Thesis Title: Subtitle - NMR Spectroscopy Research Group

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104 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. fragment selection and constrain backbone torsion angles, greatly improving the final yield of correctly folded protein models (Shen et al., 2008). As ROSETTA in favorable cases is capable of generating protein structures very close to experimentally determined structures from sequence information alone (Bradley et al., 2005), it is of great interest to combine ROSETTA with readily accessible experimental data to determine protein structures. In this paper we describe the incorporation of PCS data into ROSETTA. We show that this new PCS-ROSETTA method can generate accurate structures for proteins of up to 150 amino acids in length even from quite limited data sets. 4.3 Results 4.3.1 Test set We tested the new PCS-ROSETTA method (see Methods) on a benchmark of nine proteins for which chemical shifts and PCSs have been published. ArgN repressor was determined twice with PCS data measured from paramagnetic metal ions at two different sites. The proteins were between 56 to 186 amino acid residues in size, had different folds and had between 82 and 1169 PCSs measured from one to eleven different metal ions located at a single metal binding site [Table 4.1 and supporting information (SI) Table S4.1]. Fragments for each protein were selected with CS- ROSETTA using available chemical shift data and were used for all calculations. Structures of proteins with significant sequence similarity to the target proteins were explicitly excluded from the CS-ROSETTA database. Table 4.1 Protein structures used to evaluate the performance of PCS-ROSETTA Targets PDB ID Nres a NM b c PCS-ROSETTA Npcs run d CS-ROSETTA run e rmsd f conv g Q h rmsd f conv g protein G (A) 3GB1 56 3 158 0.61 0.92 0.06 0.80 0.88 (Wilton et al., 2008) Refcs i Refpcs j calbindin (B) 1KQV 75 11 1169 1.46 2.04 0.16 4.96 4.37 (Balayssac et al., 2006) θ subunit (C) 2AE9 76 2 91 1.65 4.35 0.07 8.90 8.75 (Mueller et al., 2005) (Saio et al., 2009) (Bertini et al., 2001) (Schmitz et al., 2008) ArgN k (D) 1AOY 78 3 222 0.98 2.38 0.08 6.93 5.32 (Su et al., 2008) (Su et al., 2008) ArgN l (E) 1AOY 78 2 82 1.03 2.25 0.09 8.01 6.64 (Su et al., 2008) (Su et al., 2009a) N-calmodulin (F) 1SW8 79 2 125 2.34 1.85 0.09 4.69 3.68 (Bertini et al., 2004) (Bertini et al., 2004)
thioredoxin (G) 1XOA 108 1 90 2.58 2.64 0.23 parvalbumin (H) 4.98 6.06 (Lemaster et al., 1988, Chandrasekhar et al., 1991) 1RJV 110 1 106 11.26 10.42 0.20 11.80 11.20 (Baig et al., 2004) calmodulin (I) 2K61 146 4 408 2.80 2.12 0.14 6.35 5.55 (Bertini et al., 2009) ε186 m (J) 1J54 186 3 738 20.57 17.54 0.36 15.46 17.23 (DeRose et al., 2002) a Number of residues. b Number of metal ions for which PCS data were measured. c Total number of PCSs measured. 4.3 Results. 105 (Jensen et al., 2006) (Baig et al., 2004) (Bertini et al., 2009) (Schmitz et al., 2006) d The structures used to calculate the rmsds were identified using the combined PCS-score and ROSETTA full atom energy on the whole protein sequence. e The structures used to calculate the rmsds were identified by the ROSETTA full-atom energy on the whole protein sequence. f C α rmsd (with respect to the native structure) of the structure of lowest score, in Å. All C rmsd values were calculated using the core residues defined in SI Table S4.2. g Average C α rmsd calculated between the lowest score structure and the next four lowest scoring structure, in Å. The rmsd values were calculated on the whole protein sequence. h Quality factor Q = rms(PCSi cal – PCSi exp ) / rms(PCSi exp ) calculated on the structure of lowest PCS-ROSETTA score. i Reference for the experimental chemical shifts. j Reference for the experimental PCSs. k PCSs measured with a covalent tag attached to the N-terminal domain of the E. coli arginine repressor (ArgN). l PCSs measured with a non-covalent tag bound to ArgN. m N-terminal 186 residues of the subunit of the E. coli polymerase III. 4.3.2 Capacity of the PCS Score to Identify Native-like Structures The PCS score describes a model’s agreement with observed PCS data by calculating the expected PCS data given the structure. To calculate this, a three dimensional grid search is used to determine the metal coordinates and Δχ-tensor components necessary for producing an optimal match between calculated and observed data (see Materials and Methods). The capacity of the PCS score to identify native like models was tested on sets of 3000 CS-ROSETTA structures for each of the nine test proteins. These test structures were produced using a reduced fragment set and
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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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