Secondary protein structure optimization

All the methods developed so far try to extract information, directly or indirectly (Lim, 1974), from the ever growing databases of X-ray crystallography resolved protein structures. Unfortunately, the rate at which new structures are added to the structure databases is far from optimal. Chothia (1992) estimated that all proteins, when their structures are known, would fall into about one thousand folding classes, more than half of them yet to be discovered. If so, this means that a great deal of information in the forthcoming structures is not available for the current methods, and therefore we still must rely on the future to see a coherent and realistic increase in the accuracy of secondary structure prediction methods. 

Side chain placement In proteins, the a-helix and P-sheet places the chemical groups of amino acid side chains on the surface of the secondary structures, optimally positioned for tertiary structural interactions, whereas the secondary structure of RNA places the chemical groups of nucleotides in the interior of the A-form duplex largely inaccessible for tertiary strueture formation. 

GA s have received much attention in recent years. In chemistry, they can be used for a search in conformational space which very often involves combinations of many parameters. In particular, there were several attempts to use them for protein structure prediction (reviewed in 48). Recently, GA s were suggested to use in three rather different aspects of RNA structurerconformational search for stem-loop structures 49), prediction of optimal and suboptimal secondary structures 50) and simulation of RNA folding pathways 44,45). In the case of RNA folding simulation, a GA is also very attractive because it allows to simulate the process, in addition to obtaining a final solution. 

Natural proteins have been selected not only to fold into stable structures, but to do so under various conditions and in reasonable time. More obviously, they have been selected for particular functions that often require hydrophobic surface groups and nonregular secondary structures. Finally, most extant proteins have been indirectly selected to be insensitive to most mutations. We note that those early proteins that could not accept numerous mutations without loss of function or structure would not adapt to changing conditions or functional requirements. All of these factors mean that natural proteins are unlikely to fit any single optimization concept. 

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