Deane C M, Blundell T L
Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, United Kingdom.
Protein Sci. 2001 Mar;10(3):599-612. doi: 10.1110/ps.37601.
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 A for three residue loops to 3.5 A 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 0.76 A for three residue loops to 3.09 A 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.
CODA是一种预测蛋白质可变区的算法，它结合了基于知识的方法FREAD和从头构建区域的PETRA。FREAD从具有环境约束替换表和其他基于规则的过滤器的蛋白质结构片段数据库中进行选择。FREAD经过参数化处理，并在3000多个环上进行了测试。在一个非同源测试集上，三个残基环的平均均方根偏差范围为0.78埃，八个残基环的平均均方根偏差范围为3.5埃。CODA对两个独立程序的预测结果进行聚类，并做出必须通过一组基于规则的过滤器的一致性预测。CODA在两组彼此不相关且与FREAD数据库中结构不同源的独立结构上进行了参数化处理和测试。测试集中的平均均方根偏差范围为，三个残基环为0.76埃，八个残基环为3.09埃。与单独的PETRA和FREAD相比，CODA在环预测方面总体上有改进。对于六个及以上的长度，这种改进更为明显，这可能是因为PETRA的预测能力变得更加重要。CODA在几个模型结构上进一步进行了测试，以确定其对建模情况的适用性。可通过http://www-cryst.bioc.cam.ac.uk/~charlotte/Coda/search_coda.html访问CODA的网络服务器。
