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在蛋白质结构预测关键评估第10轮(CASP10)和蛋白质结构预测连续评估(ROLL)中对无模板建模的评估。

Assessment of template-free modeling in CASP10 and ROLL.

作者信息

Tai Chin-Hsien, Bai Hongjun, Taylor Todd J, Lee Byungkook

机构信息

Laboratory of Molecular Biology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892.

出版信息

Proteins. 2014 Feb;82 Suppl 2(Suppl 2):57-83. doi: 10.1002/prot.24470. Epub 2013 Dec 17.

Abstract

We present the assessment of predictions for Template-Free Modeling in CASP10 and a report on the first ROLL experiment wherein predictions are collected year round for review at the regular CASP season. Models were first clustered so that duplicated or very similar ones were grouped together and represented by one model in the cluster. The representatives were then compared with targets using GDT_TS, QCS, and three additional superposition-independent score functions newly developed for CASP10. For each target, the top 15 representatives by each score were pooled to form the Top15Union set. All models in this set were visually inspected by four of us independently using the new plugin, EvalScore, which we developed with the UCSF Chimera group. The best models were selected for each target after extensive debate among the four examiners. Groups were ranked by the number of targets (hits) for which a group's model was selected as one of the best models. The Keasar group had most hits in both categories, with four of 19 FM and eight of 36 ROLL targets. The most successful prediction servers were QUARK from Zhang's group for FM category with three hits and Zhang-server for the ROLL category with seven hits. As observed in CASP9, many successful groups were not true "template-free" modelers but used remote templates and/or server models to obtain their winning models. The results of the first ROLL experiment were broadly similar to those of the CASP10 FM exercise.

摘要

我们展示了对CASP10中无模板建模预测的评估,以及关于首次ROLL实验的报告,在该实验中,全年收集预测结果,以便在常规的CASP赛季进行评审。首先对模型进行聚类,以便将重复或非常相似的模型归为一组,并由该组中的一个模型代表。然后使用GDT_TS、QCS以及为CASP10新开发的另外三个与叠加无关的评分函数,将这些代表模型与目标进行比较。对于每个目标,将每个评分中排名前15的代表模型汇总,形成Top15Union集。我们四人使用与UCSF Chimera团队共同开发的新插件EvalScore,对该集合中的所有模型进行了独立的视觉检查。在四位评审员进行广泛讨论后,为每个目标选出了最佳模型。根据一个团队的模型被选为最佳模型的目标数量(命中数)对各团队进行排名。Keasar团队在两个类别中命中数最多,在19个FM目标中有4个命中,在36个ROLL目标中有8个命中。最成功的预测服务器在FM类别中是Zhang团队的QUARK,有3次命中;在ROLL类别中是Zhang-server,有7次命中。正如在CASP9中所观察到的,许多成功的团队并非真正的“无模板”建模者,而是使用远程模板和/或服务器模型来获得其获胜模型。首次ROLL实验的结果与CASP10的FM练习结果大致相似。

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