Thesis Title: Subtitle - NMR Spectroscopy Research Group

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110 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. to assert the success of a calculation (SI Figure S4.4). For each of the eight targets for which the PCS-ROSETTA calculations converged, the structure with the lowest energy is shown superimposed with the native structure in Figure 4.4. Figure 4.4 Superimpositions of ribbon representations of the backbones of the lowest energy structures calculated with PCS-ROSETTA (blue) onto the corresponding target structures (red). The protein targets are (A) protein G, (B) calbindin, (C) the θ subunit of E. coli DNA polymerase III, (D) the N-terminal domain of the E. coli arginine repressor (ArgN; with covalent lanthanide tag), (E) ArgN with non-covalent lanthanide tag, (F) the N-terminal domain of calmodulin, (G) thioredoxin, (H) parvalbumin, (I) calmodulin and (J) the globular domain of the ε subunit of E. coli DNA polymerase III. Flexible termini were omitted as described in SI Table S4.1. Only the target structure is shown for parvalbumin (H) and the ε subunit (J), as the calculations could not reproduce the correct fold for these proteins. 4.4 Discussion The structural information content of the PCS effect has long been recognized, but initial attempts to determine the 3D structures of biomolecules by the use of PCSs were hampered by the difficulty to determine -tensor and structure simultaneously (Barry et al., 1971). Subsequently, the first 3D structure determinations of proteins relied on nuclear Overhauser effect data (Wüthrich, 1986). Full structure determination of proteins from PCS data alone continues to be regarded as difficult (Bertini et al., 2002a). Owing to its modeling capabilities, PCS-ROSETTA makes it
4.4 Discussion. 111 possible, for the first time, to determine 3D structures using PCSs as the only restraints while simultaneously determining all Δχ-tensor parameters and integrating PCSs from different metal ions. In addition, a PCS quality factor can be calculated that is highly indicative of the correctness of the final structure. The effect of the PCSs on improving convergence of the calculations towards the correct target structures is particularly remarkable if one considers that PCS data mostly were available only for backbone amides. The success of PCS-ROSETTA is based on the fact that, in contrast to scoring functions using chemical shift data, the PCS score is much more sensitive to global than local structure. Therefore, PCS data can guide the search in the low resolution fragment assembly step, greatly increasing the yield of near-native structures compared to CS-ROSETTA. PCSs thus present an ideal complement to chemical shift information that is most important in the preceding fragment selection step. The improved convergence alleviates the need to compute large numbers of decoys. It would be possible to accelerate the computations further by using the PCS score to select decoys with low rmsd values to the target structure prior to the computationally expensive refinement of amino acid side chain conformations. Many protein specific factors, including fold complexity, number and quality of PCS data, and metal site play roles in the success of PCS-ROSETTA fragment assembly and their relative importance is difficult to disentangle. In general, PCS data from two or more lanthanides are expected to assist identification of decoys with low rmsd to the target structure. While the structure of calmodulin, a protein with 146 residues, was successfully determined by PCS-ROSETTA, the structure of ε186 (186 residues) was not found by the program despite the availability of many PCSs overall (Table 4.1). The scarcity of PCS values for residues near the lanthanide binding site may have contributed to this effect. As the PCS-ROSETTA protocol did not sample structures below 10 Å rmsd (Figure 4.3 J) and the PCS scores of native-ε186 like structures only show a funnel-like energy landscape below 10 Å rmsd (Figure 4.1 J), this could also be a case where structures explored by the basic ROSETTA sampling protocol do not form enough native features for the PCS score to discriminate between them. An alternative sampling protocol, such as broken chain sampling (Bradley et al., 2006) or iterative refinement (Qian et al., 2007), may be the key to accurately modeling the structure of ε186 using PCS data. The present calculations were performed with proteins containing single metal binding sites. Clearly, data from multiple metal ions using different metal binding sites will greatly enhance the information content of PCS data. In particular, lanthanide ions display very different paramagnetic properties while their chemical similarity allows all lanthanides to bind at a given lanthanide
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Computational study of proteins wit
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iii Schmitz C, Stanton-Cook MJ, Su
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v Acknowledgements I would like to
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vii Keywords paramagnetic nmr, pseu
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ix 2.3.3 Manual resonance assignmen
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xi List of Figures Figure 1.1 NMR e
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xv List of Abbreviations α Subunit
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Thesis Title: Subtitle - NMR Spectroscopy Research Group

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