Benchmarking and analysis of protein docking performance in Rosetta v3.2.
Benchmarking and analysis of protein docking performance in Rosetta v3.2.
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DOI：
10.1371/journal.pone.0022477
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Gray JJ
中科院分区：
文献类型：
--
作者：
Chaudhury S;Berrondo M;Weitzner BD;Muthu P;Bergman H;Gray JJ
RosettaDock已越来越多地用于蛋白质对接和设计策略，以预测蛋白质-蛋白质界面的结构。在这里，我们测试RosettaDock 3.2的能力，新开发的Rosetta v3.2建模套件的一部分，对对接基准3.0，并比较它与RosettaDock v2.3，以前的Rosetta软件包的最新版本。该基准包含116个对接靶点，包括22个抗体-抗原复合物，33个酶抑制剂复合物和60个“其他”复合物。这些目标被进一步分类为84个刚体目标，17个中等目标，和14个困难的目标。我们在RosettaDock v2.3和v3.2中使用未结合结构（可用时）对每个靶标进行局部对接扰动。总体而言，RosettaDock v2.3和v3.2的性能相似。RosettaDock v3.2实现了56个对接漏斗，而v2.3中为49个。按蛋白质复合物类型划分的对接性能分析显示，RosettaDock v3.2实现了63%的抗体-抗原靶标、62%的酶抑制剂靶标和35%的“其他”靶标的对接漏斗。在对接难度方面，RosettaDock v3.2实现了58%的刚体目标，30%的中等目标和14%的困难目标的漏斗。对于失败的目标，我们进行了额外的分析，以确定失败的原因，这表明结合诱导的骨架构象变化占大多数失败。我们还提出了一个自助统计分析，量化的随机对接结果的可靠性。最后，我们证明了RosettaDock v3.2通过在较小目标集的对接中并入小分子和非蛋白质辅因子而提供的额外功能。这项研究标志着RosettaDock模块迄今为止最广泛的基准测试，并为蛋白质界面建模和结构预测的未来研究建立了基线。
RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.
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