Advances in Computational Techniques to Study GPCR-Ligand Recognition

G-protein-coupled receptors (GPCRs) are among the most intensely investigated drug targets. The recent revolutions in protein engineering and molecular modeling algorithms have overturned the research paradigm in the GPCR field. While the numerous ligand-bound X-ray structures determined have provided invaluable insights into GPCR structure and function, the development of algorithms exploiting graphics processing units (GPUs) has made the simulation of GPCRs in explicit lipid-water environments feasible within reasonable computation times. In this review we present a survey of the recent advances in structure-based drug design approaches with a particular emphasis on the elucidation of the ligand recognition process in class A GPCRs by means of membrane molecular dynamics (MD) simulations

I recettori acccoppiati alle proteine G come potenziali bersagli terapeutici: Investigazione sulla topologia recettoriale e sul riconoscimento ligando-recettore: sfruttando il potere del Processore Grafico.

G protein-coupled receptors(GPCRs) constitute a large family of seven domain spanning membrane proteins that mediates a wide variety of cellular processes. Adenosine Receptotors (ARs) are part of this family and are widely distributed through the human body. ARs are involved in the regulation of several physiological processes and their modulation can have potential therapeutic applications for chronic diseases such as Parkinson’s and Alzheimer’s and for acute conditions such as stroke, cerebral ischemia and cardiac hypoxia. From a computational point of view numerous efforts have been put in place to characterize drug candidates targeting GPCRs. Moreover, the structural information available to the scientific community has assisted to an exponential growth since the determination of the rhodopsin crystal structure. Adrenergic, dopaminergic, histaminergic, opioid and A2A denosine receptors can provide detailed three-dimensional information useful for supporting structure based drug design approach. We created the first integrated bioinformatics and chemoinformatics web-resource dedicated to Adenosine receptors that is accessible to all the scientific community. It contains an evolutionary driven visualization tool of all Adenosine Receptor models. Adenosiland provides template suggestion in order to get the highest quality receptor model for molecular docking studies and membrane embedded optimized models for biophysical investigation on receptor plasticity. With particular regards to A2A Adenosine Receptor, detailed structural investigation on the dynamic solvation process has been made using state of the art tecnology such as GPU accelerated Molecular Dynamics. Focusing on methodological advances, we report a novel approach consisting in the integration of molecular docking and membrane MD simulations anticipate the bioactive pose of a ligand within the receptor crystallographic structure. Eventually we developed a computational method that enable complete ligand-receptor recognition pathway investigations in a low nanosecond (ns) time scale. We called this new method Supervised Molecular Dynamics (SuMD). The present research work introduced promising methodological development that can have potential development and implementation on molecular modeling programs that are widely used in both industry and academia.I recettori accoppiati a proteine G costituiscono una grande famiglia di recettori, a sette eliche transmembrana, che media una grande varietà di processi cellulari. I recettori Adenosinici sono parte di questa famiglia e sono distribuiti nella maggior parte dei tessuti del corpo umano. Essi risultano coinvolti nella regolazione di svariati processi fisiologici. La modulazione dei recettori adenosinici, perciò, può avere potenziali applicazioni terapeutiche per malattie croniche, come il morbo di Parkinson ed Alzheimer, ed acute come infarto, ischemia cerebrale e ipossia cardiaca. Dal punto di vista della chimica computazionale, molti sforzi sono stati compiuti per la caratterizzazione di nuovi candidati farmaci specifici per i recettori accoppiati a proteine G. Inoltre, le informazioni strutturali disponibili hanno assistito ad una crescita esponenziale dalla determinazione della struttura cristallografica della Rodopsina. Recettori adrenergici, dopaminergici, istaminergici, oppioidi e recettori adenosinici, del sottotipo A2A , forniscono informazioni dettagliate per lo sviluppo di approcci di drug-design razionale che sfruttano informazioni riguardanti la struttura molecolare del bersaglio proteico. Abbiamo creato la prima piattaforma web bioinformatica e chemoinformatica integrata dedicata ai recettori adenosinici. Detta piattaforma è a completa disposizione della comunità scientifica e contiene strumenti per la visualizzazione, di tutti i modelli ad oggi clonati, basata su scala evolutiva. Adenosiland fornisce suggerimenti per la selezione del migliore templato, utile alla costruzione di modelli per omologia, allo scopo di compiere studi di docking molecolare. Fornisce inoltre modelli inseriti in un sistema di membrana per investigazioni di natura biofisica sulla plasticità recettoriale. In riferimento al recettore adenosinico A2A, una dettagliata investigazione sul processo di solvatazione dinamico è stata svolta utilizzando studi di dinamica molecolare basati su Processore Grafico (GPU). Inoltre una particolare attenzione è stata posta sull’avanzamento metodologico in chimica computazionale. Riportiamo lo sviluppo di un nuovo approccio che consiste nell’integrazione tra il docking e dinamica molecolare in grado di anticipare la conformazione bioattiva da un vasto insieme di possibili conformazioni di legame nel sito di legame ortosterico del recettore adenosinico umano A2A . Infine è stata sviluppata una nuova metodologia computazionale, chiamata Supervised MD (SuMD), che permette l’investigazione del processo di riconoscimento ligando recettore in una scala dei tempi ridotta, nell’ordine dei nanosecondi (ns). Il lavoro di tesi, qui introdotto, riporta promettenti sviluppi metodologici che possono avere una potenziale implementazione in programmi di modellistica molecolare ampiamente usati in ambiente accademico ed industriale

I recettori acccoppiati alle proteine G come potenziali bersagli terapeutici: Investigazione sulla topologia recettoriale e sul riconoscimento ligando-recettore: sfruttando il potere del Processore Grafico.

G protein-coupled receptors(GPCRs) constitute a large family of seven domain spanning membrane proteins that mediates a wide variety of cellular processes. Adenosine Receptotors (ARs) are part of this family and are widely distributed through the human body. ARs are involved in the regulation of several physiological processes and their modulation can have potential therapeutic applications for chronic diseases such as Parkinson’s and Alzheimer’s and for acute conditions such as stroke, cerebral ischemia and cardiac hypoxia. From a computational point of view numerous efforts have been put in place to characterize drug candidates targeting GPCRs. Moreover, the structural information available to the scientific community has assisted to an exponential growth since the determination of the rhodopsin crystal structure. Adrenergic, dopaminergic, histaminergic, opioid and A2A denosine receptors can provide detailed three-dimensional information useful for supporting structure based drug design approach. We created the first integrated bioinformatics and chemoinformatics web-resource dedicated to Adenosine receptors that is accessible to all the scientific community. It contains an evolutionary driven visualization tool of all Adenosine Receptor models. Adenosiland provides template suggestion in order to get the highest quality receptor model for molecular docking studies and membrane embedded optimized models for biophysical investigation on receptor plasticity. With particular regards to A2A Adenosine Receptor, detailed structural investigation on the dynamic solvation process has been made using state of the art tecnology such as GPU accelerated Molecular Dynamics. Focusing on methodological advances, we report a novel approach consisting in the integration of molecular docking and membrane MD simulations anticipate the bioactive pose of a ligand within the receptor crystallographic structure. Eventually we developed a computational method that enable complete ligand-receptor recognition pathway investigations in a low nanosecond (ns) time scale. We called this new method Supervised Molecular Dynamics (SuMD). The present research work introduced promising methodological development that can have potential development and implementation on molecular modeling programs that are widely used in both industry and academia

Analysis of a stereo visual SLAM system in a virtual and real environment

openLa localizzazione e mappatura simultanea (Simultaneous Localization And Mapping, SLAM) è un sistema che permette ad un robot, o generico dispositivo mobile, di costruire una mappa dell’ambiente circostante e allo stesso tempo di definire la propria posa (posizione e rotazione), senza informazioni a priori. In particolare, quando questo processo viene realizzato solamente mediante l’uso di telecamere, esso prende il nome di Visual-SLAM. In questa tesi viene implementato il sistema Visual-SLAM ORB-SLAM2 con lo scopo ultimo di dimostrare la validità di impiego di simulazioni virtuali come ambiente di testing. Possono essere individuati due blocchi costituitivi nell’elaborato. La prima parte si concentra prima sull’introdurre il problema SLAM, dalla sua formulazione probabilistica alle caratteristiche generali di un metodo basato su sistemi di visione, per poi spostarsi ad una descrizione dettagliata della implementazione utilizzata del sistema ORB-SLAM2. L’intenzione è di fornire una completa e approfondita mappa dell’algoritmo: dalla elaborazione delle immagini in input fino al riconoscimento della chiusura del loop e successiva ottimizzazione. La seconda parte illustra come è stato preparato il framework per effettuare le simulazioni e quali sono i risultati ottenuti. Una serie di test dimostra l’utilità di questo strumento per quantificare l’effetto di alcune condizioni tipiche a cui è soggetto un sistema Visual-SLAM, quali la presenza di oggetti dinamici, condizioni di illuminazione ed elevata velocità del moto delle telecamere. Viene poi presentato un confronto tra l’esito del test sostenuto utilizzando il dataset pubblico KITTI e l’esito dei test fatti negli ambienti virtuali.Simultaneous Localization And Mapping (SLAM) is a system that allows a robot, or generic mobile device, to construct a map of its surroundings and at the same time define its pose (position and rotation), without a priori information. In particular, when this process is performed solely through the use of cameras, it is called Visual-SLAM. In this thesis, the Visual-SLAM ORB-SLAM2 system is implemented with the ultimate goal of demonstrating the validity of employing virtual simulations as a testing environment. Two constituent blocks can be identified in the paper. The first part focuses first on the introduction of the SLAM problem, from its probabilistic formulation to the general characteristics of a method based on vision systems, and then moves on to a detailed description of the used implementation of the ORBSLAM2 system. The intention is to provide a complete and in-depth map of the algorithm: from input image processing to loop closure recognition and subsequent optimization. The second part shows how the framework was prepared to carry out the simulations and what results were obtained. A series of tests demonstrate the usefulness of this tool for quantifying the effect of some typical conditions to which a Visual-SLAM system is subjected, such as the presence of dynamic objects, adverse lighting conditions, and high speed of the camera motion. A comparison is then presented between the outcome of the test performed using the KITTI public dataset and the outcome of the tests done in the virtual environments

Molecular modelling studies on Arylthioindoles as potent inhibitors of tubulin polymerization

The crucial role played by microtubules in the life of eukaryotic cell makes tubulin an important route for the anticancer therapy. The Arylthioindoles (ATIs) along with the corresponding ketone and methylene compounds are potent tubulin assembly inhibitors. We are here reporting the result of a series of docking and molecular dynamics experiments on this series of compounds. The results obtained from our in silico studies not only provided us with an insight on the nature of the binding of the ATIs to tubulin, but were also at the core of the design of a new series of potent inhibitors of tubulin polymerization

Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment

Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment