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Ons) with highest Zmutations had been then made use of to calculate Z-scores for overlap in domain and loop movements. As our goal should be to receive a parameter set that combines low RMSE and higher overlap, we ranked the 1000 parameter sets according to Z Zmutations z Zdomains zZloops . The parameter set with highest Z is (a1 ,a2 ,a3 ,a4 ) (102 ,104 ,104 ,10{2 ) (solid black line in Figure 13). The optimization of the bootstrapped median is equivalent to a training procedure with leave-many-out testing. Given the dichotomy in predicting the effect of mutations and overlap on the one hand and b-factors on the other, we provide, the following is the best parameter set observed for the prediction of b-factors (a1 ,a2 ,a3 ,a4 ) (103 ,105 ,103 ,102 ) with average b-factor correlation of 0:61+0:13. The exploration of parameter space shows that there is a clear trade-off between the prediction of mutations (low RMSE), conformational sampling (high overlap) and b-factors (high correlations). Parameter sets that improve the prediction of bfactors are invariably associated with poor conformational changes (low overlap) associated to both domain and loop movements and variable RMSE for the prediction of mutations (red lines inPLOS Computational Biology | www.ploscompbiol.orgFigure 13). On the other hand, parameter sets that predict poorly b-factors, perform better in the prediction of conformational changes and the effect of mutations (blue lines in Figure 13). The a parameters used in STeM, 1 ,a2 ,a3 ,a4 60,72,9:9,3:6are arbitrary, taken without modifications from a previous study focusing on folding [77]. As expected, this set of parameters can be considerably improved upon as can be observed in Figure 13 (dashed line). The four right-most variables in the parallel coordinates plot in Figure 13 show the logarithm of the a parameters for each parameter set. Either class of parameter sets, better for b-factors (in red) or better for overlap/RMSE (in blue) come about from widely diverging values for each parameter across several orders of magnitude. There are however some patterns. Most notably for a4, where there is an almost perfect separation of parameter sets around a4 = 1. Interestingly, higher values of a1 and a2, associated with stronger constraints on distances and angles tend also to be associated to PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/20168320 better overlap values. While it is likely that a betterperforming set of parameters can be found, the wide variation of values across many orders of magnitude show that within certain limits, the method is robust with respect to the choice of parameters. This result justifies the sparse search employed.BootstrappingBootstrapping is a simple and general statistical technique to estimate standard errors, p-values, and other quantities associated with finite samples of unknown distributions. In particular, bootstrapping help mitigate the effect of outliers and offers betterENCoM: Atomic Contact Normal Mode Analysis MethodFigure 13. Performance of different parameter sets on the prediction of mutations, b-factors and motions. We present as a parallel plot the bootstrapped median RMSE for stabilizing and destabilizing mutation, average best overlap for domains and loop movements as well as purchase mDPR-Val-Cit-PAB-MMAE selfconsistency bias and errors. In the right-most four columns with include the logarithm of the 4 alpha variables. Different parameter sets are colored based on b-factors correlations (red gradient) or domain movement overlaps (blue gradient). The black line repre.