A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions

Related tags

Deep Learningstructural-biologystructural-bioinformaticsalphafoldalphafold2
Overview

AlphaFold Analyser

This program produces high quality visualisations of predicted structures produced by AlphaFold. These visualisations allow the user to view the pLDDT of each residue of a protein structure and the predicted alignment error for the entire protein to rapidly infer the quality of a predicted structure.

Dependencies

  • Python 3.7
  • AlphaFold 2.0.0
  • PyMol 2.5.2
  • Matplotlib 3.4.2

Installing AlphaFold Analyser on Linux & MacOSX

At the command line, change directory to the directory where alphafold-analyser.py was downloaded, , using the full path name.

cd <download-directory>

Now move the file to where you normally keep your binaries. This directory should be in your path. Note: you may require administrative privileges to do this (either switching user to root or by using sudo).

As root:

mv alphafold-analyser.py /usr/local/bin/

As regular user:

sudo mv alphafold-analyser.py /usr/local/bin/

alphafold-analyser.py should now run from the shell or Terminal using the command alphafold-analyser.py

Alternatively, alphafold-analyser.py can be run directly from an IDE.

AlphaFold Settings for the Analyser

For the programme to function correctly, the model names parameter should label the first two models in alphafold as model_1 and model_2_ptm. An example of how this parameter should be written when running AlphaFold is shown below.

--model_names=model_1,model_2_ptm,model_3,model_4,model_5 \

model_2_ptm is used to collect the data required to plot the Predicted Alignment Error.

All files output by alphafold are stored in a single directory. However, only the ranked_0.pdb and results_model_2_ptm.pkl file are needed for analysis.

Running AlphaFold Analyser

A directory should be created containing all necessary files (see above). AlphaFold Analyser will then ask for the following inputs:

Input Directory: The file path for the directory containing the alphafold results files

Output Directory: The file path for the directory where the Analyser results will be stored.

Protein: The name of protein being analysed. This will be used to label all files and the directory created during the analysis

Outputs

AlphaFold Analyser has produces two outputs:

  • A PyMol session labelled with the protein input (e.g protein.pse). This will contain the highest confidence structure predicted by AlphaFold. The individual residues of the structure are coloured according to their pLDDT on colour spectrum from yellow to green to blue (low to high confidence).
  • A predicted alignment error plot again labelled with the protein input (e.g protein-pae.png). The plot is colored by the confidence values for each residue using the same colour scheme as the PyMol session.

    • Comments

    Future work may involve allowing for multiple inputs at once.

    You might also like...
    Code for the TIP 2021 Paper
    Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

    PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

    Jinming Su 4 Aug 25, 2022
    TensorFlow code for the neural network presented in the paper:
    TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

    SLM: Structural Language Models of Code This is an official implementation of the model described in: "Structural Language Models of Code" [PDF] To ap

    null 73 Nov 6, 2022
    Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

    Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

    NeuLab 40 Dec 23, 2022
    [CVPR 2021] A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
    [CVPR 2021] A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts

    Visual-Reasoning-eXplanation [CVPR 2021 A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts] Project Page | Vid

    Andy_Ge 54 Dec 21, 2022
    A PyTorch implementation of
    A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

    GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

    Benedek Rozemberczki 259 Dec 5, 2022
    Towards Interpretable Deep Metric Learning with Structural Matching
    Towards Interpretable Deep Metric Learning with Structural Matching

    DIML Created by Wenliang Zhao*, Yongming Rao*, Ziyi Wang, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for paper Towards Interpr

    Wenliang Zhao 75 Nov 11, 2022
    PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
    PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

    StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

    SJTU-ViSYS 112 Nov 28, 2022
    The (Official) PyTorch Implementation of the paper
    The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

    MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc

    Miaomiao Li 82 Jan 2, 2023
    A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.
    A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

    Image Classification in Python Implementing image classification in Flask using Keras. The VGG16 is a convolution neural network model architecture th

    Gerald Maduabuchi 19 Dec 12, 2022
    Releases(v1.0.1)
    Owner
    Oliver Powell
    Biochemistry student @ UEA
    Oliver Powell
    GitHub Repository
    Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

    Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN If you use this code for your research, please cite ou

    41 Dec 08, 2022
    This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive Selective Coding)

    HCSC: Hierarchical Contrastive Selective Coding This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive

    YUANFAN GUO 111 Dec 20, 2022
    METER: Multimodal End-to-end TransformER

    METER Code and pre-trained models will be publicized soon. Citation @article{dou2021meter, title={An Empirical Study of Training End-to-End Vision-a

    Zi-Yi Dou 257 Jan 06, 2023
    This game was designed to encourage young people not to gamble on lotteries, as the probablity of correctly guessing the number is infinitesimal!

    Lottery Simulator 2022 for Web Launch Application Developed by John Seong in Ontario. This game was designed to encourage young people not to gamble o

    John Seong 2 Sep 02, 2022
    Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

    Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

    Computational Linguistics Research Group 8.4k Jan 03, 2023
    Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

    Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

    Terbe Dániel 138 Dec 17, 2022
    An implementation for `Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction`

    Text2Event An implementation for Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction Please contact Yaojie Lu (@

    Roger 153 Jan 07, 2023
    Learning Skeletal Articulations with Neural Blend Shapes

    This repository provides an end-to-end library for automatic character rigging and blend shapes generation as well as a visualization tool. It is based on our work Learning Skeletal Articulations wit

    Peizhuo 504 Dec 30, 2022
    A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks

    PixelSSL is a PyTorch-based semi-supervised learning (SSL) codebase for pixel-wise (Pixel) vision tasks. The purpose of this project is to promote the

    Zhanghan Ke 255 Dec 11, 2022
    Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks

    Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks This is the official code for DyReg model inroduced in Discovering Dyna

    Bitdefender Machine Learning 11 Nov 08, 2022
    This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

    deSpeckNet-TF-GEE This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling publi

    Adugna Mullissa 16 Sep 07, 2022
    Continual Learning of Electronic Health Records (EHR).

    Continual Learning of Longitudinal Health Records Repo for reproducing the experiments in Continual Learning of Longitudinal Health Records (2021). Re

    Jacob 7 Oct 21, 2022
    Revealing and Protecting Labels in Distributed Training

    Revealing and Protecting Labels in Distributed Training

    Google Interns 0 Nov 09, 2022
    CL-Gym: Full-Featured PyTorch Library for Continual Learning

    CL-Gym: Full-Featured PyTorch Library for Continual Learning CL-Gym is a small yet very flexible library for continual learning research and developme

    Iman Mirzadeh 36 Dec 25, 2022
    [ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

    FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

    Shihua Huang 23 Jul 22, 2022
    Cupytorch - A small framework mimics PyTorch using CuPy or NumPy

    CuPyTorch CuPyTorch是一个小型PyTorch，名字来源于： 不同于已有的几个使用NumPy实现PyTorch的开源项目，本项目通过CuPy支持

    Xingkai Yu 23 Aug 17, 2022
    PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentation.

    Shape-aware Convolutional Layer (ShapeConv) PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentatio

    Hanchao Leng 82 Dec 29, 2022
    Python based framework for Automatic AI for Regression and Classification over numerical data.

    Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

    BlobCity, Inc 141 Dec 21, 2022
    This is Official implementation for "Pose-guided Feature Disentangling for Occluded Person Re-Identification Based on Transformer" in AAAI2022

    PFD：Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer This repo is the official implementation of "Pose-gui

    Tao Wang 93 Dec 18, 2022
    Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"

    CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2

    Chenhongyi Yang 21 Dec 13, 2022