Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace

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

A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context

In modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment.This research was funded by EC research project “SHERLOCK—Seamless and safe human-centered robotic applications for novel collaborative workplace”. Grant number: 820683 (https://www.sherlock-project.eu accessed on 12 March 2021)

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining-whenever possible-empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein-ligand complexes, which will be addressed in future versions of the pipeline. Keywords: D3R; automated pipeline; binding mode prediction; docking; pocket detection

Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma.

Dysregulation of the seven-transmembrane (7TM) receptor Smoothened (SMO) and other components of the Hedgehog (Hh) signaling pathway contributes to the development of cancers including basal cell carcinoma (BCC) and medulloblastoma (MB). However, SMO-specific antagonists produced mixed results in clinical trials, marked by limited efficacy and high rate of acquired resistance in tumors. Here we discovered that Nilotinib, an approved inhibitor of several kinases, possesses an anti-Hh activity, at clinically achievable concentrations, due to direct binding to SMO and inhibition of SMO signaling. Nilotinib was more efficacious than the SMO-specific antagonist Vismodegib in inhibiting growth of two Hh-dependent MB cell lines. It also reduced tumor growth in subcutaneous MB mouse xenograft model. These results indicate that in addition to its known activity against several tyrosine-kinase-mediated proliferative pathways, Nilotinib is a direct inhibitor of the Hh pathway. The newly discovered extension of Nilotinib's target profile holds promise for the treatment of Hh-dependent cancers

Enrichment of virtual screening results using induced-fit techniques

This thesis explains the design, development, test on the benchmarking dataset DUD-e and the application to an industrial virtual screening project of the PELE VS platform. The most common and quick tools used on virtual screening campaigns do not take into account the induced-fit effect, although there are some methodologies capable of reproducing this effect they are either time consuming or very limited on which transformations the protein may undergo. In this thesis with the development of the PELE VS platform we aim at using the simulation software PELE (Protein Energy Landscape Exploration) to account for the induced-fit effect. The PELE software uses a monte carlo algorithm coupled with an energy minimization step to explore the ligand conformations and model the protein. This approach allows the program to account for both big conformational changes and small local changes of the protein and to perform a good conformational search of the ligand, which coupled can account for the induced-fit effect with only a quick simulation. PELE has been traditionally, and successfully, used in the enzyme engineering field where only a few compounds per protein are usually tested and studied. In order to apply the program to the virtual screening field, where thousands of compounds are tested in silico, the first step was to automatize the whole procedure of preparing, launching and analyzing the simulations. Thus, during this thesis the PELE VS platform has been developed altogether with other auxiliary tools. Once the platform was developed, we wanted to test the behaviour of PELE on a well known benchmarking dataset, thus we tried our methodology on the DUD-e dataset. Since this dataset is formed by more than 100 proteins, we chose a few proteins for each of the families present in the dataset, reducing the number of proteins to 21 systems. Then, we tried to use a general protocol for all the chosen proteins in order to improve the results of currently used scoring functions in the field. After studying the simulations and trying several protocols on this subset we we observed that every protein (or at least family) that we want to study needs an specific simulation protocol in order to correctly reproduce the induced-fit effect and improve the results of the most used scoring function: glide from schrodinger. Finally we applied the platform and our previous hypothesis to an industrial virtual screening campaign, as part of the collaborative Retos project: Silicoderm. In this case we worked with only one protein and several compounds and we confirmed the need for a tailored simulation protocol for the receptor in order to improve results.Esta tesis explica el diseño, desarrollo, testeo en el dataset de referencia DUD-e y la aplicación en un proyecto industrial de cribado virtual de la plataforma PELE VS. Las herramientas más rápidas usadas en campañas de cribado virtual no tienen en cuenta el efecto de ajuste inducido, y aunque hay metodologías capaces de reproducir este efecto o bien requieren de mucho tiempo de computación o son muy limitadas en cuanto a qué transformaciones pueden realizar.sobre la proteina. El objetivo de esta tesis es que gracias al desarrollo de la plataforma PELE VS podamos aplicar el programa de simulación PELE (Protein Energy Landscape Exploration) para reproducir el efecto del ajuste inducido. El programa de simulación PELE utiliza una combinación de un algoritmo de monte y una minimización energética para modelar la proteína. Esta aproximación nos permite realizar tanto grandes cambios conformacionales como pequeños ajustes locales de la proteina, a la vez que nos proporciona un buen muestreo conformacional del ligando. Esta combinación nos permite reproducir el efecto del ajuste inducido con una rápida simulación. Tradicionalmente PELE ha sido usado con éxito en el campo de la ingeniería de enzimas donde solo unos pocos compuestos por proteina son estudiados y probados. Para poder aplicar el programa al campo del cribado virtual, donde miles de compuestos son testeados in silico, el primer paso ha realizar es la automatización de todo el proceso de preparación, lanzamiento y análisis de las simulaciones. Es por eso que durante el transcurso de esta tesis la plataforma PELE VS ha sido desarrollada, junto con otros programas auxiliares. Una vez desarrollada la plataforma, quisimos comprobar el compportamiento de PELE en un conocido datase the referencia, así que lo probamos en el dataset DUD-e. Dado que este dataset contiene más de 100 proteinas, seleccionamos solo unas pocas proteinas de cada una de las familias que forman el dataset, reduciendo el numero de proteínas a 21 receptors. A continuación, probamos un protocolo de simulación genérico sobre los receptores seleccionados con el objetivo de mejorar los resultados de funciones de puntuacións utilizadas actualmente en el campo. Después de estudiar las simulaciones y probar diferentes protocolos de simulaciones en el subset seleccionado, concluimos que cada proteina (o al menos familia de proteínas) que deseemos estudiar requiere de un protocolo de simulación específico para porder reproducir correctamente el efecto del ajuste inducido y así mejorar los resultados de la función de puntuación más usada:: glide de Schrodinger. Finalmente, aplicamos la plataformar y nuestra hipóstesis previa a una campaña industrial de cribado virtual, como parte del proyecto RETOS colaborativo: SilicoDerm. En este caso trabajamos solo son una proteína y diversos compuestos. Durante esta apliación pudimos confirmar la necesidad de un protocolo de simulación adaptado al receptor que se quiere estudiar, para así poder mejorar los resultados de las metodologías actuales.Postprint (published version

Enrichment of virtual screening results using induced-fit techniques

This thesis explains the design, development, test on the benchmarking dataset DUD-e and the application to an industrial virtual screening project of the PELE VS platform. The most common and quick tools used on virtual screening campaigns do not take into account the induced-fit effect, although there are some methodologies capable of reproducing this effect they are either time consuming or very limited on which transformations the protein may undergo. In this thesis with the development of the PELE VS platform we aim at using the simulation software PELE (Protein Energy Landscape Exploration) to account for the induced-fit effect. The PELE software uses a monte carlo algorithm coupled with an energy minimization step to explore the ligand conformations and model the protein. This approach allows the program to account for both big conformational changes and small local changes of the protein and to perform a good conformational search of the ligand, which coupled can account for the induced-fit effect with only a quick simulation. PELE has been traditionally, and successfully, used in the enzyme engineering field where only a few compounds per protein are usually tested and studied. In order to apply the program to the virtual screening field, where thousands of compounds are tested in silico, the first step was to automatize the whole procedure of preparing, launching and analyzing the simulations. Thus, during this thesis the PELE VS platform has been developed altogether with other auxiliary tools. Once the platform was developed, we wanted to test the behaviour of PELE on a well known benchmarking dataset, thus we tried our methodology on the DUD-e dataset. Since this dataset is formed by more than 100 proteins, we chose a few proteins for each of the families present in the dataset, reducing the number of proteins to 21 systems. Then, we tried to use a general protocol for all the chosen proteins in order to improve the results of currently used scoring functions in the field. After studying the simulations and trying several protocols on this subset we we observed that every protein (or at least family) that we want to study needs an specific simulation protocol in order to correctly reproduce the induced-fit effect and improve the results of the most used scoring function: glide from schrodinger. Finally we applied the platform and our previous hypothesis to an industrial virtual screening campaign, as part of the collaborative Retos project: Silicoderm. In this case we worked with only one protein and several compounds and we confirmed the need for a tailored simulation protocol for the receptor in order to improve results.Esta tesis explica el diseño, desarrollo, testeo en el dataset de referencia DUD-e y la aplicación en un proyecto industrial de cribado virtual de la plataforma PELE VS. Las herramientas más rápidas usadas en campañas de cribado virtual no tienen en cuenta el efecto de ajuste inducido, y aunque hay metodologías capaces de reproducir este efecto o bien requieren de mucho tiempo de computación o son muy limitadas en cuanto a qué transformaciones pueden realizar.sobre la proteina. El objetivo de esta tesis es que gracias al desarrollo de la plataforma PELE VS podamos aplicar el programa de simulación PELE (Protein Energy Landscape Exploration) para reproducir el efecto del ajuste inducido. El programa de simulación PELE utiliza una combinación de un algoritmo de monte y una minimización energética para modelar la proteína. Esta aproximación nos permite realizar tanto grandes cambios conformacionales como pequeños ajustes locales de la proteina, a la vez que nos proporciona un buen muestreo conformacional del ligando. Esta combinación nos permite reproducir el efecto del ajuste inducido con una rápida simulación. Tradicionalmente PELE ha sido usado con éxito en el campo de la ingeniería de enzimas donde solo unos pocos compuestos por proteina son estudiados y probados. Para poder aplicar el programa al campo del cribado virtual, donde miles de compuestos son testeados in silico, el primer paso ha realizar es la automatización de todo el proceso de preparación, lanzamiento y análisis de las simulaciones. Es por eso que durante el transcurso de esta tesis la plataforma PELE VS ha sido desarrollada, junto con otros programas auxiliares. Una vez desarrollada la plataforma, quisimos comprobar el compportamiento de PELE en un conocido datase the referencia, así que lo probamos en el dataset DUD-e. Dado que este dataset contiene más de 100 proteinas, seleccionamos solo unas pocas proteinas de cada una de las familias que forman el dataset, reduciendo el numero de proteínas a 21 receptors. A continuación, probamos un protocolo de simulación genérico sobre los receptores seleccionados con el objetivo de mejorar los resultados de funciones de puntuacións utilizadas actualmente en el campo. Después de estudiar las simulaciones y probar diferentes protocolos de simulaciones en el subset seleccionado, concluimos que cada proteina (o al menos familia de proteínas) que deseemos estudiar requiere de un protocolo de simulación específico para porder reproducir correctamente el efecto del ajuste inducido y así mejorar los resultados de la función de puntuación más usada:: glide de Schrodinger. Finalmente, aplicamos la plataformar y nuestra hipóstesis previa a una campaña industrial de cribado virtual, como parte del proyecto RETOS colaborativo: SilicoDerm. En este caso trabajamos solo son una proteína y diversos compuestos. Durante esta apliación pudimos confirmar la necesidad de un protocolo de simulación adaptado al receptor que se quiere estudiar, para así poder mejorar los resultados de las metodologías actuales