Kreshna Gopal, Reetal Pai, Thomas R. Ioerger, Tod D. Romo, and James C. Sacchettini
X-ray crystallography is the most widely used method for determining the three-dimensional structures of proteins and other macromolecules. One of the most difficult steps in crystallography is interpreting the 3D image of the electron density cloud surrounding the protein. This is often done manually by crystallographers and is very time-consuming and error-prone. The difficulties stem from the fact that the domain knowledge required for interpreting electron density data is uncertain. Thus crystallographers often have to resort to intuitions and heuristics for decision-making. The problem is compounded by the fact that in most cases, data available is noisy and blurred. TEXTAL is a system designed to automate this challenging process of inferring the atomic structure of proteins from electron density data. It uses a variety of AI and pattern recognition techniques to try to capture and mimic the intuitive decision-making processes of experts in solving protein structures. The system has been quite successful in determining various protein structures, even with average quality data. The initial structure built by TEXTAL can be used for subsequent manual refinement by a crystallographer, and combined with post-processing routines to generate a more complete model.