High throughput crystallography à la SGC

High throughput crystallography à la SGC

28 March 2007 Bootstrap in refinement Gbor Bunkczi Bootstrap - basics Statistical method for estimating the sample distribution of an estimator. Procedure: 1. Given an estimator () and a sample. 2. Create a new sample by resampling the original sample WITH replacement. 3. Calculate the estimator, store the value. 4. Repeat 1-3 Nboot times (> 1000).

Computationally demanding! Bootstrap - aims Model validation 1. R-factor distribution 2. Coordinate errors Map improvement 1. Bias removal 2. Resolution extension Bootstrap - algorithm

Sample: Fo-Fc normalised in each resolution shell by Resampling: 1. Generate Fnorm = (Fo-Fc) / Norm 2. Randomise: Fnorm Fnorm, random 3. Calculate Fo = Fc + Fnorm, random * Norm Refinement: 1. Model randomisation 2. Refinement on bootstrap data 3. Calculate R/Rfree on original data

Accumulation: 1. R-factors Bootstrap - implementation START: Model, Dataset Initial refinement to calculate normalisation factor Generate bootstrap datasets Refinement Refinement

Refinement Extract data from log files Accumulate map coefficient END: R-factor distribution, multiple models Bootstrap results R-factors: - distributions very tight

- further randomisation increases absolute value but does not make the distribution broader Coordinates: Bootstrap development 1. Resample residuals

resample likelihood P(Fo, Fc) 2. Resample F resample difference map 3. Improved normalisation

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