Docking, virtual high throughput screening and in silico fragment-based drug design.

ABSTRACT The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high throughput screening and fragment-based ligand design.

Structure-based drug design studies of the interactions ofent-kaurane diterpenes derived from Wedelia paludosa with the Plasmodium falciparumsarco/endoplasmic reticulum Ca2+-ATPase PfATP6

Malaria is responsible for more deaths around the world than any other parasitic disease. Due to the emergence of strains that are resistant to the current chemotherapeutic antimalarial arsenal, the search for new antimalarial drugs remains urgent though hampered by a lack of knowledge regarding the molecular mechanisms of artemisinin resistance. Semisynthetic compounds derived from diterpenes from the medicinal plant Wedelia paludosawere tested in silico against the Plasmodium falciparumCa2+-ATPase, PfATP6. This protein was constructed by comparative modelling using the three-dimensional structure of a homologous protein, 1IWO, as a scaffold. Compound 21 showed the best docking scores, indicating a better interaction with PfATP6 than that of thapsigargin, the natural inhibitor. Inhibition of PfATP6 by diterpene compounds could promote a change in calcium homeostasis, leading to parasite death. These data suggest PfATP6 as a potential target for the antimalarial ent-kaurane diterpenes.

Structure- and ligand-based drug design approaches for neglected tropical diseases

Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. Structure( SBDD) and ligand-based drug design (LBDD) approaches bring together the most powerful concepts in modern chemistry and biology, linking medicinal chemistry with structural biology. The definition and assessment of both chemical and biological space have revitalized the importance of exploring the intrinsic complementary nature of experimental and computational methods in drug design. Major challenges in this field include the identification of promising hits and the development of high-quality leads for further development into clinical candidates. It becomes particularly important in the case of neglected tropical diseases (NTDs) that affect disproportionately poor people living in rural and remote regions worldwide, and for which there is an insufficient number of new chemical entities being evaluated owing to the lack of innovation and R&D investment by the pharmaceutical industry. This perspective paper outlines the utility and applications of SBDD and LBDD approaches for the identification and design of new small-molecule agents for NTDs.

Ligand- and Structure-Based Drug Design Strategies and PPAR delta/alpha Selectivity

Peroxisome-proliferator-activated receptors are a class of nuclear receptors with three subtypes: a, ? and d. Their main function is regulating gene transcription related to lipid and carbohydrate metabolism. Currently, there are no peroxisome-proliferator-activated receptors d drugs being marketed. In this work, we studied a data set of 70 compounds with a and d activity. Three partial least square models were created, and molecular docking studies were performed to understand the main reasons for peroxisome-proliferator-activated receptors d selectivity. The obtained results showed that some molecular descriptors (log P, hydration energy, steric and polar properties) are related to the main interactions that can direct ligands to a particular peroxisome-proliferator-activated receptors subtype.

Structure-based drug design studies on a series of aldolase inhibitors

Human African trypanosomiasis, also known as sleeping sickness, is a major cause of death in Africa, and for which there are no safe and effective treatments available. The enzyme aldolase from Trypanosoma brucei is an attractive, validated target for drug development. A series of alkyl‑glycolamido and alkyl-monoglycolate derivatives was studied employing a combination of drug design approaches. Three-dimensional quantitative structure-activity relationships (3D QSAR) models were generated using the comparative molecular field analysis (CoMFA). Significant results were obtained for the best QSAR model (r2 = 0.95, non-cross-validated correlation coefficient, and q2 = 0.80, cross-validated correlation coefficient), indicating its predictive ability for untested compounds. The model was then used to predict values of the dependent variables (pKi) of an external test set,the predicted values were in good agreement with the experimental results. The integration of 3D QSAR, molecular docking and molecular dynamics simulations provided further insight into the structural basis for selective inhibition of the target enzyme.

Exploring unfrequent events with accelerated molecular dynamics simulations: from photochemistry to drug design

La meva incorporació al grup de recerca del Prof. McCammon (University of California San Diego) en qualitat d’investigador post doctoral amb una beca Beatriu de Pinós, va tenir lloc el passat 1 de desembre de 2010; on vaig dur a terme les meves tasques de recerca fins al darrer 1 d’abril de 2012. El Prof. McCammon és un referent mundial en l’aplicació de simulacions de dinàmica molecular (MD) en sistemes biològics d’interès humà. La contribució més important del Prof. McCammon en la simulació de sistemes biològics és el desenvolupament del mètode de dinàmiques moleculars accelerades (AMD). Les simulacions MD convencionals, les quals estan limitades a l’escala de temps del nanosegon (~10-9s), no son adients per l’estudi de sistemes biològics rellevants a escales de temps mes llargues (μs, ms...). AMD permet explorar fenòmens moleculars poc freqüents però que son clau per l’enteniment de molts sistemes biològics; fenòmens que no podrien ser observats d’un altre manera. Durant la meva estada a la “University of California San Diego”, vaig treballar en diferent aplicacions de les simulacions AMD, incloent fotoquímica i disseny de fàrmacs per ordinador. Concretament, primer vaig desenvolupar amb èxit una combinació dels mètodes AMD i simulacions Car-Parrinello per millorar l’exploració de camins de desactivació (interseccions còniques) en reaccions químiques fotoactivades. En segon lloc, vaig aplicar tècniques estadístiques (Replica Exchange) amb AMD en la descripció d’interaccions proteïna-lligand. Finalment, vaig dur a terme un estudi de disseny de fàrmacs per ordinador en la proteïna-G Rho (involucrada en el desenvolupament de càncer humà) combinant anàlisis estructurals i simulacions AMD. Els projectes en els quals he participat han estat publicats (o estan encara en procés de revisió) en diferents revistes científiques, i han estat presentats en diferents congressos internacionals. La memòria inclosa a continuació conté més detalls de cada projecte esmentat.

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.

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.

Fragment-Based QSAR and Molecular Modeling Studies on a Series of Discodermolide Analogs as Microtubule-Stabilizing Anticancer Agents

Inhibition of microtubule function is an attractive rational approach to anticancer therapy. Although taxanes are the most prominent among the microtubule-stabilizers, their clinical toxicity, poor pharmacokinetic properties, and resistance have stimulated the search for new antitumor agents having the same mechanism of action. Discodermolide is an example of nontaxane natural product that has the same mechanism of action, demonstrating superior antitumor efficacy and therapeutic index. The extraordinary chemical and biological properties have qualified discodermolide as a lead structure for the design of novel anticancer agents with optimized therapeutic properties. In the present work, we have employed a specialized fragment-based method to develop robust quantitative structure - activity relationship models for a series of synthetic discodermolide analogs. The generated molecular recognition patterns were combined with three-dimensional molecular modeling studies as a fundamental step on the path to understanding the molecular basis of drug-receptor interactions within this important series of potent antitumoral agents.

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.