Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
Bioinformatics. 2011 Jun 15;27(12):1715-6. doi: 10.1093/bioinformatics/btr268. Epub 2011 May 5.
We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure.
http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.
我们构建了一个名为 APOLLO 的网络服务器，它可以使用机器学习方法评估单个蛋白质模型的绝对全局和局部质量，或者使用两两比较方法评估模型池的全局和局部质量。基于我们对 107 个 CASP9（蛋白质结构预测技术的关键评估）目标的评估，我们的机器学习和两两方法生成的预测质量得分与真实模型质量得分的平均每目标相关性分别为 0.671 和 0.917。基于我们对 92 个 CASP9 目标的测试，我们预测的绝对局部质量与实际距离天然结构的平均差异为 2.60 Å。
http://sysbio.rnet.missouri.edu/apollo/。该网站还提供单人和成对的全局质量评估软件。
