Thesis Title: Subtitle - NMR Spectroscopy Research Group

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112 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. binding site. Several metal binding tags have recently been developed to tag proteins site- specifically with a paramagnetic lanthanide; for a recent review, see (Su et al., 2009b). We note that PCSs were as useful for targets devoid of natural metal binding sites (targets A, C, D and E) as for metalloproteins (Figure 4.2). We propose a new approach to protein structure determination in which PCS data are collected from natural or engineered metal binding sites, and then used to guide ROSETTA conformational search along with backbone chemical shift data. The accuracy and reliability of the lowest energy models is assessed based on the convergence of the calculation and the PCS quality factor. With multiple independent lanthanide datasets and improved conformational search methods, the approach should be extendable to proteins greater than 150 amino acids. 4.5 Materials and Methods 4.5.1 PCS-ROSETTA Score. et al., 2002b) The PCS (in ppm) induced by a metal ion M on a nuclear spin can be calculated as (Bertini (4.2) where ri is the distance between the spin i and the paramagnetic centre M, xi, yi, zi are the Cartesian coordinates of the vector between the metal ion and the spin i in an arbitrary frame f and Δχxx, Δχyy, Δχzz, Δχxy, Δχxz, Δχyz are the Δχ-tensor components in the frame f (as Δχzz = -Δχxx -Δχyy, there are only five independent parameters). The Δχ-tensor components and the metal coordinates are initially unknown and must be redetermined each time the PCS score c is evaluated. c is calculated over all metal ions Mj as (4.3) where PCSi calc (Mj) and PCSi exp (Mj) are the calculated and experimental PCS values of spin i induced by the metal ion Mj, respectively. The determination of the Δχ-tensor components and the
4.5 Materials and Methods. 113 metal coordinates presents a non-linear least square fitting problem. In order to avoid local minima and speed up the calculation, we split the problem into its linear and non-linear part. Equation (4.2) shows that PCSi calc is linear with respect to the five Δχ-tensor components. Using a three- dimensional grid search over the Cartesian coordinates xM, yM, zM of the paramagnetic centre, singular value decomposition optimizes the five Δχ-tensor parameters efficiently and without ambiguity for lowest residual score c at each node of the grid. The grid node with the lowest c score is then used as the starting point for optimization of the three metal coordinates along with the five Δχ-tensor components to reach the minimal cost c. The PCS score was added to the ROSETTA low resolution energy function using a different weighting factor w(c) for each structure calculation. w(c) was determined by first generating 1000 decoys with ROSETTA and calculating w(c) as (4.4) where ahigh and alow are the average of the highest and lowest 10% of the values of the ROSETTA ab initio score, and chigh and clow are the average of the highest and lowest 10% of the values of the PCS score c upon rescoring each of the 1000 decoys with the PCS. The weights used for the ten structure calculations performed in the present work are given in SI Table S4.1. 4.5.2 PCS-ROSETTA Algorithm PCS-ROSETTA uses the ROSETTA de novo structure prediction methodology to build low resolution models, followed by all atom refinement using the ROSETTA high resolution Monte Carlo minimization protocol. The additions to the standard ROSETTA structure prediction methods are: the use of chemical shifts to guide fragment selection as in CS-ROSETTA, the use of PCS data to guide the initial low resolution search and the use of PCS data for final model selection. A flow diagram of the computational protocol of PCS-ROSETTA is shown in SI Figure S4.5. 4.5.3 Input for PCS-ROSETTA The chemical shifts of all protein targets were taken from the literature or from the BioMagResBank. CS-ROSETTA was used for fragment selection. CS-ROSETTA reports the difference between experimental and expected chemical shifts. Chemical shifts with very large deviations from expectations (often attributable to errors in the deposited data) were removed from the input. CS-ROSETTA also suggests corrections in the chemical shift referencing. We only
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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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xiii
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xv List of Abbreviations α Subunit
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2 Chapter 1. Introduction. The scal
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Thesis Title: Subtitle - NMR Spectroscopy Research Group

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