PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS
Icons made by Freepik from Flaticon is licensed by CC 3.0 BY
Welcome to PyPLIF-HIPPOS's project page. PyPLIF-HIPPOS is an upgraded version of PyPLIF (Python-based Protein-Ligand Interaction Fingerprinting), a tool for molecular docking post-analysis. It will translate the 3D coordinates of both ligand(s) (generated from docking simulation) and protein into a series of interaction bitstring (also known as Interaction Fingerprint) (see image below). HIPPOS (/ˌhipoʊz/) is a recursive acronym of HIPPOS Is PyPLIF On Steroids. From this point forward, PyPLIF-HIPPOS is simplified to HIPPOS.
Compared to PyPLIF, HIPPOS is not only faster and able to generate more customized interaction bitstring, but also supports both PLANTS & Vina! More over, unlike its predecessor it is (far) more well-documented.
Reprinted with permission from https://doi.org/10.1021/acs.jcim.0c00305. Copyright 2020 American Chemical Society.
Illustration by Radifar et al (2013) from Bioinformation.net is licensed by CC 4.0 BY
The easiest way to install HIPPOS is using Anaconda or Miniconda. If you have Anaconda or Miniconda ready in your machine, you can start with creating new environment (recommended):
conda create -n hippos python=3.6
Then activate the environment and install HIPPOS:
conda activate hippos
conda install -c conda-forge pyplif-hippos
next you can try run HIPPOS and HIPPOS-GENREF with the following command:
hippos
hippos-genref
So I already installed HIPPOS, now what? Well you could start with how to generate the reference bitstring and Getting Started tutorial for AutoDock Vina or PLANTS.
If you have any idea for improvement or found bug to report feel free to write them here.
If you are using HIPPOS please cite this paper:
Istyastono, E., Radifar, M., Yuniarti, N., Prasasty, V. and Mungkasi, S., 2020. PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS. Journal of Chemical Information and Modeling, 60(8), pp.3697-3702. https://doi.org/10.1021/acs.jcim.0c00305
This project has received funding from the National Agency for Research and Innovation (Indonesia) under grant agreement No. 807.7/LL5/PG/2020. This project has been restructured based on the MOLSSI Computational Molecular Science Python Cookiecutter version 1.3, and benefited greatly from MOLSSI Python Package Development Best Practices workshop.
© Copyright 2021, Muhammad Radifar & Enade Perdana Istyastono