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Protein structure prediction of CASP5 comparative modeling and fold recognition targets using consensus alignment approach and 3D assessment.

机译:使用共有序列比对方法和3D评估,对CASP5比较模型和折叠识别目标进行蛋白质结构预测。

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摘要

For the fifth round of Critical Assessment of Techniques for Protein Structure Prediction (CASP5) all comparative modeling (CM) and fold recognition (FR) target proteins were modeled using a combination of consensus alignment strategy and 3D assessment. A large number and broad variety of prediction targets, with sequence identity between each modeled domain and the related known structure, ranging from 6 to 49%, represented all difficulty levels in comparative modeling and fold recognition. The critical steps in modeling, selection of template(s) and generation of sequence-to-structure alignment, were based on the results of secondary structure prediction and tertiary fold recognition carried out using the Meta Server coupled with the 3D-Jury system. The main idea behind the modeling procedure was to select the most common alignment variants provided by individual servers, as well as to generate several alternatives for questionable regions and to evaluate them in 3D by building corresponding molecular models. Analysis of fold-specific features and sequence conservation patterns for the target family was also widely used at this stage. For both CM and FR targets remote homologs of known structure were clearly recognized by the 3D-Jury system. In the analogous fold recognition subcategory, the correct fold was identified for five out of eight domains. The average alignment accuracy for FR models (48%) was far less than for CM predictions (80%). These finding, coupled with the observation that in the majority of cases the submitted models were not closer to the experimental structure than their best templates, indicate that, especially for difficult targets, there is still ample room for improvement.
机译:对于蛋白质结构预测技术的第五轮关键评估(CASP5),使用共有比对策略和3D评估相结合,对所有比较模型(CM)和折叠识别(FR)目标蛋白进行了建模。大量的预测目标,每个建模域与相关已知结构之间的序列同一性范围为6%至49%,代表了比较建模和折叠识别中的所有难度级别。建模,模板选择和序列与结构比对生成的关键步骤基于使用Meta Server和3D-Jury系统进行的二级结构预测和三级折叠识别的结果。建模过程背后的主要思想是选择单个服务器提供的最常见的比对变体,并为可疑区域生成几种替代方案,并通过建立相应的分子模型以3D评估它们。在这一阶段,针对靶家族的折叠特异性特征和序列保守模式的分析也被广泛使用。对于CM和FR目标,3D-Jury系统可以清楚地识别出已知结构的远程同源物。在类似的折叠识别子类别中,为八个域中的五个确定了正确的折叠。 FR模型的平均对准精度(48%)远小于CM预测的精度(80%)。这些发现以及在大多数情况下所提交的模型都没有比其最佳模板更接近实验结构的观察结果表明,尤其是对于困难的目标,仍有足够的改进空间。

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