Dynamic undocking and the quasi-bound state as tools for drug discovery

There is a pressing need for new technologies that improve the efficacy and efficiency of drug discovery. Structure-based methods have contributed towards this goal but they focus on predicting the binding affinity of protein–ligand complexes, which is notoriously difficult. We adopt an alternative approach that evaluates structural, rather than thermodynamic, stability. As bioactive molecules present a static binding mode, we devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a quasi-bound state at which the ligand has just broken the most important native contact with the receptor. This non-equilibrium property is surprisingly effective in virtual screening because true ligands form more-resilient interactions than decoys. Notably, DUck is orthogonal to docking and other ‘thermodynamic’ methods. We demonstrate the potential of the docking–undocking combination in a fragment screening against the molecular chaperone and oncology target Hsp90, for which we obtain novel chemotypes and a hit rate that approaches 40

QCSPScore: a new scoring function for driving protein-ligand docking with quantitative chemical shifts perturbations

Through the use of information about the biological target structure, the optimization of potential drugs can be improved. In this work I have developed a procedure that uses the quantitative change in the chemical perturbations (CSP) in the protein from NMR experiments for driving protein-ligand docking. The approach is based on a hybrid scoring function (QCSPScore) which combines traditional DrugScore potentials, which describe the interaction between protein and ligand, with Kendall’s rank correlation coefficient, which evaluates docking poses in terms of their agreement with experimental CSP. Prediction of the CSP for a specific ligand pose is done efficiently with an empirical model, taking into account only ring current effects. QCSPScore has been implemented in the AutoDock software package. Compared to previous methods, this approach shows that the use of rank correlation coefficient is robust to outliers. In addition, the prediction of native-like complex geometries improved because the CSP are already being used during the docking process, and not only in a post-filtering setting for generated docking poses. Since the experimental information is guaranteed to be quantitatively used, CSP effectively contribute to align the ligand in the binding pocket. The first step in the development of QCSPScore was the analysis of 70 protein-ligand complexes for which reference CSP were computed. The success rate in the docking increased from 71% without involvement of CSP to 100% if CSP were considered at the highest weighting scheme. In a second step QCSPScore was used in re-docking three test cases, for which reference experimental CSP data was available. Without CSP, i.e. in the use of conventional DrugScore potentials, none of the three test cases could be successfully re-docked. The integration of CSP with the same weighting factor as described above resulted in all three cases successfully re-docked. For two of the three complexes, native-like solutions were only produced if CSP were considered.Conformational changes in the binding pockets of up to 2 Å RMSD did not affect the success of the docking. QCSPScore will be particularly interesting in difficult protein-ligand complexes. They are in particular those cases in which the shape of the binding pocket does not provide sufficient steric restraints such as in flat protein-protein interfaces and in the virtual screening of small chemical fragments.Durch die Verwendung von Information über die biologische Zielstruktur kann die Optimierung potentieller Wirkstoffe verbessert werden. Im Rahmen dieser Arbeit habe ich ein Verfahren entwickelt, das quantitativ die Veränderung der Chemischen Verschieben (CSP) im Protein aus NMR-Experimenten für das Protein-Ligand-Docking verwendet. Der Ansatz basiert auf einer Hybridbewertungsfunktion (QCSPScore) und kombiniert herkömmliche DrugScore-Potentiale, welche die Wechselwirkung zwischen Protein und Ligand beschreiben, mit dem Rangkorrelationskoeffizienten nach Kendall, der die Dockingposen hinsichtlich ihrer Übereinstimmung mit experimentellen CSP. Die Vorhersage der CSP für einen bestimmten Liganden geschieht effizient mit einem empirischen Modell, wobei nur Ringstromeffekte berücksichtigt werden. QCSPScore wurde in das AutoDock Softwarepaket implementiert. Im Vergleich zu früheren Verfahren zeigt dieser Ansatz, dass die Verwendung des Rangkorrelationskoeffizienten robuster ist gegenüber Ausreißern in den vorhergesagten CSP. Außerdem ist die Vorhersage nativ-ähnlicher Komplexgeometrien verbessert, da die CSP bereits während des Docking-Prozesses eingesetzt werden, und nicht erst in einem nachträglichen Filter für generierte Dockingposen. Da die experimentelle Informationen quantitativ benutzt werden wird sichergestellt, dass die CSP effektiv dazu beitragen, den Liganden in der Bindetasche auszurichten. Der erste Schritt bei der Entwicklung des QCSPScore war die Analyse von 70 Protein-Ligand-Komplexen, für die als Referenz CSP vorhergesagt wurden. Die Erfolgsrate im Docking erhöhte sich von 71 %, ohne Einbeziehung von CSP, auf 100 %, wenn CSP mit höchster Gewichtung mit einbezogen wurden. Die globale Optimierung auf der kombinierten Docking-Energiehyperfläche ist also erfolgreich. In einem zweiten Schritt wurde QCSPScore zum Docking dreier Testfälle verwendet, für die als Referenz experimentelle CSP zur Verfügung standen. Ohne CSP, d.h. bei der Verwendung von herkömmlichen DrugScore-Potentialen, konnte keiner der drei Testfälle erfolgreich gedockt werden. Die Einbeziehung von CSP mit dem selben hohen Gewichtungsfaktor wie oben führte in allen drei Fällen zu erfolgreichen Docking-Ergebnissen. Für zwei der drei Komplexe wurden zudem nur bei Einbeziehung der experimentellen Information nativ-ähnliche Geometrien vorhergesagt. Konformationelle Änderungen der Bindetasche bis zu 2 Å RMSD beeinträchtigen den Erfolg des Dockings nicht. Ich bin davon überzeugt, dass mein Verfahren besonders für Protein-Ligand-Komplexe interessant sein wird, für die die Vorhersage nativ-ähnlicher Komplexe bislang schwierig war. Das sind insbesondere solche Fälle, in denen die Form der Bindetasche zur Vorhersage des Komplexes nicht ausreichend, wie das bei flachen Protein-Protein-Wechselwirkungsregionen oder beim virtuellen Screening kleiner Fragmente der Fall ist

Estudio y cribado virtual de compuestos químicos antivirales (VIH). Estudio de la modulación alostérica de agonistas y antagonistas del receptor celular CXCR4

Els mètodes de descobriment de nous fàrmacs han evolucionat recentment gràcies a la resolució de les estructures proteiques que actuen com a dianes terapèutiques responsables de malalties o desregulacions biològiques. Aquestes estructures proteiques tridimensionals, juntament amb el desenvolupament de noves tècniques computacionals permeten el desenvolupament accelerat de nous compostos candidats a esdevenir fàrmacs. El present treball s’inicia proposant un nou mètode que millora l’elecció de compostos candidats a ser inhibidors d’una “diana difícil”, però ben coneguda com és el receptor VEGFR-2, a partir de la seva estructura tridimensional cristal·litzada, així com de compostos inhibidors coneguts de l’esmentada diana. La resolució tridimensional de l’estructura CXCR4 mitjançant cristal•lografia de raigs X a l’any 2010, ha esdevingut un avenç important a l’hora de millorar el disseny de compostos inhibidors del VIH, així com compostos antitumorals, malalties en les que intervé de forma determinant el receptor CXCR4. Així doncs, els models de cribratge virtual desenvolupats abans del 2010 dins el laboratori de disseny molecular de l’IQS (GEM) han estat generats a partir de models creats per homologia vers a altres proteïnes GPCRs i/o s’han basat solament en la forma de lligands coneguts. D’aquesta forma, a partir de les diferents estructures proteiques publicades de CXCR4, s’ha avaluat quina d’aquestes estructures presenta la conformació que distingeix millor els compostos antagonistes actius dels compostos inactius. A més, s’han avaluat múltiples mètodes de cribratge virtual de CXCR4 basats en l’estructura, en la forma del lligand i mitjançant models farmacofòrics. Una vegada obtinguda la millor estructura de CXCR4 i els millors mètodes de cribratge virtual retrospectiu, es realitzen cribratges virtuals prospectius d’una nova quimioteca generada de forma combinatòria, basada en estructures anàlogues prèviament desenvolupades al laboratori de disseny molecular de l’IQS. Addicionalment, s’ha estudiat el comportament al·lostèric del receptor CXCR4 davant de moduladors antagonistes petits i moduladors al·lostèrics agonistes de naturalesa pèptica. CXCR4 és qualificada com a una “diana difícil” per la gran mida del seu lloc actiu ortostèric, així com per l’ampli número de funcions reguladores en les que intervé el receptor. Per això la modulació al·lostèrica en CXCR4 s’ha estudiat utilitzant diferents aproximacions com són el docking cec, docking proteïna-proteïna, docking per subllocs d’unió i dinàmica molecular.Los métodos de descubrimiento de nuevos fármacos han evolucionado recientemente gracias a la resolución de las estructuras proteicas las cuales actúan como dianas terapéuticas responsables de enfermedades o desregulaciones biológicas. Estas estructuras proteicas tridimensionales, conjuntamente con el desarrollo de nuevas técnicas computacionales están permitiendo el desarrollo acelerado de nuevos compuestos candidatos a convertirse en fármacos. El presente trabajo se inicia proponiendo un nuevo método que permita mejorar la elección de compuestos candidatos a ser inhibidores de una “diana difícil” aunque bien conocida, como es el receptor VEGFR-2, partiendo de su estructura tridimensional cristalizada y de compuestos inhibidores conocidos de dicha diana. La resolución tridimensional de la estructura del receptor CXCR4 mediante cristalografía de rayos X, en el año 2010, ha supuesto un avance importante de cara a mejorar el diseño de compuestos inhibidores del VIH, así como de compuestos antitumorales, enfermedades en las que interviene de forma determinante el receptor CXCR4. Así pues, los modelos de cribado virtual desarrollados anteriormente al 2010 en el laboratorio de diseño molecular del IQS (GEM) han sido generados a partir de modelos creados por homología a otras proteínas GPCRs y/o basados únicamente en la forma de ligandos conocidos. De este modo, partiendo de las diferentes estructuras proteicas publicadas de CXCR4, se ha evaluado cuál de dichas estructuras presenta la conformación que distingue mejor los compuestos antagonistas activos de compuestos inactivos. Además, se han evaluado múltiples métodos de cribado virtual de CXCR4 basados en la estructura, en la forma del ligando y mediante modelos farmacofóricos. Una vez obtenida la mejor estructura de CXCR4 y los mejores métodos de cribado virtual retrospectivo, se realizan cribados virtuales prospectivos de una nueva quimioteca generada combinatoriamente, basada en análogos de estructuras previamente desarrolladas en el laboratorio de diseño molecular del IQS. Adicionalmente se ha estudiado el comportamiento alostérico del receptor CXCR4 frente a moduladores antagonistas de pequeño tamaño y moduladores alostéricos agonistas de naturaleza peptídica. CXCR4 se califica como “diana difícil” debido al gran tamaño del sitio activo ortostérico, juntamente con el amplio número de funciones reguladoras en las que interviene el receptor CXCR4. Por ello la modulación alostérica en CXCR4 se ha estudiado utilizando diferentes aproximaciones, como son: docking ciego, docking proteína-proteína, docking por subsitios y dinámica molecular.: Drug discovery methods have recently emerged thanks to the resolution of protein structures which act as therapeutic targets responsible for diseases or biological deregulations. These three dimensional structures in combination with the development of new computational techniques are accelerating the development of new candidates to become drug compounds. This work starts with the proposal of a new method that improves the selection of candidates to become inhibitors of a well-known “difficult target” such us VEGFR-2 receptor. This method is based on the crystal structure of the receptor and also by a number of inhibitors known for this target. CXCR4 crystal structure was solved in 2010 by X-ray crystallography and this has been an important event in order to improve the molecular design of HIV inhibitors, as well as anticancer compounds, diseases where CXCR4 receptor is involved. Therefore, virtual screening models developed in the laboratory of molecular design of IQS (GEM) were generated using homology models from other GPCRs and/or based on ligand shape techniques. In this sense, taking into consideration all published CXCR4 crystal structures, it has been evaluated which of them shows the most suitable conformation to distinguish antagonists actives from inactives. Moreover, different virtual screening methods have also been evaluated such us structure based methods, ligand based methods and pharmacophoric models. Once obtained the most suitable structure and the best retrospective virtual screening methods, a prospective virtual screening has been carried out using a new combinatorial library of chemical structures. This new library is based on analogous structures previously generated in the laboratory of molecular design of IQS (GEM). In addition, the allosteric behaviour of CXCR4 receptor has been studied versus small antagonist modulators and versus peptidomimetic agonist modulators. CXCR4 is classified as a “difficult target” due to the large size of its extracellular pocket that the orthosteric binding site is placed as well as the diverse number of biochemical regulations where the receptor mediates. Thus, the allosteric modulation of CXCR4 has been studied using different approaches such as blind docking, protein-protein docking, docking by subsites and molecular dynamics

Integrative omics approaches for new target identification and therapeutics development

The growing research and commercial pressures for novel therapeutics development accentuate why better strategies are needed for drug discovery. The costly nature of developing a pharmaceutical compound as well as the shrinking pool of ‘easy’ targets are some of the key reasons why there is a research paradigm shift towards integrative and systems biology driven approaches. Moreover, multifactorial aspects of many diseases require more innovative clinical strategies rather than just focusing on a single target. Cardiovascular diseases as well as associated immune components exemplify this complexity well. This thesis aimed to introduce a gradual and highly integrative analytical framework by incorporating a full range of studies from disease target selection to high-throughput virtual screening so that a cost-effective and efficient stratification of targets and associated compounds could be achieved. Heart failure served as a case study for complex diseases where the first in-depth omics study on cardiomyopathies helped to elucidate new therapeutic avenues. This research tied in with a development of a novel scoring function and integrated machine learning approach for multiple therapeutic target classification and exploration. Finally, all pieces of the introduced research were used to create a highly integrative in silico screening workflow. Some of the key results included the first reported molecular dynamics analyses for a complex immunotherapeutic target, c-Rel, as well as 15 new therapeutic compounds that could potentially modulate this transcription factor subunit. Thus, this dissertation provided several important improvements for target identification, validation, and drug discovery that could significantly advance current development strategies and accelerate new therapeutics production

Understanding Pre-mRNA Dynamics in Single Spliceosome Complexes.

The spliceosome is a large RNA-protein complex that catalyzes pre-mRNA splicing by removing sequences called introns and joining of the remaining sequences, called exons, to produce a mature mRNA. Despite over a quarter century of research on splicing, little is known about the compositional and conformational rearrangements, timing, and coordination of this process. Particularly, the pre-mRNA, which provides the template for spliceosome assembly and the reactive sites for splicing chemistry, has been largely ignored. This thesis utilizes single molecule fluorescence resonance energy transfer (smFRET) approaches on a short budding yeast pre-mRNA to address this challenge. Specifically, we have placed fluorescent dyes near the conserved splice sites that are recognized by the spliceosome and have monitored dynamic changes in the distance between these sites, in real-time throughout the splicing cycle. We find that, contrary to conventional depictions, the splice sites are highly dynamic and explore reversible transitions. By stalling the progress of splicing at various steps, we have been able to associate unique dynamic information of the splice sites with specific steps of splicing using novel analysis methods that have broad applicability. We find that the splice sites explore reversible splice site proximity in a non-monotonic fashion throughout the process. Our results show that even at very early steps of splicing assembly, the splice sites are brought close together via a novel ATP-independent role of a helicase. Furthermore, employing a combination of smFRET and affinity purification (termed SiMPull-FRET), we have been able to describe the conformational dynamics of single isolated spliceosomes and find them to follow a biased Brownian ratcheting mechanism leading up to the first chemical step of splicing. Our results hint at the possibility that, much like the ribosome, the spliceosome and its substrates often toggle between active and inactive conformations that are subsequently locked into the preferred state by a specific cofactor. The work presented in this thesis provides a structural and dynamic view of the pre-mRNA in the spliceosome, finds associated roles for protein factors, and pioneers single molecule techniques to answer focused questions about the mechanisms of RNA:protein complex assembly and catalysis in general.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102364/1/ramyak_1.pd

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research