application of graph theory and topological indices derived from molecular graph in structure-activity analysis. Such applications are based on regression and various multivariate analyses. Most of the topological indices are computed for the whole molecule and used as descriptors for explaining properties/activities of chemical compounds. However, some substructural descriptors in the form of topological distance based vertex indices have been found to be useful in identifying activity related substructures and in predicting pharmacological and toxicological activities of bioactive compounds. Another important aspect of drug discovery e. g. designing novel pharmaceutical candidates could also be done from the distance distribution associated with such vertex indices. In this article, we will review the development and applications of this approach both in activity prediction as well as in designing novel compounds.

Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q(2) = 0.68 and r(2) = 0.72; HQSAR, q(2) = 0.63 and r(2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(pred)(2) = 0.87; classical QSAR, r(pred)(2) = 0.75).

Classical and fragment-based hologram structure-activity relationships for a series of analgesic cyclic imides

Cyclic imides have been widely employed in drug design research due to their multiple pharmacological and biological properties. In the present study, two-dimensional quantitative structure-activity relationship (2D QSAR) studies were conducted on a series of potent analgesic cyclic imides using both classical and hologram QSAR (HQSAR) methods, yielding significant statistical models (classical QSAR, q(2) = 0.80; HQSAR, q(2) = 0.84). The models were then used to evaluate an external data test, and the predicted values were in good agreement with the experimental results, indicating their consistency for untested compounds.

Molecular hybridization: A useful tool in the design of new drug prototypes

Molecular hybridization is a new concept in drug design and development based on the combination of pharmacophoric moieties of different bioactive substances to produce a new hyrid compound with improved affinity and efficacy, when compared to the parent drugs. Additionally, this strategy can results in compounds presenting modified selectivity profile, different and/or dual modes of action and reduced undesired side effects. So, in this described several example of different strategies for drug design, discovery and pharmacomodulation focused on new innovative hybrid compounds presenting analgesic, anti-inflammatory, platelet anti-aggregating, anti-infections, anticancer, cardio- and neuroactive properties.

Structure- and Ligand-Based Structure-Activity Relationships for a Series of Inhibitors of Aldolase

Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r(2) = 0.98 and q(2) = 0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pK(i) values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.

Rational design of serine protease inhibitors

Proteases regulate a spectrum of diverse physiological processes, and dysregulation of proteolytic activity drives a plethora of pathological conditions. Understanding protease function is essential to appreciating many aspects of normal physiology and progression of disease. Consequently, development of potent and specific inhibitors of proteolytic enzymes is vital to provide tools for the dissection of protease function in biological systems and for the treatment of diseases linked to aberrant proteolytic activity. The studies in this thesis describe the rational design of potent inhibitors of three proteases that are implicated in disease development. Additionally, key features of the interaction of proteases and their cognate inhibitors or substrates are analysed and a series of rational inhibitor design principles are expounded and tested. Rational design of protease inhibitors relies on a comprehensive understanding of protease structure and biochemistry. Analysis of known protease cleavage sites in proteins and peptides is a commonly used source of such information. However, model peptide substrate and protein sequences have widely differing levels of backbone constraint and hence can adopt highly divergent structures when binding to a protease’s active site. This may result in identical sequences in peptides and proteins having different conformations and diverse spatial distribution of amino acid functionalities. Regardless of this, protein and peptide cleavage sites are often regarded as being equivalent. One of the key findings in the following studies is a definitive demonstration of the lack of equivalence between these two classes of substrate and invalidation of the common practice of using the sequences of model peptide substrates to predict cleavage of proteins in vivo. Another important feature for protease substrate recognition is subsite cooperativity. This type of cooperativity is commonly referred to as protease or substrate binding subsite cooperativity and is distinct from allosteric cooperativity, where binding of a molecule distant from the protease active site affects the binding affinity of a substrate. Subsite cooperativity may be intramolecular where neighbouring residues in substrates are interacting, affecting the scissile bond’s susceptibility to protease cleavage. Subsite cooperativity can also be intermolecular where a particular residue’s contribution to binding affinity changes depending on the identity of neighbouring amino acids. Although numerous studies have identified subsite cooperativity effects, these findings are frequently ignored in investigations probing subsite selectivity by screening against diverse combinatorial libraries of peptides (positional scanning synthetic combinatorial library; PS-SCL). This strategy for determining cleavage specificity relies on the averaged rates of hydrolysis for an uncharacterised ensemble of peptide sequences, as opposed to the defined rate of hydrolysis of a known specific substrate. Further, since PS-SCL screens probe the preference of the various protease subsites independently, this method is inherently unable to detect subsite cooperativity. However, mean hydrolysis rates from PS-SCL screens are often interpreted as being comparable to those produced by single peptide cleavages. Before this study no large systematic evaluation had been made to determine the level of correlation between protease selectivity as predicted by screening against a library of combinatorial peptides and cleavage of individual peptides. This subject is specifically explored in the studies described here. In order to establish whether PS-SCL screens could accurately determine the substrate preferences of proteases, a systematic comparison of data from PS-SCLs with libraries containing individually synthesised peptides (sparse matrix library; SML) was carried out. These SML libraries were designed to include all possible sequence combinations of the residues that were suggested to be preferred by a protease using the PS-SCL method. SML screening against the three serine proteases kallikrein 4 (KLK4), kallikrein 14 (KLK14) and plasmin revealed highly preferred peptide substrates that could not have been deduced by PS-SCL screening alone. Comparing protease subsite preference profiles from screens of the two types of peptide libraries showed that the most preferred substrates were not detected by PS SCL screening as a consequence of intermolecular cooperativity being negated by the very nature of PS SCL screening. Sequences that are highly favoured as result of intermolecular cooperativity achieve optimal protease subsite occupancy, and thereby interact with very specific determinants of the protease. Identifying these substrate sequences is important since they may be used to produce potent and selective inhibitors of protolytic enzymes. This study found that highly favoured substrate sequences that relied on intermolecular cooperativity allowed for the production of potent inhibitors of KLK4, KLK14 and plasmin. Peptide aldehydes based on preferred plasmin sequences produced high affinity transition state analogue inhibitors for this protease. The most potent of these maintained specificity over plasma kallikrein (known to have a very similar substrate preference to plasmin). Furthermore, the efficiency of this inhibitor in blocking fibrinolysis in vitro was comparable to aprotinin, which previously saw clinical use to reduce perioperative bleeding. One substrate sequence particularly favoured by KLK4 was substituted into the 14 amino acid, circular sunflower trypsin inhibitor (SFTI). This resulted in a highly potent and selective inhibitor (SFTI-FCQR) which attenuated protease activated receptor signalling by KLK4 in vitro. Moreover, SFTI-FCQR and paclitaxel synergistically reduced growth of ovarian cancer cells in vitro, making this inhibitor a lead compound for further therapeutic development. Similar incorporation of a preferred KLK14 amino acid sequence into the SFTI scaffold produced a potent inhibitor for this protease. However, the conformationally constrained SFTI backbone enforced a different intramolecular cooperativity, which masked a KLK14 specific determinant. As a consequence, the level of selectivity achievable was lower than that found for the KLK4 inhibitor. Standard mechanism inhibitors such as SFTI rely on a stable acyl-enzyme intermediate for high affinity binding. This is achieved by a conformationally constrained canonical binding loop that allows for reformation of the scissile peptide bond after cleavage. Amino acid substitutions within the inhibitor to target a particular protease may compromise structural determinants that support the rigidity of the binding loop and thereby prevent the engineered inhibitor reaching its full potential. An in silico analysis was carried out to examine the potential for further improvements to the potency and selectivity of the SFTI-based KLK4 and KLK14 inhibitors. Molecular dynamics simulations suggested that the substitutions within SFTI required to target KLK4 and KLK14 had compromised the intramolecular hydrogen bond network of the inhibitor and caused a concomitant loss of binding loop stability. Furthermore in silico amino acid substitution revealed a consistent correlation between a higher frequency of formation and the number of internal hydrogen bonds of SFTI-variants and lower inhibition constants. These predictions allowed for the production of second generation inhibitors with enhanced binding affinity toward both targets and highlight the importance of considering intramolecular cooperativity effects when engineering proteins or circular peptides to target proteases. The findings from this study show that although PS-SCLs are a useful tool for high throughput screening of approximate protease preference, later refinement by SML screening is needed to reveal optimal subsite occupancy due to cooperativity in substrate recognition. This investigation has also demonstrated the importance of maintaining structural determinants of backbone constraint and conformation when engineering standard mechanism inhibitors for new targets. Combined these results show that backbone conformation and amino acid cooperativity have more prominent roles than previously appreciated in determining substrate/inhibitor specificity and binding affinity. The three key inhibitors designed during this investigation are now being developed as lead compounds for cancer chemotherapy, control of fibrinolysis and cosmeceutical applications. These compounds form the basis of a portfolio of intellectual property which will be further developed in the coming years.

Structural Basis of Drug Resistance by Genetic Variants of HIV Type 1 Clade C Protease from India

Using computer modeling of three-dimensional structures and structural information available on the crystal structures of HIV-1 protease, we investigated the structural effects of mutations, in treatment-naive and treatment-exposed individuals from India and postulated mechanisms of resistance in clade C variants. A large number of models (14) have been generated by computational mutation of the available crystal structures of drug bound proteases. Localized energy minimization was carried out in and around the sites of mutation in order to optimize the geometry of interactions present. Most of the mutations result in structural differences at the flap that favors the semiopen state of the enzyme. Some of the mutations were also found to confer resistance by affecting the geometry of the active site. The E35D mutation affects the flap structure in clade B strains and E35N and E35K mutation, seen in our modeled strains, have a more profound effect. Common polymorphisms at positions 36 and 63 in clade C also affected flap structure. Apart from a few other residues Gln-58, Asn-83, Asn-88, and Gln-92 and their interactions are important for the transition from the closed to the open state. Development of protease inhibitors by structure-based design requires investigation of mechanisms operative for clade C to improve the efficacy of therapy.

On the functional ordering of biomembranes : Implications for drug binding

Cells are packed with membrane structures, defining the inside and outside, and the different subcellular compartments. These membranes consisting mainly of phospholipids have a variety of functions in addition to providing a permeability barrier for various compounds. These functions involve cellular signaling, where lipids can act as second messengers, or direct regulation of membrane associating proteins. The first part of this study focuses on relating some of the physicochemical properties of membrane lipids to the association of drug compounds to membranes. A fluorescence based method is described allowing for determination of the membrane association of drugs. This method was subsequently applied to a novel drug, siramesine, previously shown to have anti-cancer activity. Siramesine was found to associate with anionic lipids. Especially interesting is its strong affinity for a second messenger lipid phosphatidic acid. This is the first example of a small molecule drug compound specifically interacting with a cellular lipid. Phosphatidic acid in cells is required for the activation of many signaling pathways mediating growth and proliferation. This provides an intriguing possibility for a simple molecular mechanism of the observed anti-cancer activity of siramesine. In the second part the thermal behavior and self assembly of charged and uncharged membrane assemblies was studied. Strong inter-lamellar co-operativity was observed for multilamellar DPPC vesicles using fluorescence techniques together with calorimetry. The commonly used membrane models, large unilamellar vesicles (LUV) and multilamellar vesicles (MLV) were found to possess different biophysical properties as interlamellar interactions of MLVs drive segregation of a pyrene labeled lipid analogue into clusters. The effect of a counter-ion lattice on the self assembly of a cationic gemini surfactant was studied. The presence of NaCl strongly influenced the thermal phase behavior of M-1 vesicles, causing formation of giant vesicles upon exceeding a phase transition temperature, followed by a subsequent transition into a more homogenous dispersion. Understanding the underlying biophysical aspects of cellular membranes is of fundamental importance as the complex picture of the structure and function of cells is evolving. Many of the cellular reactions take place on membranes and membranes are known to regulate the activity of many peripheral and intergral membrane associating proteins. From the point of view of drug design and gene technology, membranes can provide an interesting target for future development of drugs, but also a vehicle sensitive for environmental changes allowing for encapsulating drugs and targeting them to the desired site of action.

Engineered protease inhibitors based on sunflower trypsin inhibitor-1 (SFTI-1) provide insights into the role of sequence and conformation in Laskowski mechanism inhibition

Laskowski inhibitors regulate serine proteases by an intriguing mode of action that involves deceiving the protease into synthesizing a peptide bond. Studies exploring naturally occurring Laskowski inhibitors have uncovered several structural features that convey the inhibitor's resistance to hydrolysis and exceptional binding affinity. However, in the context of Laskowski inhibitor engineering, the way that various modifications intended to fine-tune an inhibitor's potency and selectivity impact on its association and dissociation rates remains unclear. This information is important as Laskowski inhibitors are becoming increasingly used as design templates to develop new protease inhibitors for pharmaceutical applications. In this study, we used the cyclic peptide, sunflower trypsin inhibitor-1 (SFTI-1), as a model system to explore how the inhibitor's sequence and structure relate to its binding kinetics and function. Using enzyme assays, MD simulations and NMR spectroscopy to study SFTI variants with diverse sequence and backbone modifications, we show that the geometry of the binding loop mainly influences the inhibitor's potency by modulating the association rate, such that variants lacking a favourable conformation show dramatic losses in activity. Additionally, we show that the inhibitor's sequence (including both the binding loop and its scaffolding) influences its potency and selectivity by modulating both the association and the dissociation rates. These findings provide new insights into protease inhibitor function and design that we apply by engineering novel inhibitors for classical serine proteases, trypsin and chymotrypsin and two kallikrein-related peptidases (KLK5 and KLK14) that are implicated in various cancers and skin diseases.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.