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Model lipid membranes made from binary mixtures of dimyristoylphosphatidylcholine/dipalmitoylphosphatidylcholine (DMPC/DPPC) and dimyristoylphosphatidylcholine/cholesterol (DMPC/Chol) exhibit coexistence of diverse lipid phases at appropriate temperature and composition. Since lipids in different phases show different structural and motional properties, it is expected that the corresponding spin probe electron paramagnetic resonance (EPR) spectra will be superposition of several spectral components. From comparison of proportions of spectral components of the EPR spectrum with the fractions of the corresponding lipid phases obtained from known phase diagrams the partition coefficient of spin probe methyl ester of 5-doxyl palmitate between different lipid phases was determined. The results indicate that the used spin probe partitions approximately equally between different phases.

Complexity of biological systems is one of the toughest problems for any experimental technique. Complex biochemical composition and a variety of biophysical interactions governing the evolution of a state of a biological system imply that the experimental response of the system would be superimposed of many different responses. To obtain a reliable characterization of such a system based on spin-label Electron Paramagnetic Resonance (EPR) spectroscopy, multiple Hybrid Evolutionary Optimization (HEO) combined with spectral simulation can be applied. Implemented as the GHOST algorithm this approach is capable of handling the huge solution space and provides an insight into the “quasicontinuous” distribution of parameters that describe the biophysical properties of an experimental system. However, the analysis procedure requires several hundreds of runs of the evolutionary optimization routine making this algorithm extremely computationally demanding. As only the best parameter sets from each run are assumed to contribute into the final solution, this algorithm appears far from being optimized. The goal of this study is to modify the optimization routine in a way that 20−40 runs would be enough to obtain qualitatively the same characterization. However, to keep the solution diversity throughout the HEO run, fitness sharing and newly developed shaking mechanisms are applied and tested on various test EPR spectra. In addition, other evolutionary optimization parameters such as population size and probability of genetic operators were also varied to tune the algorithm. According to the testing examples a speed-up factor of 5−7 was achieved.

Molecular modeling based on a hybrid evolutionary optimization and an information condensation algorithm, called GHOST, of spin label ESR spectra was applied to study the structure and dynamics of membrane proteins. The new method is capable of providing detailed molecular information about the conformational space of the spin-labeled segment of the protein in a membrane system. The method is applied to spin-labeled bacteriophage M13 major coat protein, which is used as a model membrane protein. Single cysteine mutants of the coat protein were labeled with nitroxide spin labels and incorporated in 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) bilayers. The new computational method allows us to monitor distributions of local spatial constraints and molecular mobility, in addition to information about the location of the protein in a membrane. Furthermore, the results suggest that different local conformations may coexist in the membrane protein. The knowledge of different local conformations may help us to better understand the function−structure relationship of membrane proteins.

Following the widely spread EPR spin-label applications for biosystem characterization, a novel approach is proposed for EPR-based characterization of biosystem complexity. Hereto a computational method based on a hybrid evolutionary optimization (HEO) is introduced. The enormous volume of information obtained from multiple HEO runs is reduced with a novel so-called GHOST condensation method for automatic detection of the degree of system complexity through the construction of two-dimensional solution distributions. The GHOST method shows the ability of automatic quantitative characterization of groups of solutions, e.g. the determination of average spectral parameters and group contributions. The application of the GHOST condensation algorithm is demonstrated on four synthetic examples of different complexity and applied to two physiologically relevant examples − the determination of domains in biomembranes (lateral heterogeneity) and the study of the low-resolution structure of membrane proteins.