Simulations of Calcium Selectivity and Binding

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Channels are proteins with holes down their middle that control an enormous range of biological function in health and disease by controlling movement of charged atoms (ions) across otherwise insulating membranes. Ions are charged spheres that move through channels by diffusion and drift in the electric field. Open channels allow membranes to select between different kinds of ions: selectivity is a 'defining feature' of life, at least in textbooks. Channel structure does not change once they are open and so we can try to understand and control selectivity of channels using the language and mathematics of physical science, without addressingspecial properties of proteins or their conformation changes. Channels have large amounts of permanent electrical charge on their walls, created by the natural charge on the amino acids forming the protein. The permanent charge must be accompanied by (nearly) equal amounts of opposite mobile charge. Ions and channels are inseparable, according to a basic law of electricity, called 'the principle of electroneutrality'. The number density (i.e., concentration) of ions in channels is very high, often -20 M (pure water is -55 M), so it is logical to think of ions in channels the way physical chemists think of ions in concentrated solutions. Surprisingly, such simple theories acccount for many complex highly selective properties of calcium channels without invoking other special forces that might be present. Evolution seems to use crowded charge to produce selectivity, more than anything else. We propose to study highly selective calcium channels with simulations of real proteins that contain crowded charge. We will use proteins synthesized to have crowded charge and compute the selectivity of these channels with several different methods, comparing the results with previous work using less refined models of the system. We will use these computations to design highly selective Ca channels of medical and technological interest. The simulations will suggest what needs to be improved in theory and design.