CODA: A combined algorithm for predicting the structurally variable regions of protein models
Article first published online: 31 DEC 2008
Copyright © 2001 The Protein Society
Volume 10, Issue 3, pages 599–612, March 2001
How to Cite
Deane, C. M. and Blundell, T. L. (2001), CODA: A combined algorithm for predicting the structurally variable regions of protein models. Protein Science, 10: 599–612. doi: 10.1110/ps.37601
- Issue published online: 31 DEC 2008
- Article first published online: 31 DEC 2008
- Manuscript Accepted: 4 DEC 2000
- Manuscript Revised: 16 NOV 2000
- Manuscript Received: 6 SEP 2000
- substitution tables;
CODA, an algorithm for predicting the variable regions in proteins, combines FREAD a knowledge based approach, and PETRA, which constructs the region ab initio. FREAD selects from a database of protein structure fragments with environmentally constrained substitution tables and other rule-based filters. FREAD was parameterized and tested on over 3000 loops. The average root mean square deviation ranged from 0.78 Å for three residue loops to 3.5 Å for eight residue loops on a nonhomologous test set. CODA clusters the predictions from the two independent programs and makes a consensus prediction that must pass a set of rule-based filters. CODA was parameterized and tested on two unrelated separate sets of structures that were nonhomologous to one another and those found in the FREAD database. The average root mean square deviation in the test set ranged from 0.76Å for three residue loops to 3.09 Å for eight residue loops. CODA shows a general improvement in loop prediction over PETRA and FREAD individually. The improvement is far more marked for lengths six and upward, probably as the predictive power of PETRA becomes more important.
CODA was further tested on several model structures to determine its applicability to the modeling situation. A web server of CODA is available at http://www-cryst.bioc.cam.ac.uk/∼charlotte/Coda/search_coda.html.