Thesis Title: Subtitle - NMR Spectroscopy Research Group

More documents

Recommendations

Info

102 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. 4.1 Abstract Pseudocontact shifts (PCS) arise from paramagnetic metal ions bound to proteins and are manifested as large changes in chemical shifts detected in nuclear magnetic resonance (NMR) spectra. PCS data constitute long-range restraints on the positions of nuclear spins relative to the coordinate system of the magnetic susceptibility anisotropy tensor ( -tensor) of the metal ion. Protein structure determination using PCS data only, however, is hampered by the difficulty to determine the -tensor and metal position without knowledge of the protein structure. We have circumvented this problem in the program PCS-ROSETTA by using the structure prediction program ROSETTA to generate the models required for fitting of the -tensor parameters. PCS restraints implemented in the fragment assembly step of PCS-ROSETTA proved highly efficient in biasing the sampling of the conformational space towards the correct target structure. The results show that using a combination of chemical shift and PCS data, ROSETTA can determine structures accurately for proteins of up to 150 residues. Lanthanides can be incorporated into proteins quite generally through metal binding tags, and the combination of these data with the PCS-ROSETTA method provides a powerful new approach to protein structure determination. 4.2 Introduction The three-dimensional (3D) structure of proteins is a prerequisite for understanding protein function, protein-ligand interactions and rational drug design. Protein structures can be readily determined by NMR spectroscopy. The most difficult part of an NMR structure determination typically is the assignment of sidechain chemical shifts and NOESY peaks. This bottleneck can potentially be avoided if methods for computing high accuracy structures from backbone-only NMR experiments can be developed. PCSs are a potentially rich source of structural information that are manifested as large changes in chemical shifts in the NMR spectrum caused by a non-vanishing magnetic susceptibility anisotropy tensor ( -tensor) of a paramagnetic metal ion. The PCS (in ppm) of a nuclear spin i depends on the polar coordinates ri, i, and i of the nuclear spin with respect to the -tensor frame of the metal ion and the axial and rhombic components of the -tensor:
4.2 Introduction. 103 (4.1) The -tensor defines a coordinate system in the molecule that is centered on the metal ion and is fully described by eight parameters ( ax, rh, three Euler angles relating the orientation of the Δχ-tensor to the protein frame, and the coordinates of the metal ion). Therefore, the Δχ-tensor can be determined using PCS data from at least eight nuclear spins, provided the coordinates of the spins are known. As PCSs can be measured for nuclear spins 40 Å away from the metal, they present long- range structure restraints exquisitely suited to characterize the global structural arrangement of a protein. PCSs have thus been used very successfully to refine protein structures (Bertini et al., 2001, Gaponenko et al., 2004, Arnesano et al., 2005), dock protein molecules of known 3D structures (Ubbink et al., 1998, Pintacuda et al., 2006) and determine the structure of small molecules bound to a protein of known 3D structure (John et al., 2006, Pintacuda et al., 2007, Zhuang et al., 2008). The need for atom coordinates to determine the Δχ-tensor parameters, however, makes it more difficult to use PCSs in de novo determinations of protein 3D structures. All presently available protein structure determination software that uses PCS data to supplement conventional NMR restraints requires estimates of ax and rh as input parameters (Banci et al., 1998, Banci et al., 2004). These are often difficult to estimate accurately, as they depend on the chemical environment of the metal ion and the mobility of the paramagnetic center with respect to the protein. The ROSETTA structure prediction methodology (Simons et al., 1997) is well suited for taking advantages of the rich source of information inherent in PCSs. ROSETTA de novo structure prediction has two stages —first a low resolution phase in which conformational space is searched broadly using a coarse grained energy function, and second, a high resolution phase in which models generated in the first phase are refined in a physically realistic all atom force field. The bottleneck in structure prediction using ROSETTA is conformational sampling; close to native structures almost always have lower energies than non native structures. For small proteins ( < 100 residues), ROSETTA has produced models with atomic level accuracy in blind prediction challenges (Raman et al., 2009). For larger proteins, however, structures close enough to the native structure to fall into the deep native energy minimum are generated seldom or not at all. This sampling problem can be overcome if even very limited experimental data is available to guide the initial low resolution search. For example, CS-ROSETTA uses NMR chemical shifts to guide
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: 52 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 120 and 121: 104 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?