Explanatory optimization of protein mass spectrometry via genetic search.

Optimizing experimental conditions for the effective analysis of intact proteins by mass spectrometry is challenging, as many analytical factors influence the spectral quality, often in very different ways for different proteins and especially with complex protein mixtures. We show that genetic search methods are highly effective in this kind of optimization and that it was possible in 6 generations with a total of <500 experiments out of some 10(14) to find good combinations of experimental variables (electrospray ionization mass spectral settings) that would not have been detected by optimizing each variable alone (i.e., the search space is epistatic). Moreover, by inspecting the evolution of the variables to be optimized using genetic programming, we discovered an important relationship between two of the mass spectrometer settings that accounts for much of this success. Specifically, the conditions that were evolved included very low values of skimmer 1 voltage (the sample cone) and a skimmer 2 voltage (extraction cone) above a threshold that would nevertheless minimize the potential difference between the sample and extraction skimmers. The discovery of this relationship demonstrates the hypothesis-generating ability of genetic search in optimization processes where the size of the search space means that little or no a priori knowledge of the optimal conditions is available.