TUPÃ was developed to analyze electric field properties in molecular simulations

Related tags

Deep Learningtupa
Overview

Twitter Follow

TUPÃ: Electric field analyses for molecular simulations

alt text

What is TUPÃ?

TUPÃ (pronounced as tu-pan) is a python algorithm that employs MDAnalysis engine to calculate electric fields at any point inside the simulation box throughout MD trajectories. TUPÃ also includes a PyMOL plugin to visualize electric field vectors together with molecules.

Required packages:

  • MDAnalysis >= 1.0.0
  • Python >= 3.x
  • Numpy >= 1.2.x

Installation instructions

First, make sure you have all required packages installed. For MDAnalysis installation procedures, click here.

After, just clone this repository into a folder of your choice:

git clone https://github.com/mdpoleto/tupa.git

To use TUPÃ easily, copy the directory pathway to TUPÃ folder and include an alias in your ~/.bashrc:

alias tupa="python /path/to/the/cloned/repository/TUPA.py"

To install the PyMOL plugin, open PyMOL > Plugin Manager and click on "Install New Plugin" tab. Load the TUPÃ plugin and use it via command-line within PyMOL. To usage instructions, read our FAQ.

TUPÃ Usage

TUPÃ calculations are based on parameters that are provided via a configuration file, which can be obtained via the command:

tupa -template config.conf

The configuration file usually contains:

[Environment Selection]
sele_environment      = (string)             [default: None]

[Probe Selection]
mode                = (string)             [default: None]
selatom             = (string)             [default: None]
selbond1            = (string)             [default: None]
selbond2            = (string)             [default: None]
targetcoordinate    = [float,float,float]  [default: None]
remove_self         = (True/False)         [default: False]
remove_cutoff       = (float)              [default: 1 A ]

[Solvent]
include_solvent     = (True/False)         [default: False]
solvent_cutoff      = (float)              [default: 10 A]
solvent_selection   = (string)             [default: None]

[Time]
dt                  = (integer)            [default: 1]

A complete explanation of each option in the configuration file is available via the command:

tupa -h

TUPÃ has 3 calculations MODES:

  • In ATOM mode, the coordinate of one atom will be tracked throughout the trajectory to serve as target point. If more than 1 atom is provided in the selection, the center of geometry (COG) is used as target position. An example is provided HERE.

  • In BOND mode, the midpoint between 2 atoms will be tracked throughout the trajectory to serve as target point. In this mode, the bond axis is used to calculate electric field alignment. By default, the bond axis is define as selbond1 ---> selbond2. An example is provided HERE.

  • In COORDINATE mode, a list of [X,Y,Z] coordinates will serve as target point in all trajectory frames. An example is provided HERE.

IMPORTANT:

  • All selections must be compatible with MDAnalysis syntax.
  • TUPÃ does not handle PBC images yet! Trajectories MUST be re-imaged before running TUPÃ.
  • Solvent molecules in PBC images are selected if within the cutoff. This is achieved by applying the around selection feature in MDAnalysis.
  • TUPÃ does not account for Particle Mesh Ewald (PME) electrostatic contributions! To minimize such effects, center your target as well as possible.
  • If using COORDINATE mode, make sure your trajectory has no translations and rotations. Our code does not account for rotations and translations.

TUPÃ PyMOL Plugin (pyTUPÃ)

To install pyTUPÃ plugin in PyMOL, click on Plugin > Plugin Manager and then "Install New Plugin" tab. Choose the pyTUPÃ.py file and click Install.

Our plugin has 3 functions that can be called via command line within PyMOL:

  • efield_point: create a vector at a given atom or set of coordinates.
efield_point segid LIG and name O1, efield=[-117.9143, 150.3252, 86.5553], scale=0.01, color="red", name="efield_OG"
  • efield_bond: create a vector midway between 2 selected atoms.
efield_point resname LIG and name O1, resname LIG and name C1, efield=[-94.2675, -9.6722, 58.2067], scale=0.01, color="blue", name="efield_OG-C1"
  • draw_bond_axis: create a vector representing the axis between 2 atoms.
draw_bond_axis resname LIG and name O1, resname LIG and name C1, gap=0.5, color="gray60", name="axis_OG-C1"

Citing TUPÃ

If you use TUPÃ in a scientific publication, we would appreciate citations to the following paper:

Marcelo D. Polêto, Justin A. Lemkul. TUPÃ: Electric field analysis for molecular simulations, 2022.

Bibtex entry:

@article{TUPÃ2022,
    author = {Pol\^{e}to, M D and Lemkul, J A},
    title = "{TUPÃ : Electric field analyses for molecular simulations}",
    journal = {},
    year = {},
    month = {},
    issn = {},
    doi = {},
    url = {},
    note = {},
    eprint = {},
}

Why TUPÃ?

In the Brazilian folklore, Tupã is considered a "manifestation of God in the form of thunder". To know more, refer to this.

Contact information

E-mail: [email protected] / [email protected]

You might also like...
Differentiable molecular simulation of proteins with a coarse-grained potential

Differentiable molecular simulation of proteins with a coarse-grained potential This repository contains the learned potential, simulation scripts and

Few-Shot Graph Learning for Molecular Property Prediction

Few-shot Graph Learning for Molecular Property Prediction Introduction This is the source code and dataset for the following paper: Few-shot Graph Lea

SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks (Scientific Reports)
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks (Scientific Reports)

SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks Molecular interaction networks are powerful resources for the discovery. While dee

MolRep: A Deep Representation Learning Library for Molecular Property Prediction
MolRep: A Deep Representation Learning Library for Molecular Property Prediction

MolRep: A Deep Representation Learning Library for Molecular Property Prediction Summary MolRep is a Python package for fairly measuring algorithmic p

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).
Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Part of the 9th place solution for the Bristol-Myers Squibb – Molecular Translation challenge translating images containing chemical structures into I

source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Code for the paper
Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design This repository contains code for the paper: JA

Comments
  • 1.4.0 branch

    1.4.0 branch

    TUPÃ update (Aug 03 2022):

    • Empty environment selection now issues an error.
      
    • Empty probe selection now issues an error.
      
    • Improved Help/Usage 
      
    • Configuration file examples are based on common syntax
      
    opened by mdpoleto 0
  • 1.3.0 branch

    1.3.0 branch

    TUPÃ update (Jun 21 2022):

    • -dumptime now accepts multiple entries
    • Add average and standard deviation values at the end of ElecField_proj_onto_bond.dat and ElecField_alignment.dat
    • Add Angle column in ElecField_alignment.dat with the average angle between Efield(t) and bond axis.
    • Fix documentation issues/typos.
    opened by mdpoleto 0
Releases(v1.4.0)
  • v1.4.0(Aug 3, 2022)

    TUPÃ update (Aug 03 2022):

    • Empty environment selection now issues an error.
      
    • Empty probe selection now issues an error.
      
    • Improved Help/Usage 
      
    • Configuration file examples are based on common syntax
      
    Source code(tar.gz)
    Source code(zip)
  • v1.3.0(Jun 22, 2022)

    TUPÃ update (Jun 21 2022):

    • -dumptime now accepts multiple entries
    • Add average and standard deviation values at the end of ElecField_proj_onto_bond.dat and ElecField_alignment.dat
    • Add Angle column in ElecField_alignment.dat with the average angle between Efield(t) and bond axis.
    • Fix documentation issues/typos.
    Source code(tar.gz)
    Source code(zip)
  • v1.2.0(Apr 18, 2022)

    TUPÃ update (Apr 18 2022):

    • Make -dump now writes the entire system instead of just the environment selection.
    • Add field average and standard deviation values at the end of ElecField.dat
    • Fix documentation issues/typos.
    • Update paper metadata
    Source code(tar.gz)
    Source code(zip)
  • v1.1.0(Mar 23, 2022)

    TUPÃ update (Mar 22 2022):

    • Inclusion of LIST mode: TUPÃ reads a file containing XYZ coordinates that will be used as the probe position. Useful for binding sites or other pockets.
    • Fix documentation issues/typos.

    pyTUPÃ update (Mar 22 2022):

    • Support for a 3D representation of electric field standard deviation as a truncated cone that involves the electric field arrow.
    Source code(tar.gz)
    Source code(zip)
  • v1.0.0(Feb 9, 2022)

    TUPÃ first release (Feb 13 2022):

    • Calculation modes available: ATOM, BOND, COORDINATE
    • Support for triclinic simulation boxes only.
    • PBC support is limited to triclinic boxes. Future versions are expected to handle PBC corrections.
    • Removal of "self-contributions" are available to the COORDINATE mode only.
    • Users can dump a specific frame as a .pdb file. Futures versions are expected to allow the extraction of the environment set coordinates.
    • Residue contributions are calculated.

    pyTUPÃ first release (Feb 13 2022):

    • Support for draw_bond, efield_bond and efield_point.
    • EField vectors can be scaled up/down
    Source code(tar.gz)
    Source code(zip)
Owner
Marcelo D. Polêto
Marcelo D. Polêto
Machine Learning Time-Series Platform

cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s

632 Dec 26, 2022
Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet

🚀 If it helps you, click a star! ⭐ Update log 2020.12.10 Project structure adjustment, the previous code has been deleted, the adjustment will be re-

Deeachain 269 Jan 04, 2023
Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

Jooyoung Choi 88 Dec 01, 2022
Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.

Yongchun Zhu 81 Dec 29, 2022
Auto-Lama combines object detection and image inpainting to automate object removals

Auto-Lama Auto-Lama combines object detection and image inpainting to automate object removals. It is build on top of DE:TR from Facebook Research and

44 Dec 09, 2022
A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions

AlphaFold Analyser This program produces high quality visualisations of predicted structures produced by AlphaFold. These visualisations allow the use

Oliver Powell 3 Nov 13, 2022
ReGAN: Sequence GAN using RE[INFORCE|LAX|BAR] based PG estimators

Sequence Generation with GANs trained by Gradient Estimation Requirements: PyTorch v0.3 Python 3.6 CUDA 9.1 (For GPU) Origin The idea is from paper Se

40 Nov 03, 2022
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
Rapid experimentation and scaling of deep learning models on molecular and crystal graphs.

LitMatter A template for rapid experimentation and scaling deep learning models on molecular and crystal graphs. How to use Clone this repository and

Nathan Frey 32 Dec 06, 2022
💡 Type hints for Numpy

Type hints with dynamic checks for Numpy! (❒) Installation pip install nptyping (❒) Usage (❒) NDArray nptyping.NDArray lets you define the shape and

Ramon Hagenaars 377 Dec 28, 2022
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset

MassiveSumm: a very large-scale, very multilingual, news summarisation dataset This repository contains links to data and code to fetch and reproduce

Daniel Varab 19 Dec 16, 2022
Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Wonjong Jang 8 Nov 01, 2022
Code for Deep Single-image Portrait Image Relighting

Deep Single-Image Portrait Relighting [Project Page] Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, David W. Jacobs. In ICCV, 2019 Overview Test script for

438 Jan 05, 2023
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages

PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages Abstract NLP applications for code-mixed (CM) or mix-li

Mohsin Ali, Mohammed 1 Nov 12, 2021
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

LibraNet This repository includes the official implementation of LibraNet for crowd counting, presented in our paper: Weighing Counts: Sequential Crow

Hao Lu 18 Nov 05, 2022
Deep learning with TensorFlow and earth observation data.

Deep Learning with TensorFlow and EO Data Complete file set for Jupyter Book Autor: Development Seed Date: 04 October 2021 ISBN: (to come) Notebook tu

Development Seed 20 Nov 16, 2022
The code of “Similarity Reasoning and Filtration for Image-Text Matching” [AAAI2021]

SGRAF PyTorch implementation for AAAI2021 paper of “Similarity Reasoning and Filtration for Image-Text Matching”. It is built on top of the SCAN and C

Ronnie_IIAU 149 Dec 22, 2022
Platform-agnostic AI Framework 🔥

🇬🇧 TensorLayerX is a multi-backend AI framework, which can run on almost all operation systems and AI hardwares, and support hybrid-framework progra

TensorLayer Community 171 Jan 06, 2023
FairMOT - A simple baseline for one-shot multi-object tracking

FairMOT - A simple baseline for one-shot multi-object tracking

Yifu Zhang 3.6k Jan 08, 2023
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022