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本文引用的文献

1
GalaxyTongDock: Symmetric and asymmetric ab initio protein-protein docking web server with improved energy parameters.GalaxyTongDock:具有改进能量参数的对称和非对称从头蛋白质-蛋白质对接网络服务器。
J Comput Chem. 2019 Oct 15;40(27):2413-2417. doi: 10.1002/jcc.25874. Epub 2019 Jun 7.
2
What method to use for protein-protein docking?用于蛋白质-蛋白质对接的方法是什么?
Curr Opin Struct Biol. 2019 Apr;55:1-7. doi: 10.1016/j.sbi.2018.12.010. Epub 2019 Feb 1.
3
Evaluation of Predicted Protein-Protein Complexes by Binding Free Energy Simulations.结合自由能模拟评价预测的蛋白质-蛋白质复合物。
J Chem Theory Comput. 2019 Mar 12;15(3):2071-2086. doi: 10.1021/acs.jctc.8b01022. Epub 2019 Feb 15.
4
ComplexContact: a web server for inter-protein contact prediction using deep learning.复杂接触:一个使用深度学习进行蛋白质间接触预测的网络服务器。
Nucleic Acids Res. 2018 Jul 2;46(W1):W432-W437. doi: 10.1093/nar/gky420.
5
Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking.将交联实验与从头蛋白质对接相结合。
J Mol Biol. 2018 Jun 8;430(12):1814-1828. doi: 10.1016/j.jmb.2018.04.010. Epub 2018 Apr 14.
6
Protein-Protein Docking Using Evolutionary Information.利用进化信息进行蛋白质-蛋白质对接
Methods Mol Biol. 2018;1764:429-447. doi: 10.1007/978-1-4939-7759-8_28.
7
Protein homology model refinement by large-scale energy optimization.利用大规模能量优化进行蛋白质同源模型精修。
Proc Natl Acad Sci U S A. 2018 Mar 20;115(12):3054-3059. doi: 10.1073/pnas.1719115115. Epub 2018 Mar 5.
8
The challenge of modeling protein assemblies: the CASP12-CAPRI experiment.蛋白质组装体建模的挑战:CASP12-CAPRI实验
Proteins. 2018 Mar;86 Suppl 1:257-273. doi: 10.1002/prot.25419. Epub 2017 Nov 26.
9
Protein Structure Modeling with MODELLER.使用MODELLER进行蛋白质结构建模。
Methods Mol Biol. 2017;1654:39-54. doi: 10.1007/978-1-4939-7231-9_4.
10
Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.在第37轮CAPRI中,使用LZerD对接和基于模板的建模以及组合评分函数提高了性能。
Proteins. 2018 Mar;86 Suppl 1(Suppl 1):311-320. doi: 10.1002/prot.25376. Epub 2017 Sep 11.

LZerD 蛋白组装流水线在 CAPRI 38-46 中的表现和增强。

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46.

机构信息

Department of Computer Science, Purdue University, West Lafayette, Indiana.

Department of Biological Sciences, Purdue University, West Lafayette, Indiana.

出版信息

Proteins. 2020 Aug;88(8):948-961. doi: 10.1002/prot.25850. Epub 2019 Nov 25.

DOI:10.1002/prot.25850
PMID:31697428
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7685511/
Abstract

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

摘要

我们报告了我们小组的蛋白质对接预测管道的性能以及第 38-46 轮关键评估预测相互作用 (CAPRI) 的结果。该管道集成了我们小组开发的程序以及其他现有的评分功能。管道的核心是 LZerD 蛋白质-蛋白质对接算法。如果在 PDB 中未找到目标复合物的模板,我们对接预测管道的第一步是运行 LZerD 对查询蛋白对。同时,在人类组预测的情况下,我们会查阅文献以找到可以指导建模的信息,例如蛋白质-蛋白质界面信息。除了任何文献信息和结合残基预测外,生成的对接诱饵通过统计评分函数的排名聚合进行选择。在提交之前,对前 10 名诱饵进行短分子动力学模拟松弛,以消除原子冲突并改善侧链构象。在这些 CAPRI 轮次中,我们小组,特别是 LZerD 服务器,表现出了强大的性能。另一方面,也有一些失败的案例,而其他一些小组则成功了。为了了解我们管道的弱点,我们分析了失败目标的错误来源。由于我们注意到结构细化是一个需要改进的步骤,因此我们新进行了几种细化方法的比较研究。最后,我们展示了几个我们小组成功和失败的例子。