# What is CoCo?
CoCo ("Complementary Coordinates") is a method for testing and potentially enriching the the variety of conformations within an ensemble of molecular structures. It was originally developed with NMR datasets in mind and the background and this application is described in:
[Laughton C.A., Orozco M. and Vranken W., COCO: A simple tool to enrich the representation of conformational variability in NMR structures, PROTEINS, 75, 206-216 (2009)](http://onlinelibrary.wiley.com/doi/10.1002/prot.22235/abstract)
CoCo, which is based on principal component analysis, analyses the distribution of an ensemble of structures in conformational space, and generates a new ensemble that fills gaps in the distribution. These new structures are not guaranteed to be valid members of the ensemble, but should be treated as possible, approximate, new solutions for refinement against the original data.
Though developed with protein NMR data in mind, the method is quite general – the initial structures do not have to come from NMR data, and can be of nucleic acids, carbohydrates, etc.
The outline of the CoCo method is as follows:
![COCO method2.gif](https://bitbucket.org/repo/bAGR4M/images/1692328909-COCO%20method2.gif)
Step 1: The input ensemble is subjected to Principal Component Analysis and the position of each structure in a low dimensional subspace identified.
Step 2: New points are placed to fill gaps in the distribution.
Step 3: The new points are converted back into new structures, which are returned to the user.
This package provides a python-based command line tool that implements the CoCo method, and also exemplifies its application within an ExTASY workflow that can be used in an iterative manner to rapidly expand the coverage of a MD-generated ensemble.
------
# Installation
## ... on Ubuntu Linux (Desktop)
The following packages need to be installed on the system (via `apt-get`) `python-dev, libblas-dev, liblapack-dev, gfortran, tcsh`, as well as `Amber`.
Having `numpy` and `scipy` is a prerequisite to the CoCo installation as well.
After making sure that the previously listed dependencies are installed, clone the CoCo repository and install:
```
git clone https://bitbucket.org/extasy-project/coco.git
cd coco
python setup.py install --user
```
## ... on XSEDE's Stampede Cluster
First, we need to load the appropriate modules:
```
module load amber
module load python/2.7.3-epd-7.3.2
```
Clone the CoCo repository and install:
```
rm -r $HOME/.local/*
git clone https://bitbucket.org/extasy-project/coco.git
cd coco
easy_install --user --upgrade .
chmod +x $HOME/.local/lib/python2.7/site-packages/radical.pilot-0.20-py2.7.egg/radical/pilot/bootstrapper/*.sh
```
Ensure the executables are in your PATH:
```
export PATH=$PATH:$HOME/.local/bin
```
## ... on ARCHER
First, we need to load the appropriate modules:
```
module load amber
module load numpy/1.8.0-libsci
module load scipy
```
Clone the CoCo repository and install (Must be on /work to run on the backend):
```
export WORK=/path/to/your/work/directory
rm -r $WORK/.local
git clone https://bitbucket.org/extasy-project/coco.git
cd coco
mkdir -p $WORK/.local/lib/python2.7/site-packages
export PYTHONPATH=$PYTHONPATH:$WORK/.local/lib/python2.7/site-packages
easy_install --prefix $WORK/.local --upgrade .
```
# Using the Command Line Tool "pyCoCo"
In the ./example subdirectory is a test script to run a simple CoCo analysis. Four short trajectory files (AMBER .ncdf format) and an associated topology file for penta-alanine (penta.top) are provided. The test script analyses the ensemble and generates eight new structures, in PDB format, that represent apparent gaps in the sampling of conformational space. To run the test example type:
```
./test.sh
```
When the job completes (about a minute), you should see eight new pdb files (coco0.pdb - coco7.pdb), and a log file (test.log).
The log file should look like this:
```
*** pyCoCo ***
Trajectory files to be analysed:
md0.dcd: 10 frames
md1.dcd: 10 frames
md2.dcd: 10 frames
md3.dcd: 10 frames
Total variance in trajectory data: 97.51
Conformational sampling map will be generated in
3 dimensions at a resolution of 30 points
in each dimension.
8 complementary structures will be generated.
Sampled volume: 11.4286389744 Ang.^3.
Coordinates of new structures in PC space:
0 -12.37 9.31 -7.29
1 15.03 6.45 -7.29
2 1.81 -7.27 8.44
3 15.03 9.31 5.19
4 15.03 -7.27 -7.29
5 -12.37 9.31 8.44
6 2.75 9.31 8.44
7 -12.37 -7.27 -7.29
RMSD matrix for new structures:
0.00 2.63 3.46 3.24 3.38 1.99 3.15 2.19
2.63 0.00 2.80 1.69 1.69 3.44 2.81 3.73
3.46 2.80 0.00 2.60 2.43 2.76 2.49 2.77
3.24 1.69 2.60 0.00 2.82 3.64 1.82 3.56
3.38 1.69 2.43 2.82 0.00 3.45 3.60 3.62
1.99 3.44 2.76 3.64 3.45 0.00 2.43 3.11
3.15 2.81 2.49 1.82 3.60 2.43 0.00 3.70
2.19 3.73 2.77 3.56 3.62 3.11 3.70 0.00
```
pyCoCo accepts most of the most widely-used trajectory and topology file formats (AMBER, CHARMM, GROMACS, NAMD, etc.). For a full guide to pyCoCo see [here](http://bitbucket.org/extasy-project/coco/wiki/Home).
# Running the Workflow Example
The examples/workflow directory illustrates how pyCoCo can be used as part of workflow to perform enhanced sampling. In this case, we begin by running eight independent short MD simulations on alanine pentapeptide. The combined trajectory data is fed into a CoCo analysis to identify poorly sampled regions and generate eight new starting structures that populate these. These structures are then used in a new cycle of eight MD simulations, the new trajectory data is combined with the old data for a second CoCo analysis, etc. In this case ten cycles of MD/CoCo are run.
**NOTE:** This workflow requires you to have AMBER (or AMBERTOOLS) installed on your machine and have the commands *tleap* and *sander* in your path.
(CoCo workflow diagram here)
## Sequentially / Locally
Change into the examples directory:
```
cd examples/workflow
```
Then run the example:
```
python penta_coco.py
```
The output should look like this:
```
running md cycle 0
Running CoCo...
creating new crd files...
running md cycle 1
[...]
```
After the job has completed you will see that many files have been created. In the current directory
you will see a series of files *coco_cycle\*.log*; these are the log files from each CoCo run. In the individual rep0?/ directories are the input and
output files from each cycle of MD. The growing ensemble of structures is thus the growing set of trajectory
files (\*.ncdf).
To see how the CoCo process has performed, you can type:
```
% grep Total coco_cycle*.log
coco_cycle1.log:Total variance in trajectory data: 91.16
coco_cycle2.log:Total variance in trajectory data: 129.50
coco_cycle3.log:Total variance in trajectory data: 178.80
coco_cycle4.log:Total variance in trajectory data: 219.20
coco_cycle5.log:Total variance in trajectory data: 241.58
coco_cycle6.log:Total variance in trajectory data: 256.77
coco_cycle7.log:Total variance in trajectory data: 267.05
coco_cycle8.log:Total variance in trajectory data: 280.75
coco_cycle9.log:Total variance in trajectory data: 291.82
```
Note that the exact numbers you see will be different from these, due to variability in the MD.
To see how CoCo improves the rate of sampling, you can run the script 'penta_nococo.py'.
This runs the same workflow, but at the end of each MD cycle, instead of generating new start points by CoCo, the next MD runs just start from the final points from the last cycle:
```
% python penta_nococo.py
running md cycle 0
creating new crd files...
running md cycle 1
creating new crd files...
running md cycle 2
[...]
```
If you then run
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
共17个文件
txt:5个
py:4个
pkg-info:2个
资源分类:Python库 所属语言:Python 资源全名:extasy.coco-0.3.4rc4.tar.gz 资源来源:官方 安装方法:https://lanzao.blog.csdn.net/article/details/101784059
资源推荐
资源详情
资源评论
收起资源包目录
extasy.coco-0.3.4rc4.tar.gz (17个子文件)
extasy.coco-0.3.4rc4
MANIFEST.in 37B
PKG-INFO 12KB
cocopackage
coco
coco.py 4KB
__init__.py 55B
_version.py 25B
extasy.coco.egg-info
PKG-INFO 12KB
requires.txt 48B
not-zip-safe 1B
SOURCES.txt 475B
top_level.txt 5B
namespace_packages.txt 5B
dependency_links.txt 1B
CHANGES.md 0B
setup.cfg 59B
setup.py 3KB
README.md 9KB
scripts
pyCoCo 15KB
共 17 条
- 1
资源评论
挣扎的蓝藻
- 粉丝: 13w+
- 资源: 15万+
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功