Thesis Title: Subtitle - NMR Spectroscopy Research Group

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108 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. Figure 4.2 Improved conformational sampling by PCS-ROSETTA. 10000 independent low resolution trajectories were carried out with (black) or without (red) PCS information. The plots show the density of C rmsd values to the target structure after the fragment assembly step. The targets are labeled as in Table 4.1. Corresponding plots of structures calculated with full atom relaxation for positioning the amino acid side chains are shown in SI Figure S4.2. The library used for fragment selection explicitly excluded any protein with sequence similarity to the target protein. The figure shows that PCS scores efficiently guide fragment assembly towards the correct target structure. Figure 4.3 Energy landscapes generated by PCS-ROSETTA. Combined ROSETTA energy and PCS score (using the weighting factor w(c)) are plotted versus the C rmsd to the target structure for structures calculated using PCS-ROSETTA. The lowest energy structures are indicated in red. The targets are labeled as in Table 4.1. The results show that PCS-ROSETTA is likely to generate and identify the correct fold.
4.3 Results. 109 Agreement of the structures with the experimental data can also be directly assessed by the quality factor Q = rms(PCSi cal – PCSi exp ) / rms(PCSi exp ), where PCSi exp is the experimental PCS value for the nuclear spin i 7 . A quality factor above 25% indicates failure to find a correct structure and a quality factor below 20% indicates that the computed structure is in good agreement with the experimental PCSs (Table 4.1), as in other definitions of quality factors (Cornilescu et al., 1998). The low quality factor of the θ subunit (7%) establishes the success of the calculation despite the lack of clear convergence. 4.3.4 Successes and Limits of PCS-ROSETTA Calculations The results of PCS-ROSETTA calculations are summarized in Table 4.1. The structures of small proteins (< 80 residues, targets A to F) are easily solved by PCS-ROSETTA: the lowest PCS- ROSETTA energy are consistently below 2.4 Å in C rmsd relative to the native structure and have quality factor below 16%. For these proteins, the generation of 10000 models was ample (Figure 4.2 A to F). The same number of decoys calculated with CS-ROSETTA did not lead to satisfactory convergence for targets B-C-D-E-F (Table 4.1-g), though targets C and D partially recover if flexible termini are removed at the full atom rescoring step (SI Text S4.2). The tag used to paramagnetically label ArgN (D) produced Δχ-tensor axes of significantly different orientation with different lanthanides (Su et al., 2008) which may explain why the PCS-ROSETTA calculations performed particular well with these data. PCS-ROSETTA succeeded in calculating the structure of a protein with 146 residues and PCSs from multiple lanthanides (target I). More than 62% of calculated structures had a C RMSD below 5 Å, while only 6.2% met that criterion for CS-ROSETTA calculation (Figure 4.2 I). This indicates that the PCS data score will effectively guide the sampling towards the correct fold also for larger proteins. While calculations on target J (186 residues) did not converge despite a large PCS data set, this can be attributed to a sampling problem associated with large proteins of complex topology (Bradley et al., 2005) which may be overcome with a modified protocol. Importantly, the success of a calculation can be ascertained from calculating the quality factor Q. Combined with the convergence criterion (Shen et al., 2008), the quality factor is an effective way 7 Rms stands for Root Mean Square. Not to be confused with Rmsd (Root Mean Square Deviation).
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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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