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Machine Learning and Visualization in Structural Biology


Our research aims to develop computational tools that assist X-ray crystallographers in rapidly determining the three-dimensional structure of a protein from X-ray reflection data. More specifically, we address the following task: given a 3D electron-density map and the primary sequence of the protein that produced it, find the most likely layout (i.e, "trace") of the protein structure. We develop both automated methods based on statistical machine-learning and computer-vision techniques, as well as visualization tools that support humans doing this layout. Our methods are especially well-suited to high-throughput, protein-structure determination; being able to accurately interpret lower-quality X-ray data promises to allow higher-throughput structure determination at lower cost.


Our work is funded by grant 5-R01-LM008796-03 from the National Library of Medicine.


Our research has developed ACMI (Automatic Crystallographic Map Interpretation), a probabilistic method for automatically interpreting low-resolution electron-density maps. ACMI is available for download here.


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Computer Sciences Department
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University of Wisconsin - Madison


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