Proteins are the basic building blocks of life. They perform many important functions within the cells of each living being. These functions include signaling, metabolism, transport, and reproduction. A protein performs its function by interacting with other proteins or molecules inside the cell. Such an interaction may result in the binding of two molecules to form a complex, or in the separation of such a complex into its components. During the interactions among proteins, each molecule may have to change its three-dimensional shape in order to accommodate the binding with another molecule. This is possible because many proteins are inherently flexible. It is generally accepted that the three-dimensional shape of a protein and its ability to change its shape uniquely determine the protein's biological function.
An understanding of the biological function of proteins in the cell would allow a detailed understanding of the complex processes that happen inside a cell. Such an understanding would also facilitate the design of new drugs to influence these processes, should they be affected in the case of a disease. To gain such an understanding from biological experiments alone is very costly and time-consuming. The ability to accurately simulate interactions among flexible biomolecules therefore promises to facilitate scientific advances in computational drug design and represents an important computational tool to expand our understanding of cellular processes. As a significant contribution towards this objective, the investigators propose to develop a novel computational framework for computationally efficient and biologically accurate docking of flexible proteins.
The problem of protein docking is computationally challenging, even under the simplifying assumption that both bodies are internally rigid. However, ignoring the internal flexibility of a protein is recognized as a shortcoming in current docking approaches. The proposed algorithmic framework uses methods from robotics to effectively analyze and model the internal flexibility of a protein. Based on this analysis, conformational changes occurring during the docking process can be accommodated effectively. The resulting computational framework can be seen as a new algorithmic foundation for efficient and biologically accurate computational docking of flexible proteins.
