Xponge 1.2.6.4

A Python package to perform pre- and post-processing of molecular simulations

Welcome to Use Xponge!

Introduction

Xponge is a lightweight and easy-customizing python package to perform pre- and post-processing of molecular simulations.

What can Xponge do?

Xponge includes three major categories of functionality, namely, the simulation system construction, simulation data transformation and analysis, and automated workflows for complex simulations. Xponge is mainly designed for the molecular dynamics (MD) program SPONGE[1], but it can also output some general format files such as mol2 and PDB, so it may help the other molecular modelling programs too.

Installation

Xponge can be used on all operating systems (Windows/Linux/MacOS).

1. pip install

pip install Xponge

2. source setup

• 2.1 download or clone the source of the gitee or github repository

  The gitee repository is here. The github repository is here.

    git clone http://gitee.com/gao_hyp_xyj_admin/xponge.git
    git clone http://github.com/xia-yijie/xponge.git
  

• 2.2 open the directory where you download or clone the repository

• 2.3 run the command

  python setup.py install
  

Installation check

There are some unit tests in Xponge. You can do the basic test to check whether the installation is successful like this:

Xponge test --do base -o test --verbose 1

Here, Xponge can be replaced to python -m Xponge, python3 -m Xponge and so on according to your settings of the environmental variables. Some files will be generated after the test is finished.

Quickstart

Here is a simple example.

import Xponge
# Import the force field you need
import Xponge.forcefield.amber.ff14sb
# Build the molecule like this
peptide = ACE + ALA + NME
# or like this
peptide2 = NALA + ALA * 10 + CALA
# or like this
peptide3 = Xponge.Get_Peptide_From_Sequence("AAAAA")
# See the documentation for more usage!
# Save them as your favorite format
Xponge.save_pdb(peptide, "ala.pdb")
Xponge.save_mol2(peptide2, "ala12.mol2")
Xponge.save_sponge_input(peptide3, "ala5")

Then we can see ala12.mol2 in VMD:

Here is another simple example.

import Xponge
import Xponge.forcefield.amber.tip3p

box = BlockRegion(0, 0, 0, 60, 60, 60)
region_1 = BlockRegion(0, 0, 20, 20, 20, 40)
region_2 = BlockRegion(0, 0, 40, 20, 20, 60)
region_3 = BlockRegion(0, 0, 0, 20, 20, 20)
region_4 = SphereRegion(20, 10, 30, 10)
region_5 = BlockRegion(20, 0, 20, 60, 60, 60)
region_2or3 = Region(region_2, region_3, do="union")
region_4and5 = Region(region_4, region_5, do="intersect")
t = Lattice("bcc", basis_molecule=CL, scale=4)
t2 = Lattice("fcc", basis_molecule=K, scale=3)
t3 = Lattice("sc", basis_molecule=NA, scale=3)
mol = t.Create(box, region_1)
mol = t2.create(box, region_2or3, mol)
mol = t3.create(box, region_4and5, mol)
Save_PDB(mol, "out.pdb")

Then we can see out.pdb in VMD:

Detailed usage and API documentation

All can be seen here.

Dependencies

Xponge does not depend on other packages except numpy for its basic use.

However, there are some complicated functions rely on some other packages. If you do not install the dependent package, you can not use the related functions.

Here is the list of all packages which may be uesd:

package name	description	how to install
XpongeLib	c/c++ compiled library for Xponge	pip install XpongeLib
pyscf [2-4]	quantum chemistry	pip install pyscf
geometric[5]	geometry optimization	pip install geometric
rdkit[6]	cheminformatics	conda install -c rdkit rdkit
MDAnalysis[7-8]	trajectory analysis	pip install MDAnalysis
mindspore[9]	AI framework for machine learning	See the official website
mindsponge[1]	end-to-end differentiable MD	See the official website

References

[1] Y.-P. Huang, et al. Chinese J. Chem. (2022) DOI: 10.1002/cjoc.202100456

[2] Q. Sun, et al. J. Chem. Phys. (2020) DOI: 10.1063/5.0006074

[3] Q. Sun, et al. Wiley Interdiscip. Rev. Comput. Mol. Sci. (2018) DOI: 10.1002/wcms.1340

[4] Q. Sun, J. Comp. Chem. (2015) DOI: 10.1002/jcc.23981

[5] L.-P. Wang, C.C. Song, J. Chem. Phys. (2016) DOI: 10.1063/1.4952956

[6] RDKit: Open-source cheminformatics. https://www.rdkit.org

[7] R. J. Gowers, et al. Proceedings of the 15th Python in Science Conference (2016) DOI: 10.25080/majora-629e541a-00e

[8] N. Michaud-Agrawal, et al. J. Comput. Chem. (2011) DOI: 10.1002/jcc.21787

[9] MindSpore: An Open AI Framwork. https://www.mindspore.cn/