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Molecular dynamics (MD) simulation serves as an important and widely used computational tool to study molecular systems at an atomic resolution. No experimental technique is capable of generating a complete description of the dynamical structure of the biomolecules in their native solution environment. MD simulations allow us to study the dynamics and structure of the system and, moreover, helps in the interpretation of experimental observations. MD simulation was first introduced and applied by Alder and Wainwright in 1957 \cite{Alder57}. However, the first MD simulation of a macromolecule of biological interest was published 28 years ago \cite{McCammon77}. The simulation was concerned with the bovine pancreatic trypsin inhibitor (BPTI) protein, which has served as the hydrogen molecule'' of protein dynamics because of its small size, high stability, and relatively accurate X-ray structure available in 1977 \cite{Deisenhofer75}. This method is now widely used to tackle larger and more complex biological systems \cite{Groot01,Roux02} and has been facilitated by the development of fast and efficient methods for treating the long-range electrostatic interactions \cite{Essmann95}, the availability of faster parallel computers, and the continuous development of empirical molecular mechanical force fields \cite{Langley98,Cheatham99,Foloppe00}. It took several years until the first MD simulations of nucleic acid systems were performed \cite{Levitt83,Tidor83,Prabhakaran83,Nilsson86}. These investigations, which were also performed in vacuo, clearly demonstrated the importance of proper handling of electrostatics in a highly charged nucleic acid system, and different approaches, such as reduction of the phosphate charges and addition of hydrated counterions, have been applied to remedy this shortcoming and to maintain stable DNA structures. A few years later, the first MD simulation of a DNA molecule, including explicit water molecules and counterions was published \cite{Seibel85}. Various MD simulations on fully solvated RNA molecules with explicit inclusion of mobile ions indicated the importance of proper treatment of the environment of highly charged nucleic acids \cite{Lee95,Zichi95,Auffinger97,Auffinger99}. Given the central roles of RNA in the life of cells, it is important to understand the mechanism by which RNA forms three dimensional structures endowed with properties such as catalysis, ligand binding, and recognition of proteins. Furthermore, the increasing awareness of the essential role of RNA in controlling viral replication and in bacterial protein synthesis emphazises the potential of ribonucleicacids as targets for developing new antibacterial and new antiviral drugs. Driven by fruitful collaborations in the Sonderforschungsbereich RNA-Ligand interactions" the model RNA systems in this study include various RNA tetraloops and HIV-1 TAR RNA. For the latter system, the binding sites of heteroaromatic compounds have been studied employing automated docking calculations \cite{Goodsell90}. The results show that it is possible to use this tool to dock small rigid ligands to an RNA molecule, while large and flexible molecules are clearly problematic. The main part of this work is focused on MD simulations of RNA tetraloops.
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.