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Progettazione razionale attraverso metodi computazionali di ligandi multi-target come potenziali farmaci
No full textI metodi computazionali giocano un ruolo sempre più importante nella progettazione del farmaco. Lo screening virtuale (SV) è uno strumento potente per l'identificazione di composti attivi. Durante il dottorato ho studiato ed applicato metodi computazionali volti alla scoperta di potenziali farmaci. In particolare ho lavorato nel campo emergente della polifarmacologia. Molti farmaci esercitano il loro effetto terapeutico interagendo con bersagli multipli (es. molti farmaci usati nella terapia del cancro e delle patologie del sistema nervoso centrale). La capacità di progettare piccole molecole con un profilo di attività predefinito è un obiettivo attraente ed impegnativo. Ho lavorato alla messa a punto di un protocollo computazionale di polifarmacologia su Hsp90, un bersaglio rilevante per la terapia del cancro. Hsp90 è un chaperone molecolare con un vasto interattoma di cui fanno parte diversi bersagli validati per la terapia del cancro. L'obiettivo principale è stato la selezione di combinazioni di bersagli adatte per la progettazione di inibitori duali e di molecole candidate per la duplice attività. Le informazioni depositate in banche dati molecolari è stata ampiamente analizzata per la determinazione di combinazioni promettenti di bersagli costituite da Hsp90 ed un interattore. Lo SV basato sui ligandi, comprendente calcoli di ricerca per similarità e macchine a vettori di supporto, condotto sulle banche dati ChEMBL e ZINC è stato effettuato nel corso di un periodo che ho trascorso nel laboratorio del Prof. Jürgen Bajorath a Bonn. Sono state costituite una libreria di composti ChEMBL ed una di composti ZINC per ciascuna combinazione di bersagli. Successivamente uno SV basato sulla struttura dei bersagli è stato effettuato utilizzando programmi di docking (AutoDock4, Glide) e processamento post-docking (BEAR). Cinque composti ChEMBL e sette composti ZINC sono stati selezionati per i saggi biologici. Due composti attivi sono stati identificati nella libreria ZINC Hsp90-B-Raf. Un nuovo SV è stato effettuato per espandere le molecole attive e selezionare nuovi candidati. Ventinove composti sono stati infine selezionati. I risultati ottenuti sono riportati e discussi in questa tesi. Ho valutato l'impatto del processamento post-docking nell'identificazione di antagonisti noti di recettori accoppiati a proteina-G (GPCR) in un insieme più ampio di decoy molecolari. Le GPCR sono bersagli rilevanti e recenti progressi nella cristallografia stanno aprendo nuove prospettive per la progettazione di farmaci. Nonostante il loro successo è noto che i programmi di docking possono risultare inaccurati nella valutazione dell'energia di legame. Ho confrontato le prestazioni di AutoDock4 con un metodo di processamento post-docking (BEAR). Sono state studiate quattro GPCR: il recettore adrenergico beta-2, il recettore per l'adenosina A2a, il recettore della dopamina D3 ed il recettore dell'istamina H1. I risultati ottenuti hanno confermato AutoDock4 come strumento utile per la previsione di modalità di legame. D'altra parte l'applicazione di una funzione di scoring più accurata come MM-PBSA può migliorare il fattore di arricchimento dello SV. E' stata presentata inoltre una prospettiva sulla progettazione di molecole con bersagli multpli osservando il successo del metodo studiato nel classificare in posizioni elevate gli antagonisti condivisi da due differenti GPCR nei rispettivi SV. Lo stesso metodo di SV è stato applicato nella ricerca di inibitori allosterici della chinasi ciclina-dipendenti 2 (CDK2). CDK2 è coinvolta nel controllo del ciclo cellulare ed è un bersaglio validato nella terapia del cancro. Una libreria commerciale di 600.000 composti e stata processata tramite docking e post-docking. Trentacinque composti sono stati scelti per i saggi biologici e 7 di questi sono risultati attivi a livello micromolare in saggi su proteina e linee cellulari modello.Nowadays, computational methods are playing an increasingly important role in drug discovery. As a matter of fact, virtual screening (VS) is a powerful tool for the identification of novel hit compounds. During my doctorate thesis, I studied and applied computational methods aiming at the discovery of potential drugs. In particular, I worked in the emerging field of polypharmacology. Many drugs elicit their therapeutic effect through the simultaneous modulation of multiple targets (e.g. cancer and central nervous system drugs). The ability to rationally design small molecules with a predefined multi-target activity profile is a highly attractive and challenging task. As a major part of my thesis, I worked on the set up of a computational polypharmacology VS protocol targeting Hsp90, a relevant target for cancer therapy. Hsp90 is a molecular chaperone interacting with many validated targets for cancer therapy. The main goal of my work was the identification of suitable target combination for dual-target inhibitor design and the identification of candidate compounds for dual activity. The information deposited in molecular databases regarding single and multi-target known inhibitors of Hsp90 and its interactome was extensively analysed. A set of promising target combinations made up by Hsp90 and an interactor was identified. The ligand-based VS part of the work including similarity search and support vector machines calculations on ChEMBL and ZINC databases was carried out during a six months period that I spent in the laboratory of Prof. Jürgen Bajorath in Bonn. Then, analysing the rankings, a ChEMBL and a ZINC compound library was built for each target combination. After the ligand-based work, extensive structure-based VS was subsequently carried out by using docking (AutoDock4, Glide) and post-docking processing (BEAR). Five ChEMBL and seven ZINC compounds were selected for biological assays. Two active compounds were identified in the Hsp90-B-Raf ZINC library. Following the results obtained a new ligand- and structure-based VS round was carried-out aiming at hit expansion and selection of new candidates. Twenty-nine compounds were finally selected for biological assays. The results obtained in this VS campaign are here reported and discussed.
As for VS methodologies, I assessed the performance of post-docking processing in the identification of G-protein coupled receptor (GPCR) known antagonists among a larger set of molecular decoys. GPCRs are relevant targets and recent advancements in crystallography are opening new perspectives for structure-based drug design. Despite their success, it is a known fact that docking programs may prove inaccurate when comes to binding energy evaluation. To address this issue, I compared the performance of AutoDock4 with a post-docking processing method developed in the laboratory (BEAR). Four GPCRs were studied: beta2 adrenergic, adenosine A2a, dopamine D3 and histamine H1 receptors. The results obtained confirmed AutoDock4 as a useful tool for the correct prediction of binding modes. On the other hand, the application of a more accurate scoring function such as MM-PBSA can improve the VS enrichment factor. Furthermore, a perspective on multi-target drug design was put forward by successfully checking the ability of the studied method to rank at high positions antagonists shared by two different GPCR in the respective VS. The same VS method was also applied to perform a VS campaign aiming at the identification of cyclin-dependent kinase 2 allosteric inhibitors (CDK2). CDK2 is involved in the control of the cell cycle and is a validated target in cancer. Docking and post-docking with BEAR of a commercial library of 600,000 compounds was carried out. After visually inspecting the best scoring complexes, 35 candidates were selected for biological assays and 7 of them proved to be active in the micromolar range toward both isolated protein and breast cancer cell lines
Enrichment Factor Analyses on G-Protein Coupled Receptors with Known Crystal Structure
No full textG-protein coupled receptors (GPCRs) are highly relevant drug targets. Four GPCRs with known crystal structure were analyzed with docking (AutoDock4) and postdocking (MM-PBSA) in order to evaluate the ability to recognize known antagonists from a larger database of molecular decoys and to predict correct binding modes. Moreover, implications on multitarget drug screening are put forward. The results suggest that these methods may be of interest to the growing field of GPCR structure-based virtual screening
BEAR, a Molecular Docking Refinement and Rescoring Method
No full textBEAR (Binding Estimation After Refinement) is a computational method for structure-based virtual screening. It was set up as a post-docking processing tool for the refinement of ligand binding modes predicted by molecular docking programs and the accurate evaluation of free energies of binding. BEAR has been validated in a number of computational drug discovery applications. It performed well in discriminating active ligands with respect to molecular decoys of biological targets belonging to different protein families as well as in discovering biologically active hits. Recently, it has been validated also in the emerging field of G-protein coupled receptors structure based virtual screening
Targeting the Hsp90 interactome using in silico polypharmacology approaches
No full textIn recent years, polypharmacology has gained popularity in drug discovery. [1] Especially for complex diseases such as cancer, the ability of a drug to bind to and interfere with multiple targets provides new opportunities for therapeutic intervention In this article, we focus on Hsp90 and its interactome, whose pivotal role in survival and proliferation of cancer cells renders this array of targets particularly attractive polypharmacological drug design strategies.
The primary goal of our work is the identification and selection of suitable target proteins from the interactome that might be combined with Hsp90 to explore and exploit a multi-target inhibition approach. This task is accomplished by applying computational methods to mine the structural and biological information associated with potential ligands in public databases and assess the degree of structural similarity between known inhibitors of different targets. Therefore, we propose an integrated ligand- and structure-based approach to select small molecules from databases suitable for consideration as multi-target inhibitors
Structure-based discovery of the first allosteric inhibitors of cyclin-dependent kinase 2
No full textAllosteric targeting of protein kinases via displacement of the structural αC helix with type III allosteric inhibitors is currently gaining a foothold in drug discovery. Recently, the first crystal structure of CDK2 with an open allosteric pocket adjacent to the αC helix has been described, prospecting new opportunities to design more selective inhibitors, but the structure has not yet been exploited for the structure-based design of type III allosteric inhibitors. In this work we report the results of a virtual screening campaign that resulted in the discovery of the first-in-class type III allosteric ligands of CDK2. Using a combination of docking and post-docking analyses made with our tool BEAR, 7 allosteric ligands (hit rate of 20%) with micromolar affinity for CDK2 were identified, some of them inhibiting the growth of breast cancer cell lines in the micromolar range. Competition experiments performed in the presence of the ATP-competitive inhibitor staurosporine confirmed that the 7 ligands are truly allosteric, in agreement with their design. Of these, compound 2 bound CDK2 with an EC50 value of 3 μM and inhibited the proliferation of MDA-MB231 and ZR-75-1 breast cancer cells with IC50 values of approximately 20 μM, while compound 4 had an EC50 value of 71 μM and IC50 values around 4 μM. Remarkably, the most potent compound 4 was able to selectively inhibit CDK2-mediated Retinoblastoma phosphorylation, confirming that its mechanism of action is fully compatible with a selective inhibition of CDK2 phosphorylation in cells. Finally, hit expansion through analog search of the most potent inhibitor 4 revealed an additional ligand 4g with similar in vitro potency on breast cancer cells
Polypharmacology: challenges and opportunities in drug discovery
No full textAt present, the legendary magic bullet, i.e., a drug with high potency and selectivity toward a specific biological target, shares the spotlight with an emerging and alternative polypharmacology approach. Polypharmacology suggests that more effective drugs can be developed by specifically modulating multiple targets. It is generally thought that complex diseases such as cancer and central nervous system diseases may require complex therapeutic approaches. In this respect, a drug that "hits" multiple sensitive nodes belonging to a network of interacting targets offers the potential for higher efficacy and may limit drawbacks generally arising from the use of a single-target drug or a combination of multiple drugs. In this review, we will compare advantages and disadvantages of multitarget versus combination therapies, discuss potential drug promiscuity arising from off-target effects, comment on drug repurposing, and introduce approaches to the computational design of multitarget drugs
Identification of 4-aryl-1H-pyrrole[2,3-b]pyridine derivatives for the development of new B-Raf inhibitors
No full textDuring the last years, a significant interest in the identification of new classes of B-Raf inhibitors has emerged. In this study, which was conceived within an effort that culminated in the recent report of the first dual inhibitors of B-Raf and Hsp90, we describe the identification of four compounds based on 4-aryl-1H-pyrrole[2,3-b]pyridine scaffold as interesting starting points for the development of new B-Raf inhibitors. Structure-activity relationships and predicted binding modes are discussed. Moreover, the novelty of the newly identified structures with respect to currently known B-Raf inhibitors was assessed through a ligand-based dissimilarity assessment. Finally, structural modifications with the potential ability to improve the activity toward B-Raf are put forward. This article is protected by copyright. All rights reserved
Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations
No full textIn the last decades, molecular docking has emerged as an increasingly useful tool in the modern drug discovery process, but it still needs to overcome many hurdles and limitations such as how to account for protein flexibility and poor scoring function performance. For this reason, it has been recognized that in many cases docking results need to be post-processed to achieve a significant agreement with experimental activities. In this study, we have evaluated the performance of MM-PBSA and MM-GBSA scoring functions, implemented in our post-docking procedure BEAR, in rescoring docking solutions. For the first time, the performance of this post-docking procedure has been evaluated on six different biological targets (namely estrogen receptor, thymidine kinase, factor Xa, adenosine deaminase, aldose reductase, and enoyl ACP reductase) by using i) both a single and a multiple protein conformation approach, and ii) two different software, namely AutoDock and LibDock. The assessment has been based on two of the most important criteria for the evaluation of docking methods, i.e., the ability of known ligands to enrich the top positions of a ranked database with respect to molecular decoys, and the consistency of the docking poses with crystallographic binding modes. We found that, in many cases, MM-PBSA and MM-GBSA are able to yield higher enrichment factors compared to those obtained with the docking scoring functions alone. However, for only a minority of the cases, the enrichment factors obtained by using multiple protein conformations were higher than those obtained by using only one protein conformation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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