搜索

Development and Application of a Fully Blind Flexible Peptide-protein Docking Protocol, pepATTRACT
全盲柔性肽-蛋白对接实验方法pepATTRACT的开发和应用   

下载 PDF 引用 收藏 提问与回复 分享您的反馈 Cited by

本文章节

Abstract

Peptide-mediated interactions are involved in many signaling and regulatory pathways as well as the DNA replication machinery and are linked to many pathological disorders. Many research groups are currently working towards a more detailed understanding of these important interactions by characterizing the 3D complex structures with experimental methods like X-ray crystallography and NMR. However, for a large number of peptide-protein complexes such atomistic structural information is lacking to date. Computational peptide docking methods can yield information complementary to experimental information by predicting the protein-peptide complex structure from the 3D structure of the protein and the peptide sequence. This approach can also be used to study interactions between folded and disordered proteins/protein regions (e.g., the interactions of the disordered regions in tumor suppressor p53 with its different partners). Here, we describe the development and usage of the fully blind, flexible peptide-protein docking protocol pepATTRACT. The ATTRACT docking engine is implemented as a suite of command line tools and options that can be combined at will. Therefore, ATTRACT protocols like pepATTRACT are typically invoked via a custom, hand-written shell script. Although this approach is very flexible, it limits the accessibility of ATTRACT to expert users only. To make pepATTRACT easily accessible to non-expert users, we created a web-interface which helps the user set up a peptide docking protocol by editing parameters in a web browser (www.attract.ph.tum.de/peptide.html). pepATTRACT docking scripts can then executed on the user's local machine, once the ATTRACT software has been installed. Here, we describe all the steps necessary for setting up a pepATTRACT docking run via the web-interface including installation of the ATTRACT software.

Keywords: Protein-peptide interaction(蛋白肽的相互作用), Disordered protein binding(无序蛋白结合), Protein-peptide complex structure(蛋白质肽复合物结构), Peptide binding prediction(肽结合预测), Binding site prediction(结合位点预测)

Materials and Reagents

  1. Atomic 3D structure of protein of interest in PDB file format (www.pdb.org)
  2. Sequence of peptide of interest in one-letter code
    Note: The protocol was tested on peptide lengths of up to 15 residues.
  3. Optional: information on protein residues involved in binding (literature research)

Equipment

  1. Any computer with Unix-based OS (Linux/Mac) and at least 2-3 GB RAM
    Note: It should be sufficient to run the protocol.

Software

  1. ATTRACT software
    1. ATTRACT source code (available at www.attract.ph.tum.de/services/ATTRACT/attract.tgz)
    2. ATTRACT virtual machine (VM) ( www.attract.ph.tum.de/services/ATTRACT/ATTRACT.vdi.gz)
  2. Molecular viewer (PyMOL, VMD, Rasmol etc.)
  3. VirtualBox (www.virtualbox.org)

Procedure

Predicting the structure of a peptide-protein complex with the fully blind docking protocol pepATTRACT requires the following steps:

  1. Installing the ATTRACT software (www.attract.ph.tum.de/services/ATTRACT/attract.tgz) and a molecular viewer.
  2. Obtaining the protein structure and the sequence of the peptide of interest.
  3. Generating a docking script and docking files with the pepATTRACT web interface (www.attract.ph.tum.de/peptide.html).
  4. Running the docking script on the user’s local machine.

Instructions and the main parts of the pepATTRACT docking protocol are visualized in Figure 1.
In the following, the individual steps are described in more detail.


  1. Installation of the ATTRACT software
    The user can choose between building and installing ATTRACT directly from the source code or downloading an ATTRACT virtual machine (VM). The virtual machine has ATTRACT and all its dependencies installed. Note that ATTRACT can only be installed on Unix-based OS (Linux/Mac). The ATTRACT VM can be used on a large number of operating systems including Windows, Linux, Macintosh and Solaris (the following instructions for the ATTRACT VM are valid for all operating systems).


    Figure 1. A flow chart illustrating the usage of the pepATTRACT docking protocol and its web interface

  2. Instructions for ATTRACT VM
    1. Download and install VirtualBox (www.virtualbox.org), any version can be used.
    2. Download the ATTRACT VM from www.attract.ph.tum.de/services/ATTRACT/ATTRACT.vdi.gz and unpack the file.
    3. Open the VirtualBox program. Add the ATTRACT.vdi file to VirtualBox (click the “New” button, pick the option “Use an existing virtual hard disk file” and select the ATTRACT.vdi file, follow the instructions). Start the virtual machine to use ATTRACT.

  3. Instructions for installing ATTRACT from source code
    Example commands are given for Ubuntu OS 14.04.
    1. Download the ATTRACT source code from www.attract.ph.tum.de/services/ATTRACT/attract.tgz.
    2. Open a terminal and unpack the source code (tar xzf attract.tgz)
    3. Install g++ and gfortran (sudo apt-get install g++ gfortran)
    4. Install numpy and scipy (sudo apt-get install python-numpy python-scipy)
    5. Install pdb2pqr (sudo apt-get install pdb2pqr)
    6. Go into attract/bin, type make clean and then make all or make all -j 4 (if you have 4 cores)
    7. Edit your .bashrc file and add the following lines to it:
      1. Export ATTRACTDIR=/home/yourname/attract/bin *(i.e. wherever you installed attract)*
      2. Export ATTRACTTOOLS=$ATTRACTDIR/../tools
      3. Export PYTHONPATH=$PYTHONPATH:/usr/share/pdb2pqr
    8. Type source ~/.bashrc

  4. Installation of a molecular viewer
    The molecular viewer PyMOL can be installed on Ubuntu by entering sudo apt-get install pymol in the terminal. Open a PDB file with it by typing pymol myprotein.pdb.

  5. Input preparation
    The user has to supply an atomic 3D structure of the protein of interest in PDB file format.
    Alternatively, good homology models with sufficient sequence similarity can be used. Using a sequence alignment, such models can be built with programs like MODELLER (Eswar et al., 2014). Homology models can also be found in public databases; e.g., ModBase (modbase.compbio.ucsf.edu/modbase-cgi/index.cgi), the Protein Model Portal (www.proteinmodelportal.org) or the Protein Model Data Base (bioinformatics.cineca.it/PMDB). Investigations on structural deviations of homology models have shown that good models can be generated for sequence identities of e.g., > 40% (accuracy statistics of EVA at pdg.cnb.uam.es/eva/cm/res/accuracy.html; Eyrich et al., 2001; Rodrigues et al., 2013). Note that the docking protocol is quite sensitive to structural changes of the docking partners and hence it is sensitive to structural deviations in the input structure resulting from incorrect homology modeling. At the moment, unnatural and modified amino acids (e.g., post-translational modifications) are not supported and have to be removed from the input PDB file or mutated to standard amino acids manually (see Note 2). We are working towards adding support for modified amino acids to pepATTRACT in the next ATTRACT release.

  6. Generating the docking script and executing it
    1. Go to www.attract.ph.tum.de/peptide.html (Figure 2).
    2. Upload the PDB file of the protein.
    3. Enter the sequence of the peptide in one-letter code (standard amino acids only).
    4. Specify optional parameters (see next paragraphs for details).
    5. Hit the “Get configuration” button.
    6. Download the archive yourrunname.tgz and unpack it.
    7. Run the protocol by double-click (typical run time 1-4 h).


      Figure 2. Screenshot of the pepATTRACT web interface with instructions

  7. Including experimental information
    The web interface offers the possibility to include experimental information in the docking run and restrict the search for the peptide binding site to a portion of the protein’s surface. If certain protein residues are known to be important in peptide binding e.g., from mutational experiments, they can be specified as active residues. This will ensure that only solutions in which these residues are in contact with the peptide are generated. Multiple residues can be specified as active residues separated by commas. There is no limit on the number of possible active residues, however, keep in mind that the more residues are specified the less specific the search gets while increasing the computational load.

  8. Protein conformational change
    Many proteins undergo conformational changes when binding to a partner molecule. Although this is less common for peptide-protein than for protein-protein interactions, it influences the prediction quality of this semi-rigid peptide docking approach strongly. The pepATTRACT protocol allows to include conformational change on the protein side by providing an ensemble of possible conformations. This option is also useful if the protein structure has been derived from template-based homology modeling. Instead of uploading a PDB file containing a single protein structure to the web interface, the user can upload a multi-model PDB file and then has to specify the number of conformers present in the uploaded multi-model PDB file (web interface field “if the PDB is a multi-model ensemble, specify the number of conformers”). In a multi-model PDB file, different structures of the protein are separated by MODEL and ENDMDL lines. For more information on the format please visit deposit.rcsb.org/adit/docs/pdb_atom_format.html - MODEL. Please note that all the conformers need to have the same sequence and coordinate information for the same residues in the same order. Unfortunately, predicting conformational change upon binding is very difficult due to the large number of degrees of freedoms involved. Protein flexibility can be tested to a certain degree by performing molecular dynamics simulations or normal mode calculations prior to docking.

  9. Analysis
    The final models are converted to PDB file format for visual inspection by the user (type pymol results.pdb to look at the structures). The final structures could then be further refined in molecular dynamics simulations (Schindler et al., 2015a). The web interface offers the opportunity to benchmark pepATTRACT’s performance by docking previously experimentally resolved peptide-protein complexes and evaluating how accurate the experimental structure can be reproduced. For this, reference PDB files containing the protein structure and the peptide structure can be uploaded. Standard evaluation criteria like ligand-RMSD, interface-RMSD and fraction of native contacts (Mendez et al., 2005) can be selected for automatic computation (results.lrmsd, results.irmsd, results.fnat) (see also Note 1).

  10. Docking protocol description
    The web interface generates an archive containing the PDB files and a bash script (yourruname.sh). The bash script contains all the commands necessary to run the fully blind peptide-protein docking protocol pepATTRACT for a given protein structure and peptide sequence. The docking protocol consists of the following steps (Figure 1). First, three peptide model structures are generated from sequence (α-helical, extended and poly-proline conformation) (Tien et al., 2013). This approach is supported by the experimental observation that this limited set of three motifs dominates the conformational ensemble that peptides adopt in peptide-protein complexes (London et al., 2010) and has also been successfully used in the HADDOCK peptide docking protocol (Trellet et al., 2013). Then global rigid body docking with ATTRACT using a coarse-grained force field is performed (Zacharias, 2003; May and Zacharias, 2008). The ATTRACT coarse-grained force field uses soft distance-dependent Lennard-Jones (LJ)-type potentials with attractive or repulsive parameters and electrostatics between charged residues to calculate the interaction energy between residue types A and B.


    Notes:
    1. The ATTRACT energy/score is only used to compare/rank different docking models, it does not represent a measure for peptide binding affinity.
    2. In the coarse-grained docking stage, the internal structure of the protein and the peptide are kept rigid and only orientational and translational degrees of freedoms are explored (6 rigid body degrees of freedom).
    3. The energy calculations are accelerated by a precalculated grid (de Vries et al., 2015). The rigid body docking solutions are rescored and ranked by ATTRACT score.
    4. The top ranked models can then be refined with the flexible interface refinement method iATTRACT (Schindler et al., 2015b).
    5. In the refinement stage, the models are subjected to a simultaneous optimization of their global rigid body degrees of freedom and of the local position of the interface atoms.

Representative data

As an example application, we predict the complex of a peptide derived from type 1 human immunodeficiency virus (HIV-1) capsid protein with a cellular protein, cyclophilin A (CypA). The HIV-1 virion forms by assembly of the Gag polyprotein. Approximately 2,000 Gag molecules bind to the host cell membrane and assemble into budding virions. The HIV-1 virion also contains about 200 copies of the cytosolic CypA protein. These are essential for virus replication and are packaged into the virion by a direct interaction between Gag and CypA. Previous studies have characterized both the 3D complex structure of CypA and the His87-Ala-Gly-Pro-Ile-Ala92 sequence from the capsid protein (PDB code: 1AWR) (Vajdos et al., 1997) and the apo cypA structure (PDB code: 2ALF). We use these structures to test the pepATTRACT protocol refining the top 1,000 rigid body docking models with iATTRACT. One of the final top 10 ranked models is shown in Figure 3. It correctly predicts the binding site and an extended conformation for the peptide (IRMSD 1.15 A°, fraction of native contacts 0.67).


Figure 3. Results for docking cyclophilin A to the HAGPIA sequence of HIV-1 capsid protein. The protein is drawn in surface representation (gray), the peptide in stick representation (red: docking model, black: reference from the crystal structure). The protein residues involved in binding are shown in yellow. This figure was generated with PyMOL.

Notes

  1. Performance
    pepATTRACT’s performance has been benchmarked on a set of 80 known peptide-protein complexes (Schindler et al., 2015a) yielding an overall success rate of 70% for 1,000 generated final docking models per test case. pepATTRACT was also compared to two state-of-the-art local docking methods, Rosetta FlexPepDock ab-initio (Raveh et al., 2011) and HADDOCK peptide docking (Trellet et al., 2013). We found that pepATTRACT's performance in fully blind mode was comparable to that of the two local docking methods and pepATTRACT-local surpassed their performance by a significant margin (Schindler et al., 2015a).
  2. Reproducibility
    Nevertheless, we recommend the users test the protocol for their specific biological systems whenever possible using similar experimentally resolved complex structures as benchmark cases. Rerunning the protocol with identical input files on the same machine will give identical results. However, due to different compiler settings and numerical instabilities, executing the same commands on different machines will lead to slightly different numerical results. These slight differences are not significant in terms of overall prediction quality (i.e., the success rates for testing the protocol on a benchmark set of known peptide-protein complexes).
  3. Cofactors and modified amino acids
    The ATTRACT docking engine does not support ions, cofactors or modified amino acids at the moment, we are working towards including these in future versions of ATTRACT. Currently, all HETATM entries are ignored when reading the PDB file. Users should manually convert modified amino acids to standard amino acids before uploading their PDB file and relabel them to ATOM entries.
  4. Memory requirements
    For running the protocol, a precalculated potential energy grid has to be loaded into memory. This requires at least 2 GB of RAM, for larger proteins, the demand can be higher. Failures of the protocol may result from failure to allocate sufficient memory.
  5. Large protein
    The ATTRACT software is compiled with default settings which limits the number of atoms in a protein to 10,000. This limit can be enlarged by modifying the file $ATTRACTDIR/max.h and increasing the variable MAXATOM (maximum number of atoms in protein) and if necessary MAXRES (maximum number of residues), TOTMAXATOM, TOTMAXRES etc. to the desired range. Then recompile by going to $ATTRACTDIR and typing make clean and make all. Check the OPLS converted file yourprotein-aa.pdb to find out how many atoms the protein has during docking with ATTRACT. Keep in mind that larger proteins also imply more memory and longer run times.
  6. Other files generated by the web interface
    Apart from the .tgz docking archive, the web interface also offers two other files for download: an embedded parameter file (yourrunname-embedded.web) and a delta file (yourrunname-delta.json). The embedded parameter file contains all docking parameters needed by ATTRACT in a single file (including the uploaded protein PDB file). This file describes the docking protocol in a reproducible manner and can be used for automatic recalculation of docking runs or later modification of parameters via the Upload web-interface (www.attract.ph.tum.de/services/ATTRACT/upload.html). The delta file contains the web form parameters that were filled in by the user or changed (when uploading a previously generated embedded parameter file). The delta file is the most useful reference file for describing the docking protocol in words, since it contains all the docking parameters in a text file. It can be used as a reference for writing a protocol description in the Materials & Methods section. Furthermore, the delta file should be provided in all help, support and feedback requests and for bug reports.

Acknowledgments

This protocol was adapted from the previously published studies of de Vries et al. (2015) and Schindler et al. (2015a). The authors acknowledge funding by the Center for Integrated Protein Science Munich (CIPSM). This work was performed on computational resources provided by a CIPSMWomen’s consumables grant.

References

  1. de Vries, S. J., Schindler, C. E., Chauvot de Beauchene, I. and Zacharias, M. (2015). A web interface for easy flexible protein-protein docking with ATTRACT. Biophys J 108(3): 462-465.
  2. Eyrich, V. A., Marti-Renom, M. A., Przybylski, D., Madhusudhan, M. S., Fiser, A., Pazos, F., Valencia, A., Sali, A. and Rost, B. (2001). EVA: continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17(12): 1242-1243.
  3. London, N., Movshovitz-Attias, D. and Schueler-Furman, O. (2010). The structural basis of peptide-protein binding strategies. Structure 18(2): 188-199.
  4. May, A. and Zacharias, M. (2008). Energy minimization in low-frequency normal modes to efficiently allow for global flexibility during systematic protein-protein docking. Proteins 70(3): 794-809.
  5. Mendez, R., Leplae, R., Lensink, M. F. and Wodak, S. J. (2005). Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures. Proteins 60(2): 150-169.
  6. Raveh, B., London, N., Zimmerman, L. and Schueler-Furman, O. (2011). Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One 6(4): e18934.
  7. Rodrigues, J. P., Melquiond, A. S., Karaca, E., Trellet, M., van Dijk, M., van Zundert, G. C., Schmitz, C., de Vries, S. J., Bordogna, A., Bonati, L., Kastritis, P. L. and Bonvin, A. M. (2013). Defining the limits of homology modeling in information-driven protein docking. Proteins 81(12): 2119-2128.
  8. Schindler, C. E., de Vries, S. J. and Zacharias, M. (2015a). Fully Blind Peptide-Protein Docking with pepATTRACT. Structure 23(8): 1507-1515.
  9. Schindler, C. E., de Vries, S. J. and Zacharias, M. (2015b). iATTRACT: simultaneous global and local interface optimization for protein-protein docking refinement. Proteins 83(2): 248-258.
  10. Tien, M. Z., Sydykova, D. K., Meyer, A. G. and Wilke, C. O. (2013). PeptideBuilder: A simple Python library to generate model peptides. Peer J 1: e80.
  11. Trellet, M., Melquiond, A. S. and Bonvin, A. M. (2013). A unified conformational selection and induced fit approach to protein-peptide docking. PLoS One 8(3): e58769.
  12. Vajdos, F. F., Yoo, S., Houseweart, M., Sundquist, W. I. and Hill, C. P. (1997). Crystal structure of cyclophilin A complexed with a binding site peptide from the HIV-1 capsid protein. Protein Sci 6(11): 2297-2307.
  13. Webb, B. and Sali, A. (2014). Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 47: 5 6 1-32.
  14. Zacharias, M. (2003). Protein-protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci 12(6): 1271-1282.

简介

肽介导的相互作用参与许多信号传导和调节途径以及DNA复制机制并且与许多病理性疾病相关。许多研究小组目前正在通过用诸如X射线晶体学和NMR的实验方法表征3D复合结构来更加详细地理解这些重要的相互作用。然而,对于大量的肽 - 蛋白复合物,这样的原子结构信息迄今为止是缺乏的。计算肽对接方法可以通过从蛋白质的3D结构和肽序列预测蛋白质 - 肽复合物结构而产生与实验信息互补的信息。该方法还可以用于研究折叠和无序的蛋白质/蛋白质区域之间的相互作用(例如,肿瘤抑制基因p53中的无序区域与其不同伴侣的相互作用)。在这里,我们描述了完全盲,灵活的肽蛋白对接协议pepATTRACT的发展和使用。 ATTRACT对接引擎被实现为一套命令行工具和选项,可以随意组合。因此,类似pepATTRACT的ATTRACT协议通常通过定制的手写shell脚本调用。虽然这种方法非常灵活,但它限制了ATTRACT对专家用户的可访问性。为了使非专家用户容易访问pepATTRACT,我们创建了一个网络界面,帮助用户通过在网络浏览器中编辑参数来设置肽对接协议( www.attract.ph.tum.de/peptide.html )。一旦安装了ATTRACT软件,就可以在用户的​​本地机器上执行pepATTRACT对接脚本。在这里,我们描述了通过web界面设置pepATTRACT对接运行所需的所有步骤,包括安装ATTRACT软件。

关键字:蛋白肽的相互作用, 无序蛋白结合, 蛋白质肽复合物结构, 肽结合预测, 结合位点预测

材料和试剂

  1. PDB文件格式的感兴趣的蛋白质的原子3D结构( www.pdb.org
  2. 单字母代码的感兴趣肽序列
    注意:在多达15个残基的肽段上测试方案。
  3. 可选:参与结合的蛋白质残留信息(文献研究)

设备

  1. 任何具有基于Unix的操作系统(Linux/Mac)和至少2-3 GB RAM的计算机
    注意:这应该足以运行协议。

软件

  1. ATTRACT软件
    1. ATTRACT源代码(可从 www.attract.ph.tum获取。 de/services/ATTRACT/attract.tgz
    2. ATTRACT虚拟机(VM)( www.attract.ph .tum.de/services/ATTRACT/ATTRACT.vdi.gz
  2. 分子观察器(PyMOL,VMD,Rasmol等)
  3. VirtualBox( www.virtualbox.org

程序

使用完全盲对接协议pepATTRACT预测肽 - 蛋白复合物的结构需要以下步骤:

  1. 安装ATTRACT软件( www.attract.ph.tum.de/services/ATTRACT/attract。 tgz )和分子观察器。
  2. 获得感兴趣的肽的蛋白质结构和序列。
  3. 使用pepATTRACT网络界面生成停靠脚本和停靠文件( www.attract.ph .tum.de/peptide.html )。
  4. 在用户的本地计算机上运行停靠脚本。

说明和pepATTRACT对接协议的主要部分在图1中可视化。
在下文中,更详细地描述各个步骤。


  1. 安装ATTRACT软件
    用户可以直接从源代码构建和安装ATTRACT,或下载ATTRACT虚拟机(VM)。虚拟机已安装ATTRACT及其所有依赖项。请注意,ATTRACT只能安装在基于Unix的操作系统(Linux/Mac)上。 ATTRACT VM可用于大量操作系统,包括Windows,Linux,Macintosh和Solaris(以下ATTRACT VM的说明适用于所有操作系统)。


    图1.说明pepATTRACT停靠协议及其Web界面的用法的流程图

  2. ATTRACT VM的说明
    1. 下载并安装VirtualBox( www.virtualbox.org ),可以使用任何版本。
    2. www.attract.ph下载ATTRACT VM。 tum.de/services/ATTRACT/ATTRACT.vdi.gz 并解压缩文件。
    3. 打开VirtualBox程序。将ATTRACT.vdi文件添加到VirtualBox中(单击"新建"按钮,选择"使用现有虚拟硬盘文件"选项并选择ATTRACT.vdi文件,按照说明)。启动虚拟机以使用ATTRACT。

  3. 有关从源代码
    安装ATTRACT的说明 给出了Ubuntu OS 14.04的示例命令。
    1. www.attract.ph.tum.de/services/ATTRACT/attract.tgz。
    2. 打开终端并解压缩源代码( tar xzf attract.tgz )
    3. 安装g ++和gfortran( sudo apt-get install g ++ gfortran )
    4. 安装numpy和scipy( sudo apt-get install python-numpy python-scipy )
    5. 安装pdb2pqr( sudo apt-get install pdb2pqr )
    6. 进入attract/bin,输入 make clean ,然后全部或 make all -j 4 >
    7. 编辑.bashrc文件并将以下行添加到其中:
      1. 汇出ATTRACTDIR =/home/yourname/attract/bin *(即无论您安装哪个地方)*
      2. 导出ATTRACTTOOLS = $ ATTRACTDIR /../tools
      3. 导出PYTHONPATH = $ PYTHONPATH:/usr/share/pdb2pqr
    8. 键入 source〜/.bashrc

  4. 安装分子检测器
    分子查看器PyMOL可以通过在终端中输入 sudo apt-get install pymol 在Ubuntu上安装。通过键入 pymol myprotein.pdb 打开一个PDB文件。

  5. 输入准备
    用户必须以PDB文件格式提供感兴趣的蛋白质的原子3D结构 或者,可以使用具有足够序列相似性的良好同源模型。使用序列分配,这样的模型可以用诸如MODELLER的程序来构建(Eswar等人,2014)。同源模型也可以在公共数据库中找到; 例如,ModBase( modbase.compbio.ucsf .edu/modbase-cgi/index.cgi ),蛋白质模型门户( www.proteinmodelportal.org )或蛋白质模型数据库( bioinformatics.cineca.it/PMDB )。对同源模型的结构偏差的研究已经表明,可以为序列同一性生成良好的模型,例如 40%(在pdg.cnb.uam.es/eva/cm/res/accuracy.html; Eyrich等人,2001; Rodrigues等人,中EVA的精确度统计>,2013)。注意,对接协议对对接伙伴的结构变化非常敏感,因此它对由不正确的同源性建模导致的输入结构中的结构偏差敏感。目前,不支持非天然和修饰的氨基酸(例如翻译后修饰),并且必须从输入PDB文件中去除或手动突变为标准氨基酸(参见注释2)。我们正在努力在下一个ATTRACT版本中向pepATTRACT添加改性氨基酸的支持
  6. 生成停靠脚本并执行它
    1. 转到 www.attract.ph.tum.de/peptide.html(图2)。
    2. 上传蛋白质的PDB文件。
    3. 以单字母代码(仅限标准氨基酸)输入肽的序列。
    4. 指定可选参数(有关详细信息,请参阅下一段)。
    5. 点击"获取配置"按钮。
    6. 下载归档yourrunname.tgz并解压缩。
    7. 双击(典型运行时间1-4小时)运行协议。


      图2.包含说明的pepATTRACT网络界面的屏幕截图

  7. 包括实验性信息
    Web界面提供了在对接运行中包括实验信息的可能性,并且限制对肽结合位点到蛋白质表面的一部分的搜索。如果已知某些蛋白质残基在肽结合中是重要的,例如来自突变实验,它们可以被指定为活性残基。这将确保仅产生其中这些残基与肽接触的溶液。多个残基可以指定为由逗号分隔的活性残基。可能的活性残基的数量没有限制,但是请记住,指定的残基越多,搜索的特异性越低,同时增加计算负荷。

  8. 蛋白质构象变化
    当结合配偶体分子时,许多蛋白质经历构象变化。虽然这不太常见的肽蛋白比蛋白质 - 蛋白质相互作用,它强烈影响这种半刚性肽对接方法的预测质量。 pepATTRACT方案允许通过提供可能构象的整体在蛋白质侧包括构象变化。如果蛋白质结构衍生自基于模板的同源性建模,则该选项也是有用的。代替将包含单个蛋白质结构的PDB文件上传到web界面,用户可以上传多模型PDB文件,然后必须指定存在于上传的多模型PDB文件中的构造体的数目(web接口字段"if PDB是多模型系综,指定构象的数目")。在多模型PDB文件中,蛋白质的不同结构通过MODEL和ENDMDL线分开。有关格式的更多信息,请访问deposit.rcsb.org/adit/docs/pdb_atom_format.html - MODEL。请注意,所有构象异构体需要具有相同顺序的相同残基的相同序列和坐标信息。不幸的是,由于涉及大量的自由度,预测结合后的构象变化是非常困难的。通过在对接之前进行分子动力学模拟或正常模式计算,可以在一定程度上测试蛋白质的灵活性
  9. 分析
    最终模型转换为PDB文件格式,供用户目视检查(类型pymol results.pdb查看结构)。然后可以在分子动力学模拟中进一步改进最终结构(Schindler等人,2015a)。 Web界面提供了通过对比以前实验分析的肽 - 蛋白复合物和评估实验结构的精确度可以再现基准pepATTRACT的性能的机会。为此,可以上传包含蛋白质结构和肽结构的参考PDB文件。可以选择标准评价标准如配体RMSD,界面RMSD和天然接触部分(Mendez等人,2005)用于自动计算(results.lrmsd,results.irmsd,results.fnat) (见注1)。

  10. 对接协议描述
    Web界面生成包含PDB文件和bash脚本(yourruname.sh)的存档。 bash脚本包含对于给定的蛋白质结构和肽序列运行完全盲肽 - 蛋白质对接协议pepATTRACT所需的所有命令。对接协议由以下步骤组成(图1)。首先,从序列(α-螺旋,延伸和聚脯氨酸构象)产生三个肽模型结构(Tien等人,2013)。这种方法得到实验观察的支持,即这种有限的三个基序的集合支配肽在肽 - 蛋白复合物中采用的构象集合(London等人,2010),并且也已经成功地用于HADDOCK肽对接协议(Trellet等人,2013)。然后使用粗粒力场与ATTRACT进行全局刚体对接(Zacharias,2003; May和Zacharias,2008)。 ATTRACT粗粒力场使用具有吸引或排斥参数的软距离依赖的Lennard-Jones(LJ)型电位和带电残基之间的静电来计算残基A和B之间的相互作用能。


    注意:
    1. ATTRACT能量/得分仅用于比较/分级不同的对接模型,它不代表肽结合亲和力的量度。
    2. 在粗粒对接阶段,蛋白质和肽的内部结构保持刚性,并且仅探索取向和平移自由度(6个刚体自由度)。
    3. 通过预先计算的网格加速能量计算(de Vries等人,2015)。刚体对接解决方案被重新打包并通过ATTRACT得分排名。
    4. 然后,可以使用灵活的接口细化方法iATTRACT(Schindler等人,2015b)来细化排名靠前的模型。
    5. 在细化阶段,对模型进行同时优化它们的全局刚体自由度和界面原子的局部位置。

代表数据

作为示例应用,我们预测来自1型人类免疫缺陷病毒(HIV-1)衣壳蛋白的肽与细胞蛋白,亲环蛋白A(CypA)的复合物。 HIV-1病毒体通过Gag多聚蛋白的装配形成。约2,000个Gag分子结合到宿主细胞膜并组装成出芽的病毒粒子。 HIV-1病毒体还含有约200个拷贝的胞浆CypA蛋白。这些对于病毒复制是必需的,并且通过Gag和CypA之间的直接相互作用包装在病毒粒子中。先前的研究已经表征了来自衣壳蛋白(PDB代码:1AWR)的CypA和His87-Ala-Gly-Pro-Ile-Ala92序列的3D复合结构(Vajdos等人,1997)和载脂蛋白cypA结构(PDB代码:2ALF)。我们使用这些结构来测试pepATTRACT协议,用iATTRACT改进前1000个刚体对接模型。最终排名前10位的模型之一如图3所示。它正确地预测了肽的结合位点和扩展构象(IRMSD 1.15 A°,天然接触的分数0.67)。


图3.将亲环蛋白A与HIV-1衣壳蛋白的HAGPIA序列接合的结果蛋白质以表面表示(灰色)绘制,肽以棒状表示(红色:对接模型,黑色:参考晶体结构)。参与结合的蛋白质残基以黄色显示。这个数字是用PyMOL生成的。

笔记

  1. 性能
    pepATTRACT的性能已经在一组80个已知的肽 - 蛋白复合物(Schindler等人,2015a)上进行基准测试,对于每个测试用例的1000个生成的最终对接模型,总体成功率为70%。还将pepATTRACT与两种现有技术的局部对接方法(Rosetta FlexPepDock ab-initio(Raveh et al。,2011)和HADDOCK肽对接(Trellet等人,/em>,2013)。我们发现pepATTRACT在完全盲模式中的性能与两种局部对接方法的性能相当,并且pepATTRACT-局部超过它们的性能有显着的裕度(Schindler等人,2015a)。
  2. 重现性
    然而,我们建议用户尽可能使用类似的实验分析复杂结构作为基准案例,测试其特定生物系统的协议。在同一台机器上使用相同的输入文件重新运行协议将得到相同的结果。然而,由于不同的编译器设置和数值不稳定性,在不同的机器上执行相同的命令将导致略微不同的数值结果。这些轻微差异在总体预测质量(即,在已知肽 - 蛋白复合物的基准组上测试方案的成功率)方面不显着。
  3. 辅因子和修饰氨基酸
    ATTRACT对接引擎目前不支持离子,辅助因子或修饰的氨基酸,我们正在努力将这些包含在未来版本的ATTRACT中。目前,在读取PDB文件时,将忽略所有HETATM条目。用户应在上传PDB文件并将其重新标记为ATOM条目之前,手动将修饰的氨基酸转换为标准氨基酸。
  4. 内存要求
    为了运行协议,必须将预先计算的势能网格加载到存储器中。这需要至少2 GB的RAM,对于较大的蛋白质,需求可以更高。协议失败可能是由于未能分配足够的内存所致。
  5. 大蛋白质
    ATTRACT软件使用默认设置编译,其将蛋白质中的原子数限制为10,000。可以通过修改文件$ ATTRACTDIR/max.h并将变量MAXATOM(蛋白质中的最大原子数)和必要时的MAXRES(最大残基数)TOTMAXATOM,TOTMAXRES等增加到所需范围来扩大该限制。然后通过转到$ ATTRACTDIR并键入 make clean 和全部来重新编译。检查OPLS转换的文件yourprotein-aa.pdb,以找出蛋白质在与ATTRACT对接期间有多少原子。请记住,较大的蛋白质也意味着更多的记忆和更长的运行时间
  6. 由web界面生成的其他文件
    除了.tgz对接存档之外,Web界面还提供了两个其他文件供下载:嵌入式参数文件( yourrunname- embedded.web )和增量文件( yourrunname-delta.json )。嵌入式参数文件包含ATTRACT在单个文件(包括上传的蛋白质PDB文件)中需要的所有停靠参数。此文件以可重现的方式描述对接协议,可用于通过Upload Web界面( www.attract.ph.tum.de/services/ATTRACT/upload.html )。增量文件包含由用户填写或更改(在上传以前生成的嵌入参数文件时)的Web表单参数。 delta文件是用于以单词形式描述停靠协议的最有用的参考文件,因为它包含文本文件中的所有停放参数。它可以用作在材料和材料中编写协议描述的参考。方法部分。此外,应在所有帮助,支持和反馈请求以及错误报告中提供增量文件。

致谢

该方案改编自先前发表的de Vries等人(2015)和Schindler等人(2015a)的研究。作者承认慕尼黑综合蛋白质科学中心(CIPSM)的资助。这项工作是在CIPSMWomen消费品赠款提供的计算资源上进行的。

参考文献

  1. de Vries,SJ,Schindler,CE,Chauvot de Beauchene,I.和Zacharias,M.(2015)。  用于容易地与ATTRACT进行灵活的蛋白质 - 蛋白质对接的网络界面。 Biophys J 108(3):462-465。 >
  2. Eyrich,VA,Marti-Renom,MA,Przybylski,D.,Madhusudhan,MS,Fiser,A.,Pazos,F.,Valencia,A.,Sali,A.and Rost,B。(2001) a class ="ke-insertfile"href ="http://www.ncbi.nlm.nih.gov/pubmed/11751240"target ="_ blank"> EVA:连续自动评估蛋白质结构预测服务器。 Bioinformatics 17(12):1242-1243
  3. 伦敦,N.,Movshovitz-Attias,D.和Schueler-Furman,O.(2010)。  肽 - 蛋白结合的结构基础。结构 18(2):188-199。
  4. May,A.和Zacharias,M。(2008)。  低频正常模式中的能量最小化,以在系统蛋白质 - 蛋白质对接期间有效地允许全局灵活性。蛋白质70(3):794-809。
  5. Mendez,R.,Leplae,R.,Lensink,MF和Wodak,SJ(2005)。  轮次3-5中CAPRI预测的评估显示了对接程序的进展。蛋白质60(2):150-169。
  6. Raveh,B.,London,N.,Zimmerman,L.和Schueler-Furman,O.(2011)。  Rosetta FlexPepDock从头开始:同时折叠,对接和精化肽到它们的受体上。 6(4):e18934 。
  7. Rodrigues,JP,Melquiond,AS,Karaca,E.,Trellet,M.,van Dijk,M.,van Zundert,GC,Schmitz,C.,de Vries,SJ,Bordogna,A.,Bonati, ,PL和Bonvin,AM(2013)。  定义在信息驱动蛋白质对接中同源性建模的限制。蛋白 81(12):2119-2128。
  8. Schindler,CE,de Vries,SJ和Zacharias,M.(2015a)。  使用pepATTRACT的完全盲肽 - 蛋白质对接。 结构 23(8):1507-1515。
  9. Schindler,CE,de Vries,SJ和Zacharias,M.(2015b)。  iATTRACT:蛋白质 - 蛋白质对接精制的同时全局和局部界面优化。 83(2):248-258。
  10. Tien,MZ,Sydykova,DK,Meyer,AG和Wilke,CO(2013)。  PeptideBuilder:用于生成模型肽的简单Python库 1:e80。
  11. Trellet,M.,Melquiond,AS和Bonvin,AM(2013)。  蛋白质 - 肽对接的统一构象选择和诱导拟合方法。 8(3):e58769。
  12. Vajdos,FF,Yoo,S.,Houseweart,M.,Sundquist,WI和Hill,CP(1997)。  亲环蛋白A的晶体结构与HIV-1衣壳蛋白的结合位点肽复合。蛋白质科学 6 2297-2307。
  13. Webb,B和Sali,A。(2014)。  使用MODELLER进行比较蛋白质结构建模。

    Curr Protoc Bioinformatics 47:5 6 1-32。
  14. Zacharias,M。(2003)。  蛋白蛋白对接 具有减少的侧链柔性的蛋白质模型。蛋白质科学12(6):1271-1282。
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright: © 2016 The Authors; exclusive licensee Bio-protocol LLC.
引用:Schindler, C. E., de Vries, S. J. and Zacharias, M. (2016). Development and Application of a Fully Blind Flexible Peptide-protein Docking Protocol, pepATTRACT. Bio-protocol 6(11): e1831. DOI: 10.21769/BioProtoc.1831.
提问与回复

(提问前,请先登录)bio-protocol作为媒介平台,会将您的问题转发给作者,并将作者的回复发送至您的邮箱(在bio-protocol注册时所用的邮箱)。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片或者视频的形式来说明遇到的问题。由于本平台用Youtube储存、播放视频,作者需要google 账户来上传视频。

当遇到任务问题时,强烈推荐您提交相关数据(如截屏或视频)。由于Bio-protocol使用Youtube存储、播放视频,如需上传视频,您可能需要一个谷歌账号。