TEXTAL: Crystallographic Protein Model Building Using AI and Pattern Recognition
AbstractTEXTAL is a computer program that automatically interprets electron density maps to determine the atomic structures of proteins through X-ray crystallography. Electron density maps are traditionally interpreted by visually fitting atoms into density patterns. This manual process can be time-consuming and error prone, even for expert crystallographers. Noise in the data and limited resolution make map interpretation challenging. To automate the process, TEXTAL employs a variety of AI and pattern-recognition techniques that emulate the decision-making processes of domain experts. In this article, we discuss the various ways AI technology is used in TEXTAL, including neural networks, case-based reasoning, nearest neighbor learning and linear discriminant analysis. The AI and pattern-recognition approaches have proven to be effective for building protein models even with medium resolution data. TEXTAL is a successfully deployed application; it is being used in more than 100 crystallography labs from 20 countries.
How to Cite
Gopal, K., Romo, T. D., McKee, E. W., Pai, R., Smith, J. N., Sacchettini, J. C., & Ioerger, T. R. (2006). TEXTAL: Crystallographic Protein Model Building Using AI and Pattern Recognition . AI Magazine, 27(3), 15. https://doi.org/10.1609/aimag.v27i3.1889
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