Dr. Thomas R. Ioerger
Dr. Thomas R. Ioerger is an Assistant Professor in the Department of Computer Science at Texas A&M University. He received his B.S. degree in 1989 from Penn State in the area of Molecular and Cellular Biology. He received his Ph.D. in Computer Science from the University of Illinois in 1996, where he held an NSF Graduate Fellowship. Dr. Ioerger's research interests are in the areas of Artificial Intelligence, Intelligent Agents, and Machine Learning. His work has covered diverse areas, from spatial reasoning, to simulating team-work, to modeling emotions. Currently, his primary focus is on designing automated methods for detecting feature interactions in data, and constructing new features to enhance the performance of learning algorithms. He is also involved in applying AI and machine learning methods to various problems in the area of Bioinformatics, including the improvement of protein sequence alignments, molecular modeling, and X-ray crystallography. Dr. Ioerger is a member of the American Association for Artificial Intelligence (AAAI) and the International Society for Computational Biology (ISCB).
Proteins are important macromolecules that serve a wide variety of biological functions in cells. Knowledge of the structures of proteins is useful for elucidating their mechanisms, determining their evolutionary relationships, understanding the molecular basis of disease, and designing drugs (e.g. inhibitors) that interact with them. One of the most important methods for determining the structures of proteins in a laboratory is X-ray crystallography. Crystallography has many complex steps which culminate in the production of a 3D map of "cloud-like" electron density representing the protein. However, the final phase of interpreting this electron density map and building a 3D model of the protein structure (coordinates of atoms) still remains one of the most challenging steps to automate, making it a major impediment to progress in structural biology.
We have developed a system called TEXTAL that uses pattern recognition and other AI methods to automatically interpret electron density maps and thereby solve protein structures. Given a spherical region of density in a map to be solved, TEXTAL uses a nearest-neighbor algorithm to look up regions with similar patterns of electron density in a database of previously solved maps. This procedure crucially relies on the extraction of rotation-invariant features that help characterize and match these patterns of density. Then atomic coordinates from the known structures in matched regions are rotated and translated into position to incrementally build the model for the new map. TEXTAL has already been demonstrated to build fairly accurate models from real electron density maps without human intervention, and many further improvements are currently being investigated. Our experiments with TEXTAL validate the utility of pattern recognition for solving protein structures by interpreting electron density maps, and have revealed a number of related opportunities for using AI methods to incorporate human expertise into a system for automating this complex task.
Everyone is invited and welcome to attend the seminars in this series.