A Simplified Potential for Protein Folding Simulations

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DESCRIPTION (provided by applicant): The functioning of living organisms is largely dependent on the fact that each of its constituent proteins adopts a unique structure (the so-called native structure) under physiological conditions; this structure is determined by amino-acid sequence and its environment. According to Anfinsen's thermodynamic hypothesis, the native conformation of a protein is a global-energy minimum on its free-energy hypersurface. The native structure can therefore be sought as the global minimum in this hypersurface, if an accurate potential energy function is available. The shape of the free-energy hypersurface of proteins is very complex and difficult to describe. For efficiency reasons, simplified models of polypeptide chains, in which each amino-acid residue is represented by one or a few interaction sites rather than all-atom resolution models, must be used. While our previous focus was on global optimization methods, it is now focused on our physics-based united-residue UNRES potential-energy function. The main goal is to predict the structure of proteins of all major structural classes (alpha, beta, alpha+beta and alpha/beta) with chain lengths of up to 200 amino-acid residues within 4-6 Angstrom root mean square deviation based solely on global optimization of the potential energy. This will be accomplished by improving the functional forms and parameters of individual energy components and assuring the folding property of the potential by optimizing the total energy function to reflect the energetic hierarchy of partially unfolded structures. Understanding of the role of physical interactions in the formation of the native structur e of the protein will enable us not only to predict the final structure of the protein based only on knowledge of the amino-acid sequence but can be used to study protein folding or misfolding processes. The ability to predict three-dimensional structures of proteins or to predict their folding pathways can greatly contribute to rational drug design against cancer, Alzheimer or prion deseases.