Christodoulos A. Floudas
Proteins serve as vital components in our cellular makeup and perform many biological functions that are essential for sustaining life. An important feature which determines the functionality of a protein is the form of its three-dimensional structure. The structure, in turn, is related to the protein sequences encoded by our genes, and these sequences have recently been identified as part of the data from the human genome project. Therefore, a logical undertaking upon completion of the human genome project, and an important step in understanding and treating disease, would be to develop a method to predict the structure of a protein given its sequence information.
Accurate prediction of the three-dimensional structure of a protein relies on both the mathematical model used to mimic the protein system and the technique used to identify the correct structure. Here we introduce a novel ab initio approach for the protein folding problem. The models are based solely on first principles, as opposed to the myriad of techniques relying on information from statistical databases. In addition, the search techniques rely on the foundations of deterministic global optimization, methods which can guarantee the correct identification of a protein's structure. The multistage approach begins with the identification of helical secondary structure elements, which is followed by the prediction of beta sheet and disulfide bridge configurations from a set of postulated beta strands. In the final stage the aforementioned predictions are used to derive structural restraints for the determination of the overall three-dimensional structure.