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AmberMDrun

易于使用、易于扩展、高性能的Amber模拟软件包。

版本更新

v0.0.5 添加了多配体的支持。

安装

此软件仅支持Linux，因为某些Linux系统功能被调用。Mac OS X和Windows不受支持。

必要的依赖

Ambertools、python3和python3dev是必需的。Amber加速包是可选的，但我们强烈建议安装高性能的pmemd模块。Amber

您可以参考Amber安装教程以安装琥珀色。

Then, you can choose several installation methods.

  1. Anaconda(recommend)

anaconda update download MIT platform

conda install ambermdrun -c zjack

  1. PYPI PYPI安装需要一个支持c++17标准的c++编译器。 GCC-8并不完全支持c++17标准中的文件系统，因此需要GCC-9或更高版本。因此，不同的系统有不同的处理方法，我们建议使用conda进行安装。

pypi

Ubuntu >= 20

apt install g++ libpython3-dev
pip install AmberMDrun

centos7 我们推荐Red Hat开发工具集用于更高版本的gcc。

yum update
yum install epel-release
yum install centos-release-scl-rh
yum install devtoolset-9
source /opt/rh/devtoolset-9/enable # enable gcc-9
yum install python3-devel
pip install AmberMDrun
  1. 您也可以选择从源代码构建。
git clone https://github.com/9527567/AmberMD --recursive
python setup.py install --user

Optional

如果要使用AmberMDrun计算MMPB（GB）SA，则需要其他程序。

如何使用parm7和rst7进行分子动力学模拟

usage: amberMDrun [-h] --parm7 PARM7 --rst7 RST7 [--temp TEMP] [--ns NS] [--addmask ADDMASK] [--gamd GAMD] [--MIN MIN] [--MD MD]

Tools for automated operation of AMBER MD

options:
  -h, --help            show this help message and exit
  --parm7 PARM7, -p PARM7
                        amber top file
  --rst7 RST7, -c RST7  amber rst file
  --temp TEMP, -t TEMP  Temperature
  --ns NS, -n NS        time for MD(ns)
  --addmask ADDMASK     add restarint mask
  --gamd GAMD           if run gamd
  --MIN MIN             Engine for MIN
  --MD MD               Engine for MD

如何计算单个小分子和蛋白质之间的MM-PB（GB）SA

usage: mmpbsa [-h] --protein PROTEIN [--mol2 MOL2 [MOL2 ...]] [--temp TEMP] [--ns NS] [-g] [-uc] [-c CHARGE [CHARGE ...]] [--multiplicity MULTIPLICITY [MULTIPLICITY ...]]
              [--MIN MIN] [--MD MD]

Tools for automating the operation of MMPBSA

options:
  -h, --help            show this help message and exit
  --protein PROTEIN, -p PROTEIN
                        pdb file for protein
  --mol2 MOL2 [MOL2 ...], -m MOL2 [MOL2 ...]
                        mol2 file for mol
  --temp TEMP, -t TEMP  Temperature
  --ns NS, -n NS        time for MD(ns)
  -g, --guess_charge    guess charge
  -uc, --user_charge    user charge
  -c CHARGE [CHARGE ...], --charge CHARGE [CHARGE ...]
                        charge of mol
  --multiplicity MULTIPLICITY [MULTIPLICITY ...]
                        multiplicity of mol
  --MIN MIN             Engine for MIN
  --MD MD               Engine for MD

通常，分子对接后的复合物结构用于执行MMPBSA计算。因此，我们提供了一个简短的代码来处理复合物的pdb格式。因此，当您的络合物结构对接并且配体处于所需的初始位置时，您可以直接提供络合物的pdb格式文件。以下是一个示例。需要注意的是，我们不会主动协助您处理配体的氢原子。我们需要你确保配体的氢是正确的。

mmpbsa -p complex.pdb

V0.0.5 添加了多配体的支持

只需要在-m 后跟多个配体的文件即可，添加了一个选项-g用于猜测小分子的静电荷，或者手动指定静电荷，例如：

mmpbsa -p pro.pdb -m lig1.mol2 lig2.mol2 -g -n 100

V0.0.6 添加自定义电荷选项

添加了一个选项-uc用于使用自定义小分子的静电荷，只能用mol2文件，例如：

mmpbsa -p pro.pdb -m lig1.mol2 lig2.mol2 -uc -n 100

如何通过继承扩展代码

我们将在不久的将来进行描述。

如何引用

bibtex:

@Article{biom13040635,
AUTHOR = {Zhang, Zhi-Wei and Lu, Wen-Cai},
TITLE = {AmberMDrun: A Scripting Tool for Running Amber MD in an Easy Way},
JOURNAL = {Biomolecules},
VOLUME = {13},
YEAR = {2023},
NUMBER = {4},
ARTICLE-NUMBER = {635},
URL = {https://www.mdpi.com/2218-273X/13/4/635},
ISSN = {2218-273X},
DOI = {10.3390/biom13040635}
}

如果您感兴趣的话，也可以引用这篇文章

bibtex:

@article{CUI2023134812,
title = {A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML},
journal = {Food Chemistry},
volume = {405},
pages = {134812},
year = {2023},
issn = {0308-8146},
doi = {https://doi.org/10.1016/j.foodchem.2022.134812},
url = {https://www.sciencedirect.com/science/article/pii/S0308814622027741},
author = {Zhiyong Cui and Zhiwei Zhang and Tianxing Zhou and Xueke Zhou and Yin Zhang and Hengli Meng and Wenli Wang and Yuan Liu},
keywords = {Peptides, Umami prediction, TastePeptidesDB, Machine learning},
abstract = {Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecting the reported taste peptide information. Eight key molecular descriptors from di/tri-peptides were selected and obtained by modeling screening. A gradient boosting decision tree model named Umami_YYDS (89.6\% accuracy) was established by data enhancement, comparison algorithm and model optimization. Our model showed a great prediction performance compared to other models, and its outstanding ability was verified by sensory experiments. To provide a convenient approach, we deployed a prediction website based on Umami_YYDS and uploaded the Auto_Taste_ML machine learning package. In summary, we established the system TastePeptides-Meta, containing a taste peptide database TastePeptidesDB an umami/bitter taste prediction model Umami_YYDS and an open-source machine learning package Auto_Taste_ML, which were helpful for rapid screening of umami peptides.}
}