This project's main objective is to develop computerized tools that assist x-ray crystallographers in rapidly determining the three-dimensional structure of a protein. More specifically, this project addresses the following task: given a 3D electron-density map from crystallography and the sequence of the protein, find the most likely layout (i.e. """"""""trace"""""""") of the protein sequence in 3D. The project will create both automated methods based on statistical machine-learning and computer-vision techniques, as well as visualization tools that support humans doing this layout. These two approaches complement each other and are synergistic. This project's first specific aim is to develop and empirically evaluate algorithms that interpret crystallographic electron-density maps. The second specific aim is to incorporate structural-biology domain knowledge (secondary-structure prediction and potential-energy calculations) into the project's algorithms for interpreting density maps. The third specific aim is to tightly integrate partial model-construction with phase estimation updates to improve the recognition of 3D protein structures in x-ray reflection data;crystallographers will be able to intervene whenever they desire to help """"""""steer"""""""" this iterative process. The final specific aim is to develop intuitive and effective modalities - including virtual reality and the use of speech/audio - for the efficient use of crystallographer's time in manual model fitting and validation. Structural biology has wide relevance to biomedicine, since protein function generally follows from protein form (i.e., its structure). This project's techniques will speed-up the process of determining protein 3D structures, especially from low-quality (i.e., low-resolution) x-ray data, and will be applicable to other structural-biology tasks. Being able to accurately interpret low-resolution data promises to allow higher through put structure determination. The broader impact will include a better understanding of the power of modern theories and algorithms in machine learning and visualization in solving biological problems.
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