Biomolecular Environment Mimicking Model GENerator

Welcome to BEMM-GEN

What is BEMM-GEN?

BEMM-GEN is a new tool aiming to generate biomolecular environment mimicking model for molecular dynamics simulation. BEMM-GEN generates spherical and cylindrical models with user-specified chemical properties, allowing the integration of arbitrary protein structures into the generated models. Consequently, the model and protein complex structures, along with the corresponding parameter files for MD simulations (AMBER or GROMACS), are provided as output files.

Requirements

Python >= 3.8, conda, openbabel, psi4, resp, ambertools, scikit-learn, rdkit, parmed

Installation

For Linux/MacOS

• Follow the instructions below to install BEMM-GEN.

For Windows

• Install Windows Subsystem for Linux (WSL), then follow the instructions below to install BEMM-GEN on WSL.

Using a Conda environment (such as Miniforge, or Miniconda) is recommended for BEMM-GEN. If you use only the pip package manager, please adjust the commands accordingly.

conda create -n bemm-gen-env
conda activate bemm-gen-env
conda install -c conda-forge openbabel psi4 resp ambertools
pip install bemm-gen

Basic Usage

# Make a cylindrical model
bemm-gen cylinder --proteinseq GASGASGASGAS --proteinSS HHHHHHHHHHHH --resnames MTY:HYD --composition 1:2.5

# Make a spherical model
bemm-gen sphere --proteinpdb protein.pdb --resnames MTY:HYD --composition 0.3:0.7

# Make parameters for a new residue
bemm-gen makeparam --smiles CCC --resname MTY --description "Methyl group"

Please see the documentation English / 日本語

Cite Us

T. Yasuda, R. Morita, Y. Shigeta and R. Harada. "BEMM-GEN: A Toolkit for Generating a Biomolecular Environment-Mimicking Model for Molecular Dynamics Simulation", J.Chem.Inf.Model, 64(19), 7184-7188, 2024, https://doi.org/10.1021/acs.jcim.4c01467

Authors

Takunori Yasuda, Doctoral Program in Biology, University of Tsukuba
Rikuri Morita, Center for Computational Sciences, University of Tsukuba
Yasuteru Shigeta, Center for Computational Sciences, University of Tsukuba
Ryuhei Harada, Center for Computational Sciences, University of Tsukuba
yasuda.takunori.tkb_gb@u.tsukuba.ac.jp