Sippl M J, Lackner P, Domingues F S, Prlić A, Malik R, Andreeva A, Wiederstein M
Center for Applied Molecular Engineering, Institute for Chemistry and Biochemistry, University of Salzburg, Salzburg, Austria.
Proteins. 2001;Suppl 5:55-67. doi: 10.1002/prot.10006.
We present the assessment of the CASP4 fold recognition category. The tasks we had to execute include the splitting of multidomain targets into single domains, the classification of target domains in terms of prediction categories, the numerical evaluation of predictions, the mapping of numerical scores to quality indices, the ranking of predictors, the selection of top-performing groups, and the analysis and critical discussion of the state of the art in this field. The 125 fold recognition groups were assessed by a total score that summarizes their performance over all targets and a quality score reflecting the average quality of the submitted models. Most of the top-performing groups achieved respectable results on both scores simultaneously. Several groups submitted models that were much closer to the respective target structures than any of the known folds in the Protein Data Bank. The CASP4 assessment included the automated servers of the parallel CAFASP experiment. For the total score, the highest rank achieved by a fully automated server is 12. Two thirds of the predictors have rather low scores.
我们展示了对CASP4折叠识别类别的评估。我们必须执行的任务包括将多结构域目标拆分为单个结构域、根据预测类别对目标结构域进行分类、对预测进行数值评估、将数值分数映射到质量指标、对预测器进行排名、选择表现最佳的组，以及对该领域的技术现状进行分析和批判性讨论。125个折叠识别组通过一个总分进行评估，该总分总结了它们在所有目标上的表现，还有一个质量分数反映了提交模型的平均质量。大多数表现最佳的组在这两个分数上都同时取得了不错的成绩。有几个组提交的模型比蛋白质数据库中任何已知折叠都更接近各自的目标结构。CASP4评估包括并行CAFASP实验的自动化服务器。就总分而言，一个全自动服务器取得的最高排名是第12名。三分之二的预测器得分相当低。
