The most complicated lattice model used mers with 20 discrete values derived from those of real amino-acid residues. The more complex model had only a single transition state. Both these behaviors are observed in the folding of some small natural protein structures. Lattice models have revealed unexpected regularities in the folding pathways of protein-like structures. By combining theoretical and computational approaches, such as lattice models, with data from experiments, physical chemist Teresa Head-Gordon of the Physical Biosciences Division and her colleagues have detailed water's essential role in driving protein folding and stabilization.
Many proteins have a hydrophobic core and a hydrophilic surface.
They conjectured that these water structures, much more highly ordered than water in bulk, give rise to forces that differ among different kinds of amino acids and thus influence folding pathways. When Head-Gordon and her colleagues applied what they had learned from scattering experiments to lattice models of polymers, they found that by including accurate solvation forces they could go a long way toward making the models more realistic mimics of actual proteins. Some models were swiftly eliminated, and the performance of others was improved to exhibit faster folding and more cooperative folding transitions. An illustration of the advanced state of computational modelling Simple theoretical models bolstered by experimental data are one approach to faster protein-structure prediction. Another way to use computers to translate dna sequences into protein structures is to work directly from a growing library of known folds.