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
    Secure Distributed Training at Scale

    Secure Distributed Training at Scale This repository contains the implementation of experiments from the paper "Secure Distributed Training at Scale"

    Yandex Research 9 Jul 11, 2022
    A set of examples around hub for creating and processing datasets

    Examples for Hub - Dataset Format for AI A repository showcasing examples of using Hub Uploading Dataset Places365 Colab Tutorials Notebook Link Getti

    Activeloop 11 Dec 14, 2022
    THIS IS THE **OLD** PYMC PROJECT. PLEASE USE PYMC3 INSTEAD:

    Introduction Version: 2.3.8 Authors: Chris Fonnesbeck Anand Patil David Huard John Salvatier Web site: https://github.com/pymc-devs/pymc Documentation

    PyMC 7.2k Jan 07, 2023
    Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

    Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive

    <a href=[email protected](SZ)"> 7 Dec 16, 2021
    Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks

    Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks Requirements python 0.10+ rdkit 2020.03.3.0 biopython 1.78 openbabel 2.4

    Neeraj Kumar 3 Nov 23, 2022
    The Habitat-Matterport 3D Research Dataset - the largest-ever dataset of 3D indoor spaces.

    Habitat-Matterport 3D Dataset (HM3D) The Habitat-Matterport 3D Research Dataset is the largest-ever dataset of 3D indoor spaces. It consists of 1,000

    Meta Research 62 Dec 27, 2022
    Codes for the compilation and visualization examples to the HIF vegetation dataset

    High-impedance vegetation fault dataset This repository contains the codes that compile the "Vegetation Conduction Ignition Test Report" data, which a

    1 Dec 12, 2021
    CS506-Spring2022 - Code and Slides for Boston University CS 506

    CS 506 - Computational Tools for Data Science Code, slides, and notes for Boston

    Lance Galletti 17 May 06, 2022
    Funnels: Exact maximum likelihood with dimensionality reduction.

    Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

    2 Apr 21, 2022
    End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021)

    PDVC Official implementation for End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021) [paper] [valse论文速递(Chinese)] This repo supports:

    Teng Wang 118 Dec 16, 2022
    On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition

    On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition With the spirit of reproducible research, this repository contains codes requ

    0 Feb 24, 2022
    Code for 1st place solution in Sleep AI Challenge SNU Hospital

    Sleep AI Challenge SNU Hospital 2021 Code for 1st place solution for Sleep AI Challenge (Note that the code is not fully organized) Refer to the notio

    Saewon Yang 13 Jan 03, 2022
    2.86% and 15.85% on CIFAR-10 and CIFAR-100

    Shake-Shake regularization This repository contains the code for the paper Shake-Shake regularization. This arxiv paper is an extension of Shake-Shake

    Xavier Gastaldi 294 Nov 22, 2022
    Tensorflow AffordanceNet and AffContext implementations

    AffordanceNet and AffContext This is tensorflow AffordanceNet and AffContext implementations. Both are implemented and tested with tensorflow 2.3. The

    Beatriz Pérez 6 Dec 01, 2022
    Multi Agent Path Finding Algorithms

    MATP-solver Simulator collision check path step random initial states or given states Traditional method Seperate A* algorithem Confict-based Search S

    30 Dec 12, 2022
    Geometric Deep Learning Extension Library for PyTorch

    Documentation | Paper | Colab Notebooks | External Resources | OGB Examples PyTorch Geometric (PyG) is a geometric deep learning extension library for

    Matthias Fey 16.5k Jan 08, 2023
    Code and data for paper "Deep Photo Style Transfer"

    deep-photo-styletransfer Code and data for paper "Deep Photo Style Transfer" Disclaimer This software is published for academic and non-commercial use

    Fujun Luan 9.9k Dec 29, 2022
    GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.

    The GT4SD (Generative Toolkit for Scientific Discovery) is an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-t

    Generative Toolkit 4 Scientific Discovery 142 Dec 24, 2022
    [ICCV2021] 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds

    3DVG-Transformer This repository is for the ICCV 2021 paper "3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds" Our method "3DV

    22 Dec 11, 2022
    All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

    All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

    Daniel Bourke 3.4k Jan 07, 2023