TEXTAL: Crystallographic Protein Model Building Using AI and Pattern Recognition

Kreshna Gopal, Tod D. Romo, Erik W. McKee, Reetal Pai, Jacob N. Smith, James C. Sacchettini, Thomas R. Ioerger

Abstract


TEXTAL 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.

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DOI: http://dx.doi.org/10.1609/aimag.v27i3.1889

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