TEXTAL - Crystallographic protein model building using AI and pattern recognition Academic Article uri icon


  • 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. Copyright 2006, American Association for Artificial Intelligence. All rights reserved.

published proceedings


author list (cited authors)

  • Gopal, K., Romo, T. D., McKee, E. W., Pai, R., Smith, J. N., Sacchettini, J. C., & Ioerger, T. R.

complete list of authors

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

publication date

  • September 2006