| Autor: |
Tod D. Romo, Erik McKee, Jacob Smith, Lalji Kanbi, Kreshna Gopal, Reetal Pai, James C. Sacchettini, T. loerger |
| Rok vydání: |
2005 |
| Předmět: |
|
| Zdroj: |
IEEE Intelligent Systems. 20:59-63 |
| ISSN: |
1541-1672 |
| DOI: |
10.1109/mis.2005.114 |
| Popis: |
TEXTAL is a successfully deployed system for automated model-building in protein X-ray crystallography. It represents a novel solution to an important, complex real-world, problem using various AI and pattern recognition algorithms. TEXTAL takes a model-building approach based on real-space density pattern recognition, similar to how a human crystallographer would work. TEXTAL first tries to predict the coordinates of the alpha-carbon (C/spl alpha/) atoms in the protein's connected backbone using a neural network. It then analyzes the density patterns around each C/spl alpha/ atom and searches a database of previously solved structures for regions with similar patterns. TEXTAL determines the best match, retrieves the coordinates for that region, and fits them to the unknown density. TEXTAL concatenates these local models into a global model and subjects them to various subsequent refinements to produce a complete protein model automatically. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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