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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.
Previous studies in patients with single-sided deafness (SSD) have reported results of pitch comparisons between electric stimulation of their cochlear implant (CI) and acoustic stimulation presented to their near-normal hearing contralateral ear. These comparisons typically used sinusoids, although the percept elicited by electric stimulation may be closer to a wideband stimulus. Furthermore, it has been shown that pitch comparisons between sounds with different timbres is a difficult task and subjected to various types of range biases. The present study aims to introduce a method to minimize non-sensory biases, and to investigate the effect of different acoustic stimulus types on the frequency and variability of the electric-acoustic pitch matches. Pitch matches were collected from 13 CI users with SSD using the binary search procedure. Electric stimulation was presented at either an apical or a middle electrode position, at a rate of 800 pps. Acoustic stimulus types were sinusoids (SINE), 1/3-octave wide narrow bands of Gaussian noises (NBN), or 1/3-octave wide pulse spreading harmonic complexes (PSHC). On the one hand, NBN and PSHC are presumed to better mimic the spread of excitation produced by a single-electrode stimulation than SINE. On the other hand, SINE and PSHC contain less inherent fluctuations than NBN and may therefore provide a temporal pattern closer to that produced by a constant-amplitude electric pulse train. Analysis of mean pitch match variance showed no differences between stimulus types. However, mean pitch matches showed effects of electrode position and stimulus type, with the middle electrode always matched to a higher frequency than the apical one (p < 0.001), and significantly higher across-subject pitch matches for PSHC compared with SINE (p = 0.017). Mean pitch matches for all stimulus types were better predicted by place-dependent characteristic frequencies (CFs) based on an organ of Corti map compared with a spiral ganglion map. CF predictions were closest to pitch matches with SINE for the apical electrode position, and conversely with NBN or PSHC for the middle electrode position. These results provide evidence that the choice of acoustic stimulus type can have a significant effect on electric-acoustic pitch matching.