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71	Advances in Ligand Binding Predictions using Molecular Dynamics Simulations Keränen, Henrik January 2014 (has links) Biochemical processes all involve associations and dissociations of chemical entities. Understanding these is of substantial importance for many modern pharmaceutical applications. In this thesis, longstanding problems with regard to ligand binding are treated with computational methods, applied to proteins of key pharmaceutical importance. Homology modeling, docking, molecular dynamics simulations and free-energy calculations are used here for quantitative characterization of ligand binding to proteins. By combining computational tools, valuable contributions have been made for pharmaceutically relevant areas: a neglected tropical disease, an ion channel anti-drug-target, and GPCR drug-targets. We report three compounds inhibiting cruzain, the main cysteine protease of the protozoa causing Chagas’ disease. The compounds were found through an extensive virtual screening study and validated with experimental enzymatic assays. The compounds inhibit the enzyme in the μM-range and are therefore valuable in further lead optimization studies. A high-resolution crystal structure of the BRICHOS domain is reported, together with molecular dynamics simulations and hydrogen-deuterium exchange mass spectrometry studies. This work revealed a plausible mechanism for how the chaperone activity of the domain may operate. Rationalization of structure-activity relationships for a set of analogous blockers of the hERG potassium channel is given. A homology model of the ion channel was used for docking compounds and molecular dynamics simulations together with the linear interaction energy method employed for calculating the binding free-energies. The three-dimensional coordinates of two GPCRs, 5HT1B and 5HT2B, were derived from homology modeling and evaluated in the GPCR Dock 2013 assessment. Our models were in good correlation with the experimental structures and all of them placed among the top quarter of all models assessed. Finally, a computational method, based on molecular dynamics free-energy calculations, for performing alanine scanning was validated with the A2A adenosine receptor bound to either agonist or antagonist. The calculated binding free-energies were found to be in good agreement with experimental data and the method was subsequently extended to non-alanine mutations. With extensive experimental mutation data, this scheme is a valuable tool for quantitative understanding of ligand binding and can ultimately be used for structure-based drug design. free-energy perturbation molecular dynamics ligand binding free-energy perturbation linear interaction energy binding free-energy homology modeling virtual screening alanine scanning amino acid mutagenesis hERG GPCR adenosine receptor serotonin receptor BRICHOS cruzain
72	Υπολογιστική μελέτη δομής και δυναμικής βιομοριακών συμπλόκων της α1 υπομονάδας του νικοτινικού υποδοχέα της ακετυλοχολίνης (nAChR) με άλφα-νευροτοξίνες Δημητρόπουλος, Νικόλαος 15 February 2011 (has links) Οι νικοτινικοί υποδοχείς της ακετυλοχολίνης (nAChRs) ανήκουν στην υπερ-οικογένεια των ιοντικών καναλιών που ενεργοποιούνται από τη δέσμευση ενός προσδέτη (LGICs) και αποτελούνται από πέντε ομόλογες υπομονάδες. Κάθε μονομερής υπομονάδα αποτελείται από μία Ν-τελική εξωκυττάρια περιοχή (ΕΚΠ), από τέσσερεις διαμεμβρανικές α-έλικες και από μία κυτταροπλασματική περιοχή. Στην ΕΚΠ βρίσκεται η χαρακτηριστική Cys-θηλιά της υπερ-οικογένειας, καθώς και οι θέσεις πρόσδεσης αγωνιστών και ανταγωνιστών του υποδοχέα. Οι γνώσεις μας γύρω από τη δομή των nAChRs προέρχονται κυρίως από κρυσταλλογραφικές δομές ομολόγων πρωτεϊνών δέσμευσης της ACh (AChBP) μαλακίων, από μια δομή του nAChR από ιχθείς του γένους Torpedo που προέρχεται από ηλεκτρονική μικροσκοπία, από την κρυσταλλογραφική δομή της α1-ΕΚΠ ποντικού σε σύμπλοκο με α-μπουγκαροτοξίνη (α-Btx) και από κρυσταλλογραφικές δομές δύο προκαρυωτικών LGICs. Παρά τη μεγάλη πρόοδο που πραγματοποιήθηκε με τα παραπάνω επιτεύγματα, ακόμη δεν έχει επιλυθεί πειραματικά η δομή ανθρώπινου υποδοχέα. Επίσης λίγα είναι γνωστά για την επίδραση της γλυκοζυλίωσης των ΕΚΠ στη λειτουργία του nAChR. Χρησιμοποιώντας ως εκμαγείο την κρυσταλλογραφική δομή του συμπλόκου α1-ΕΚΠ ποντικού/α-Btx δημιουργήθηκαν υπολογιστικά μοντέλα της ανθρώπινης α1-ΕΚΠ προσδεμένης στις τοξίνες α-μπουγκαροτοξίνη (α-Btx), α-κομπρατοξίνη (α-Cbtx), α-κωνοτοξίνη (α-Ctx) ImI και α-κωνοτοξίνη GI. Στα σύμπλοκα με α-Btx και α-Cbtx προστέθηκε η υδατανθρακική αλυσίδα, συνδεδεμένη με το κατάλοιπο Asn141, που συγκρυσταλλώθηκε μαζί με την α1-ΕΚΠ ποντικού. Για να μελετηθεί η δυναμική συμπεριφορά της αλληλεπίδρασης υποδοχέα-τοξίνης καθώς και η συνεισφορά των σακχάρων σε αυτήν πραγματοποιήθηκαν προσομοιώσεις Μοριακής Δυναμικής σε υδατικό περιβάλλον. Με τη χρήση υπολογιστικών εργαλείων για τη μελέτη των συμπλόκων προσδιορίστηκαν σε ατομικό επίπεδο οι αλληλεπιδράσεις που καθοδηγούν την πρόσδεση τοξινών στην α1-ΕΚΠ. Βρέθηκε ότι η υδατανθρακική αλυσίδα συμμετέχει δυναμικά στη δέσμευση της τοξίνης στον υποδοχέα. Τα σάκχαρα συγκλίνουν προς την προσδεμένη τοξίνη στηριζόμενα στα κατάλοιπα Ser187 και Trp184 της α1 υπομονάδας. Η τοξίνη επίσης μετακινείται φέρνοντας τη θηλιά Ι σε επαφή με τα σάκχαρα. Αναγνωρίστηκαν σημαντικές αλληλεπιδράσεις των σακχάρων με τα τοξινικά κατάλοιπα Thr6, Ser9, και Th15 της α-Btx και Thr6 και Pro7 της α-Cbtx. Επίσης επιβεβαιώθηκε η ύπαρξη μιας υδρόφιλης κοιλότητας στο εσωτερικό του υδρόφοβου πυρήνα της α1-ΕΚΠ, η οποία πιθανόν εμπλέκεται στο άνοιγμα του ιοντικού καναλιού του nAChR. Τα αποτελέσματα αυτά παρέχουν σημαντικά δεδομένα για την κατανόηση της επίδρασης της υδατανθρακικής αλυσίδας στη λειτουργία του υποδοχέα, η οποία μπορεί να αξιοποιηθεί στην αντιμετώπιση των πολλών παθολογικών καταστάσεων στις οποίες εμπλέκονται οι nAChRs. / Nicotinic acetylcholine receptors (nAChRs) belong to the superfamily of ligand-gated ion channels (LGICs). LGICs form homo- or hetero-pentamers of related subunits, and each of them consists of a N-terminal extracellular ligand-binding domain (ECD), four transmembrane α-helixes and an intracellular region. The characteristic Cys-loop of the superfamily is found in the ECD of each subunit. The ECD also contains binding sites for agonists and competitive antagonists. Our knowledge regarding the nAChR structure mainly derives from the X-ray crystal structures of the molluscan ACh-binding proteins (AChBPs), the electron microscopy structure of the Torpedo nAChR, the X-ray crystal structure of the mouse nAChR α1-ECD bound to α-bungarotoxin (α-Btx), and the X-ray crystal structures of two prokaryotic LGICs. Despite the progress made by these achievements, the determination of any human nAChR structure has not yet been accomplished. Furthermore, the effect of glycosylation on nAChR function has not yet been explored. Based on the crystal structure of the extracellular domain of the mouse nAChR α1 subunit bound to α-Btx we have generated in silico models of the human nAChR α1-ECD bound to the toxins α-bungarotoxin (α-Btx), α-cobratoxin (α-Cbtx), α-conotoxin (α-Ctx) ImI and α-conotoxin GI. In the case of the α1-ECD/α-Btx and α-Cbtx complexes, a Asn141-linked carbohydrate chain was modeled, its coordinates taken from the crystal structure of the mouse α1-ECD. To gain further insight into the structural role of glycosylation molecular dynamics (MD) simulations were carried out in explicit solvent so as to compare the conformational dynamics of the binding interface between nAChR α1 and the two toxins. The use of computational methods allowed the monitoring of the interactions that govern toxin binding. The MD simulations revealed the strengthening of the receptor-toxin interaction in the presence of the carbohydrate chain. A shift in the position of the sugars towards the bound toxin was observed. Residues Ser187 and Trp184 of nAChR act as critical anchor points for the stabilization of the sugar chain in a close position to the toxin. Toxin Finger I shifts closer to the mannoses, forming important toxin-sugar interactions that implicate residues Thr6, Ser9, and Thr15 of α-Btx, as well as Thr6 and Pro7 of α-Cbtx. Additionally the MD simulations of the human α1 ECD–toxin complexes confirmed the possible accommodation of two water molecules into a hydration cavity inside the hydrophobic core of the subunit, which may contribute to the gating mechanism of the receptor. These findings provide additional structural data that are intended to inspire biophysical studies on the functional role of glycosylation in the gating mechanism of nAChR and also guide the development of novel therapeutic agents for the treatment of nAChR-associated diseases. Γλυκοζυλίωση Α-νευροτοξίνες 612.804 2 Nicotinic acetylcholine receptor nAChR Ligand-gated ion channels Glycosylation Alpha-neurotoxins Homology modeling Molecular dynamics simulations Docking simulations
73	Bioinformatický nástroj pro predikci struktury proteinů / Bioinformatics Tool for Protein Structure Prediction Plaga, Michal January 2016 (has links) The goal of this thesis is test and comparation of the offline tools for prediction of protein structure and creation of metaprediktor, which allows the user to select the appropriate tool, according to given parameters. Testing tool is based on a dataset of proteins, which is based on the SCOP database and it is trying to be as balanced as possible to include proteins from different families and thus could best evaluate individual tools. The results of this thesis are requirements of metaprediktor and also which data and settings can be allowed and processed and how it will be implemented.
74	Webový server pro predikci 3D struktury proteinu / Web Server for Protein Structure Prediction Votroubek, Lukáš January 2013 (has links) This work deals with proteins, especially with their structure and kinds of tertiary, or 3D, structure prediction. Tertiary structure prediction is very important for function prediction of this vitally important substance. Bioinformatics do this prediction much more effective and faster, because classical methods of structure prediction directly from molecule are very expensive and slow. On the other hand they are much more exact. Objective of this thesis is to describe tertiary structure prediction methods, describe used tools and possibility of automatic communication with them. Next objective is describe implementation of server, that will serve to protein engineers for more effective finding of information about tertiary structure from more servers without requesting each of them separately. Results of testing will be described in this work too.
75	Homology modeling and structural analysis of the antipsychotic drugs receptorome López Muñoz, Laura 22 June 2010 (has links) Classically it was assumed that the compounds with therapeutic effect exert their action interacting with a single receptor. Nowadays it is widely recognized that the pharmacological effect of most drugs is more complex and involves a set of receptors, some associated to their positive effects and some others to the side effects and toxicity. Antipsychotic drugs are an example of effective compounds characterized by a complex pharmacological profile binding to several receptors (mainly G protein-coupled-receptors, GPCR). In this work we will present a detailed study of known antipsychotic drugs and the receptors potentially involved in their binding profile, in order to understand the molecular mechanisms of the antipsychotic pharmacologic effects.The study started with obtaining homology models for all the receptors putatively involved in the antipsychotic drugs receptorome, suitable for building consistent drug-receptor complexes. These complexes were structurally analyzed and compared using multivariate statistical methods, which in turn allowed the identification of the relationship between the pharmacological properties of the antipsychotic drugs and the structural differences in the receptor targets. The results can be exploited for the design of safer and more effective antipsychotic drugs with an optimum binding profile. / Tradicionalmente se asumía que los fármacos terapéuticamente efectivos actuaban interaccionando con un único receptor. Actualmente está ampliamente reconocido que el efecto farmacológico de la mayoría de los fármacos es más complejo y abarca a un conjunto de receptores, algunos asociados a los efectos terapéuticos y otros a los secundarios y toxicidad. Los fármacos antipsicóticos son un ejemplo de compuestos eficaces que se caracterizan por unirse a varios receptores simultáneamente (principalmente a receptores unidos a proteína G, GPCR). El trabajo de la presente tesis se ha centrado en el estudio de los mecanismos moleculares que determinan el perfil de afinidad de unión por múltiples receptores de los fármacos antipsicóticos.En primer lugar se construyeron modelos de homología para todos los receptores potencialmente implicados en la actividad farmacológica de dichos fármacos, usando una metodología adecuada para construir complejos fármaco-receptor consistentes. La estructura de estos complejos fue analizada y se llevó a cabo una comparación mediante métodos estadísticos multivariantes, que permitió la identificación de asociaciones entre la actividad farmacológica de los fármacos antipsicóticos y diferencias estructurales de los receptores diana. Los resultados obtenidos tienen interés para ser explotados en el diseño de fármacos antipsicóticos con un perfil farmacológico óptimo, más seguros y eficaces. Receptor 5-HT2A Receptor D3 Receptor D2 Ligando Clozapina Derivados Benzofuranonas Risperidona Olanzapina esquizofrenia métodos de regresión campos de interacción Molecular (MIF) Perfil Multireceptorial modelado por homología Docking sitio de unión interacciones moleculares selectividad afinidad de unión Diana Meta-Diana efectos secundarios fármaco antipsicótico típico fármaco antipsicótico atípico agonista antagonista membrana receptoroma rhodopsina estructura de rayos X descriptores moleculares farmacología análisis multivariante procedimiento integrado Computer-aided drug design Integrated approach Multivariate analysis Pharmacology Molecular descriptors X-ray structure Rhodopsin Receptorome Membrane Antagonist Agonist Atypical antipsychotic drug Typical antipsychotic drug Side effects Off-target Target Selectivity Binding affinity Molecular interactions Binding site Homology modeling Docking Multireceptor profile Molecular interaction Field (MIF) Partial Least Squares (PLS) Principal Component Analysis (PCA) Schizophrenia Benzolactam Derivatives Benzofuranone Derivatives Risperidone Olanzapine Clozapine Ligand Adrenergic receptors Muscarinic receptors Histamine receptors Serotonin receptors Dopamine receptors beta2-Adrenergic receptor D2 receptor D3 receptor 5-HT2A receptor G protein-coupled receptors (GPCR) 57

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