Thesis Title: Subtitle - NMR Spectroscopy Research Group

More documents

Recommendations

Info

106 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. included native fragments to ensure that some of the models were similar to the target structure. The C rmsd of the decoy with the lowest PCS score was always small (below 2.3 Å) with respect to the target protein (Figure 4.1). In addition, for all target proteins for which PCSs were available from two or more paramagnetic metal ion, low C rmsd values correlated with low PCS scores. This indicates that the PCS score can be used not only to identify near-native structures, but also to bias conformational sampling towards the native structure during the fragment assembly. Comparisons between the ROSETTA low resolution energy function and PCS score are shown in SI Figure S4.1. Figure 4.1 Fold identification by pseudocontact shifts. 3000 decoys were generated using CS- ROSETTA. In order to ensure the presence of decoys with low rmsd values to the target structure, the starting set of peptide fragments was reduced and included fragments from the known target structures. PCS scores are plotted versus the C rmsd to the target structure. The targets are labeled A-J as in Table 4.1. The PCS score correlates strongly with the C rmsd. PCSs from eleven different lanthanides were available for calbindin. In order to explore the value of using PCSs from multiple lanthanides, we rescored the structures using PCSs from both individual and multiple lanthanides. Linear regressions of PCS score versus rmsd had slopes ranging from 0.03 to 5.17 (average 2.26) for single data sets. Pairwise combination of PCS sets resulted in increased regression slopes ranging from 0.15 to 7.42 (average 3.84). Using all PCS sets resulted in a slope greater than 11, showing that PCSs from multiple metal ions greatly facilitate identification of native-like protein folds. 4.3.3 Comparison of PCS-ROSETTA with CS-ROSETTA
4.3 Results. 107 10000 decoys each were generated with CS-ROSETTA and PCS-ROSETTA. Both computations used the same fragment set, taking into account secondary structure information from chemical shift measurements. Figure 4.2 illustrates the ability of the PCS score to bias sampling towards the native structure. For seven out of the ten structure calculations, the PCSs dramatically increased the frequency with which decoys with low C rmsd to the reference structure were found. The effect was particularly pronounced for protein targets with larger PCS data sets. For example, more than a third of the decoys found for calmodulin had a C rmsd of less than 4 Å to the target structure, whereas fewer than 3% met this criterion in the absence of PCS data. Similar results were obtained for the θ subunit, protein G, and both ArgN repressor calculations. The PCS data did not significantly improve the results for thioredoxin and parvalbumin for which only PCS data from a single paramagnetic metal ion were available. No native-like structures were found for 186 which may be attributed to its larger size (186 residues). To evaluate the influence of the PCS score during the fragment assembly, we performed an additional calculation with the PCS score as the only energy term (SI Text S4.1). The low resolution models were subjected to full atom relaxation refinement in the last step of the calculation, using the full atom ROSETTA force field (without inclusion of the PCS score). The additional minimization step did not significantly change the overall shape of the distributions, but tended to improve the C rmsd of native-like decoys (SI Figure S4.2) and, most importantly, allows recognition of the best models based on their energies. Rescoring full atom relaxed structures with a weighted combination of the ROSETTA and PCS scores further improved the recognition of near-native structures as measured by the C rmsd of the lowest energy structure [Table 4.1-f, PCS-ROSETTA run; Figure 4.3], with PCS-ROSETTA identifying low C rmsd (< 3 Å) structures in eight out of ten cases. With the exception of target C, for all successful targets a population of the five lowest energy structures converge to less than 3 Å , while the two failed targets do not improve beyond 10 Å [Table 4.1-g]. Convergence is a signal that the protocol has found a topology that reliably satisfies the combined score, which in the case of PCS-ROSETTA clearly identifies the failed models as unreliable, allowing for their rejection (Shen et al., 2008). In the case of target C large disordered termini prevent a clear identification of convergence, but convergence becomes apparent when only the core residues are considered (Table S4.2-g). Results with CS-ROSETTA and PCS-ROSETTA are compared in SI Figure S4.3.
Page 1 and 2:
Computational study of proteins wit
Page 3 and 4:
iii Schmitz C, Stanton-Cook MJ, Su
Page 5 and 6:
v Acknowledgements I would like to
Page 7 and 8:
vii Keywords paramagnetic nmr, pseu
Page 9 and 10:
ix 2.3.3 Manual resonance assignmen
Page 11 and 12:
xi List of Figures Figure 1.1 NMR e
Page 13 and 14:
xiii
Page 15:
xv List of Abbreviations α Subunit
Page 18 and 19:
2 Chapter 1. Introduction. The scal
Page 20 and 21:
4 Chapter 1. Introduction. In contr
Page 22 and 23:
6 Chapter 1. Introduction. as the c
Page 24 and 25:
8 Chapter 1. Introduction. Figure 1
Page 26 and 27:
10 Chapter 1. Introduction. Protein
Page 28 and 29:
12 Chapter 1. Introduction. 1.3 Com
Page 30 and 31:
14 Chapter 1. Introduction. Figure
Page 32 and 33:
16 Chapter 1. Introduction. Figure
Page 34 and 35:
18 Chapter 1. Introduction. (Bugaye
Page 36 and 37:
20 Chapter 1. Introduction. The det
Page 38 and 39:
22 Chapter 1. Introduction. (ii) It
Page 40 and 41:
24 Chapter 1. Introduction. Chapter
Page 42 and 43:
26 Chapter 1. Introduction. Su XC,
Page 44 and 45:
28 Chapter 2. Possum: paramagnetica
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73: 56 Chapter 2. Possum: paramagnetica
Page 91 and 92: Chapter 3 Numbat: new user-friendly
Page 93 and 94: 3.4 Introduction 3.4 Introduction.
Page 95 and 96: 3.5 Algorithm. 79 rhombic component
Page 97 and 98: 3.6 Program Features. 81 each fitti
Page 99 and 100: 3.6 Program Features. 83 A new PCS
Page 101 and 102: 3.6 Program Features. 85 Carlo prot
Page 103 and 104: 3.7 Study case. 87 The coordinates
Page 105 and 106: 3.7 Study case. 89 illustrates how
Page 107 and 108: 3.7 Study case. 91 can be used to a
Page 109 and 110: 3.9 Acknowledgment. 93 variables, t
Page 111 and 112: 3.10 References. 95 Galassi M, Davi
Page 113 and 114: 3.11 Supporting information. 97 Zwe
Page 115: 3.11 Supporting information. 99 Tab
Page 118 and 119: 102 Chapter 4. Protein Structure De
Page 145 and 146: Chapter 5 Conclusion and perspectiv
Page 147 and 148: 5.1 The use of PCS for structure de
Page 149 and 150: 5.1 The use of PCS for structure de
Page 151 and 152: 5.3 The use of PCS for protein dock
show all

Thesis Title: Subtitle - NMR Spectroscopy Research Group

Create successful ePaper yourself

Delete template?

Save as template?