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
A compendium of useful, interesting, inspirational usage of pandas functions, each example will be an ipynb file

Pandas_by_examples A compendium of useful/interesting/inspirational usage of pandas functions, each example will be an ipynb file What is this reposit

Guangyuan(Frank) Li 32 Nov 20, 2022
Towards Long-Form Video Understanding

Towards Long-Form Video Understanding Chao-Yuan Wu, Philipp Krähenbühl, CVPR 2021 [Paper] [Project Page] [Dataset] Citation @inproceedings{lvu2021,

Chao-Yuan Wu 69 Dec 26, 2022
A Pytree Module system for Deep Learning in JAX

Treex A Pytree-based Module system for Deep Learning in JAX Intuitive: Modules are simple Python objects that respect Object-Oriented semantics and sh

Cristian Garcia 216 Dec 20, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Viewmaker Networks: Learning Views for Unsupervised Representation Learning Alex Tamkin, Mike Wu, and Noah Goodman Paper link: https://arxiv.org/abs/2

Alex Tamkin 31 Dec 01, 2022
Anchor-free Oriented Proposal Generator for Object Detection

Anchor-free Oriented Proposal Generator for Object Detection Gong Cheng, Jiabao Wang, Ke Li, Xingxing Xie, Chunbo Lang, Yanqing Yao, Junwei Han, Intro

jbwang1997 56 Nov 15, 2022
Deep learning models for change detection of remote sensing images

Change Detection Models (Remote Sensing) Python library with Neural Networks for Change Detection based on PyTorch. ⚡ ⚡ ⚡ I am trying to build this pr

Kaiyu Li 176 Dec 24, 2022
Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

41 Jan 03, 2023
In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021

In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021. Balestriero et

Sean M. Hendryx 1 Jan 27, 2022
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning.

neural-combinatorial-rl-pytorch PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning. I have implemented the basic

Patrick E. 454 Jan 06, 2023
Fast convergence of detr with spatially modulated co-attention

Fast convergence of detr with spatially modulated co-attention Usage There are no extra compiled components in SMCA DETR and package dependencies are

peng gao 135 Dec 07, 2022
Invertible conditional GANs for image editing

Invertible Conditional GANs This is the implementation of the IcGAN model proposed in our paper: Invertible Conditional GANs for image editing. Novemb

Guim 278 Dec 12, 2022
Real-time analysis of intracranial neurophysiology recordings.

py_neuromodulation Click this button to run the "Tutorial ML with py_neuro" notebooks: The py_neuromodulation toolbox allows for real time capable pro

Interventional Cognitive Neuromodulation - Neumann Lab Berlin 15 Nov 03, 2022
Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax

Clockwork VAEs in JAX/Flax Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax, ported

Julius Kunze 26 Oct 05, 2022
Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking."

Expert-Linking Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking." This is

BoChen 12 Jan 01, 2023
Expand human face editing via Global Direction of StyleCLIP, especially to maintain similarity during editing.

Oh-My-Face This project is based on StyleCLIP, RIFE, and encoder4editing, which aims to expand human face editing via Global Direction of StyleCLIP, e

AiLin Huang 51 Nov 17, 2022
Video Autoencoder: self-supervised disentanglement of 3D structure and motion

Video Autoencoder: self-supervised disentanglement of 3D structure and motion This repository contains the code (in PyTorch) for the model introduced

157 Dec 22, 2022
EssentialMC2 Video Understanding

EssentialMC2 Introduction EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2( relatio

Alibaba 106 Dec 11, 2022
The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach

The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach

ycj_project 1 Jan 18, 2022
RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

State Entropy Maximization with Random Encoders for Efficient Exploration (RE3) (ICML 2021) Code for State Entropy Maximization with Random Encoders f

Younggyo Seo 47 Nov 29, 2022