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Development of a normal mode-based geometric simulation approach for investigating the intrinsic mobility of proteins

By Aqeel Ahmed

Abstract

Specific functions of biological systems often require conformational transitions of macromolecules. Thus, being able to describe and predict conformational changes of biological macromolecules is not only important for understanding their impact on biological function, but will also have implications for the modelling of (macro)molecular complex formation and in structure-based drug design approaches. The “conformational selection model” provides the foundation for computational investigations of conformational fluctuations of the unbound protein state. These fluctuations may reveal conformational states adopted by the bound proteins. The aim of this work is to incorporate directional information in a geometry-based approach, in order to sample biologically relevant conformational space extensively. Interestingly, coarse-grained normal mode (CGNM) approaches, e.g., the elastic network model (ENM) and rigid cluster normal mode analysis (RCNMA), have emerged recently and provide directions of intrinsic motions in terms of harmonic modes (also called normal modes). In my previous work and in other studies it has been shown that conformational changes upon ligand binding occur along a few low-energy modes of unbound proteins and can be efficiently calculated by CGNM approaches. In order to explore the validity and the applicability of CGNM approaches, a large-scale comparison of essential dynamics (ED) modes from molecular dynamics (MD) simulations and normal modes from CGNM was performed over a dataset of 335 proteins. Despite high coarse-graining, low frequency normal modes from CGNM correlate very well with ED modes in terms of directions of motions (average maximal overlap is 0.65) and relative amplitudes of motions (average maximal overlap is 0.73). In order to exploit the potential of CGNM approaches, I have developed a three-step approach for efficient exploration of intrinsic motions of proteins. The first two steps are based on recent developments in rigidity and elastic network theory. Initially, static properties of the protein are determined by decomposing the protein into rigid clusters using the graph-theoretical approach FIRST at an all-atom representation of the protein. In a second step, dynamic properties of the molecule are revealed by the rotations-translations of blocks approach (RTB) using an elastic network model representation of the coarse-grained protein. In the final step, the recently introduced idea of constrained geometric simulations of diffusive motions in proteins is extended for efficient sampling of conformational space. Here, the low-energy (frequency) normal modes provided by the RCNMA approach are used to guide the backbone motions. The NMSim approach was validated on hen egg white lysozyme by comparing it to previously mentioned simulation methods in terms of residue fluctuations, conformational space explorations, essential dynamics, sampling of side-chain rotamers, and structural quality. Residue fluctuations in NMSim generated ensemble is found to be in good agreement with MD fluctuations with a correlation coefficient of around 0.79. A comparison of different geometry-based simulation approaches shows that FRODA is restricted in sampling the backbone conformational space. CONCOORD is restricted in sampling the side-chain conformational space. NMSim sufficiently samples both the backbone and the side-chain conformations taking experimental structures and conformations from the state of the art MD simulation as reference. The NMSim approach is also applied to a dataset of proteins where conformational changes have been observed experimentally, either in domain or functionally important loop regions. The NMSim simulations starting from the unbound structures are able to reach conformations similar to ligand bound conformations (RMSD < 2.4 Å) in 4 out of 5 cases of domain moving proteins. In these four cases, good correlation coefficients (R > 0.7) between the RMS fluctuations derived from NMSim generated structures and two experimental structures are observed. Furthermore, intrinsic fluctuations in NMSim simulation correlate with the region of loop conformational changes observed upon ligand binding in 2 out of 3 cases. The NMSim generated pathway of conformational change from the unbound structure to the ligand bound structure of adenylate kinase is validated by a comparison to experimental structures reflecting different states of the pathway as proposed by previous studies. Interestingly, the generated pathway confirms that the LID domain closure precedes the closing of the NMPbind domain, even if no target conformation is provided in NMSim. Hence, the results in this study show that, incorporating directional information in the geometry-based approach NMSim improves the sampling of biologically relevant conformational space and provides a computationally efficient alternative to state of the art MD simulations.Konformationsänderungen von Proteinen sind häufig eine grundlegende Voraussetzung für deren biologische Funktion. Die genaue Charakterisierung und Vorhersage dieser Konformationsänderungen ist für das Verständnis ihres Einflusses auf die Funktion erforderlich. Eines der dafür am häufigsten verwendeten und genauesten computergestützten Verfahren ist die Molekulardynamik-Simulationen (MD Simulationen). Diese sind jedoch nach wie vor sehr rechenintensiv und durchmustern den Konformationsraum nur in begrenztem Maße. Daher wurden Anstrengungen unternommen, alternative geometriebasierte Methoden (wie etwa CONCOORD oder FRODA) zu entwickeln, die auf einer reduzierten Darstellung von Proteinen beruhen. Das Ziel dieser Arbeit ist es, Richtungsinformationen in einen geometriebasierten Ansatz zu integrieren, und so den biologisch relevanten Konformationsraum erschöpfend zu durchmustern. Diese Idee führte kürzlich zur Entwicklung von „coarse-grained normal mode“ (CGNM) Methoden, wie zum Beispiel dem „elastic network model“ (ENM) und der von mir in vorangegangenen Arbeiten entwickelte „rigid cluster normal mode analysis“ (RCNMA). Beide Methoden liefern die gewünschte Richtungsinformation der intrinsischen Bewegungen eines Proteins in Form von harmonischen Moden (auch Normalmoden). Um die Aussagekraft, Robustheit und breite Anwendbarkeit solcher CGNM Verfahren zu untersuchen, wurde im Rahmen dieser Dissertation ein umfangreicher Vergleich zwischen „essential dynamics“ (ED) Moden aus MD Simulationen und Normalmoden aus CGNM Berechnungen durchgeführt. Der zugrundeliegende Datensatz enthielt 335 Proteine. Obwohl die CGNM Verfahren eine stark vereinfachte Darstellung für Proteine verwenden, korrelieren die niederfrequenten Moden dieser Verfahren bezüglich ihrer Bewegungs-Richtung (durchschnittliche maximale Überschneidung: 0,65) und -Amplitude (durchschnittliche maximale Überschneidung: 0,73) sehr gut mit ED Moden. Im Durchschnitt beschreibt das erste Viertel der Normalmoden 85 % des Raumes, der durch die ersten fünf ED Moden aufgespannt wird. Um die Leistungsfähigkeit von CGNM Verfahren genauer zu bestimmen, wurde im Rahmen der vorliegenden Studie eine dreistufige Methode zur Untersuchung der intrinsischen Dynamik von Proteinen entwickelt. Die ersten beiden Stufen basieren auf neusten Entwicklungen in der Rigiditäts-Theorie und der Beschreibung von elastischen Netzwerken. Diese sind im RCNMA Ansatz verwirklich und ermöglichen die Bestimmung der Normalmoden. Im letzten Schritt werden die Bewegungen des Proteinrückgrates entlang der mittels RCNMA erzeugten niederenergetischen Normalmoden ausgerichtet. Die Seitenkettenkonformrationen werden dabei durch Diffusionsbewegungen hin zu energetisch günstigen Rotameren erzeugt. Dies ist ein iterativer Prozess, bestehend aus mehreren kleineren Schritten, in denen jeweils intermediäre Konformationen erzeugt werden. Zur Validierung des NMSim Ansatzes wurde dieser mit den anderen zuvor genannten Simulationsmethoden am Beispiel von Lysozym verglichen. Die Fluktuationen der Aminosäurereste aus dem mit NMSim erzeugten Ensemble stimmen mit berechneten Fluktuationen aus der MD Simulation gut überein (Korrelationskoeffizient R = 0,79). Ein Vergleich der unterschiedlichen geometriebasierten Simulationsansätze zeigt, dass bei FRODA die Durchmusterung des Konformationsraumes des Proteinrückrates unzureichend ist. Bei CONCOORD ist hingegen die Durchmusterung des Konformationsraumes der Seitenketten unzureichend. NMSim hingegen durchmustert sowohl den Konformationsraum des Proteinrückrates als auch den der Seitenketten angemessen, wenn man die experimentell und mittels MD Simulationen erzeugten Konformationen als Referenz verwendet. Der NMSim Ansatz wurde ebenfalls auf einen Datensatz von Proteinen angewendet, für die Konformationsänderungen in Domänen oder in funktionell wichtigen Schleifenregionen experimentell beobacht wurden. In Übereinstimmung mit dem Konformations-Selektions-Modell ist der NMSim Ansatz bei vier von fünf Proteinen, die eine Domänenbewegung aufweisen, in der Lage, ausgehend von der ungebundenen Struktur neue Konformationen zu erzeugen, die der ligandgebundenen Konformation entsprechen (RMSD < 2,4 Å). In diesen vier erfolgreichen Fällen wurde ein hoher Korrelationskoeffizient (R > 0,7) zwischen der RMS Fluktuation der durch NMSim erzeugten Konformationen und jeweils zwei experimentellen Strukturen erreicht. Hingegen korrelieren die intrinischen Fluktuationen der NMSim Simulation in zwei von drei Fällen mit dem Bereich der ligandinduzierten Konformationsänderung in den Schleifen. Der mit NMSim generierte Pfad für die Konformationsänderungen von der ungebundenen Struktur zur ligandgebundenen Struktur der Adenylat-Kinase wurde durch den Vergleich zu experimentellen Strukturen validiert, die verschiedene Zustände des Pfades widerspiegeln. Die unterschiedlichen Kristallstrukturen, die entlang der Konformationsänderungen von der ungebundenen zur ligandgebundenen Struktur liegen, werden auf dem von NMSim erzeugten Pfad durchmustert. Interessanterweise bestätigt der generierte Pfad, dass die Schließbewegung der LID Domäne derjenigen der NMPbind Domäne vorangeht, sogar wenn keine Zielkonformation für die NMSim Simulation verwendet wurde

Topics: Molekulardynamik, Molekulardesign, Normalkoordinatenanalyse, Proteine, ddc:570
Year: 2010
OAI identifier: oai:publikationen.ub.uni-frankfurt.de:7361

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