Oliver Brock, Ph.D. - US grants

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.

[unreadable] DESCRIPTION (provided by applicant): Protein structure prediction is one of the great challenges in structural biology. The ability to accurately predict the three-dimensional structure of proteins would bring about significant scientific advances and would facilitate finding cures and treatments for many diseases. We propose a novel computational framework for protein structure prediction. The novelty of the framework lies in its approach to conformation space search. Conformation space search is considered to be the primary bottleneck towards consistent, high-resolution prediction. The proposed approach to conformation space search represents a major conceptual shift in protein structure prediction, made possible by combining insights and algorithms from robotics and machine learning with techniques from molecular biology in an innovative manner. The key innovation comes from the insight that target-specific information can effectively guide conformation space search towards biologically relevant regions. We propose to develop a framework for protein structure prediction that achieves biological accuracy and computational efficiency by guiding conformation space search using target-specific information. The proposed framework exploits two sources of target-specific information: 1) information about the characteristics of the target's energy landscape acquired continuously during search, and 2) spatial restraints about the target's structure obtained from NMR experiments. As search progresses, the continuous integration of these sources of information will tailor conformation space search to the particular characteristics of the target. This tailored conformation space exploration can overcome the current bottleneck, yielding highly accurate and efficient structure prediction. The ability to determine the three-dimensional structures of proteins, which represent the molecular machinery inside every cell, would greatly facilitate finding cures or treatments for many diseases. This research effort will develop of a novel, efficient, and biologically accurate computational approach to determine the three-dimensional structure of proteins. [unreadable] [unreadable] [unreadable]

This proposal will enable US students to attend an important international forum for intellectual exchange in the robotics community, namely the fourth Robotics: Science and Systems conference. Exposure to the state-of-the-art in global research and to the leading ideas in the field is important for the training and education of tomorrow?s leading scientists. The single-track structure of this forum will enable students to personally interact and establish connections with the international leaders in the field. Attendance at this conference can be expected to positively affect the research of many US students. The exposure to state-of-the-art research and interactions with world leaders in robotics will be inspirational and provide new intellectual impulses to the research conducted at academic institutions in the US.