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Add initial files for refactor and upgrade
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‎.gitignore

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@@ -111,6 +111,12 @@ venv.tar.gz
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.idea
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.vscode
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# TensorBoard
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tb_logs/
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# Feature Processing
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*work_filenames*.csv
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# DIPS
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project/datasets/DIPS/complexes/**
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project/datasets/DIPS/interim/**
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project/datasets/DIPS/raw/**
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project/datasets/DIPS/final/raw/**
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project/datasets/DIPS/final/final_raw_dips.tar.gz*
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project/datasets/DIPS/final/processed/**
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# DB5
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project/datasets/DB5/processed/**
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project/datasets/DB5/raw/**
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project/datasets/DB5/interim/**
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project/datasets/DB5/final/raw/**
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project/datasets/DB5/final/final_raw_db5.tar.gz*
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project/datasets/DB5/final/processed/**
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# EVCoupling
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project/datasets/EVCoupling/raw/**
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project/datasets/CASP-CAPRI/raw/**
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project/datasets/CASP-CAPRI/interim/**
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project/datasets/CASP-CAPRI/final/raw/**
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project/datasets/CASP-CAPRI/final/processed/**
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project/datasets/CASP-CAPRI/final/processed/**
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# Input
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project/datasets/Input/**

‎README.md

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@@ -26,7 +26,7 @@ The Enhanced Database of Interacting Protein Structures for Interface Prediction
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* Benchmark results included in our paper were run after this issue was resolved
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* However, if you ran experiments using DB5-Plus' filename list for its test complexes, please re-run them using the latest list
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## How to run creation tools
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## How to set up
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First, download Mamba (if not already downloaded):
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```bash
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pip3 install -e .
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```
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## Default DIPS-Plus directory structure
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To install PSAIA for feature generation, install GCC 10 for PSAIA:
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```bash
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# Install GCC 10 for Ubuntu 20.04:
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sudo apt install software-properties-common
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sudo add-apt-repository ppa:ubuntu-toolchain-r/ppa
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sudo apt update
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sudo apt install gcc-10 g++-10
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# Or install GCC 10 for Arch Linux/Manjaro:
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yay -S gcc10
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```
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Then install QT4 for PSAIA:
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```bash
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# Install QT4 for Ubuntu 20.04:
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sudo add-apt-repository ppa:rock-core/qt4
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sudo apt update
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sudo apt install libqt4* libqtcore4 libqtgui4 libqtwebkit4 qt4* libxext-dev
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# Or install QT4 for Arch Linux/Manjaro:
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yay -S qt4
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```
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Conclude by compiling PSAIA from source:
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```bash
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# Select the location to install the software:
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MY_LOCAL=~/Programs
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# Download and extract PSAIA's source code:
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mkdir "$MY_LOCAL"
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cd "$MY_LOCAL"
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wget http://complex.zesoi.fer.hr/data/PSAIA-1.0-source.tar.gz
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tar -xvzf PSAIA-1.0-source.tar.gz
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# Compile PSAIA (i.e., a GUI for PSA):
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cd PSAIA_1.0_source/make/linux/psaia/
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qmake-qt4 psaia.pro
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make
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# Compile PSA (i.e., the protein structure analysis (PSA) program):
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cd ../psa/
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qmake-qt4 psa.pro
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make
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# Compile PIA (i.e., the protein interaction analysis (PIA) program):
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cd ../pia/
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qmake-qt4 pia.pro
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make
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# Test run any of the above-compiled programs:
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cd "$MY_LOCAL"/PSAIA_1.0_source/bin/linux
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# Test run PSA inside a GUI:
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./psaia/psaia
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# Test run PIA through a terminal:
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./pia/pia
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# Test run PSA through a terminal:
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./psa/psa
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```
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Lastly, install Docker following the instructions from https://docs.docker.com/engine/install/
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## How to generate protein feature inputs
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In our [feature generation notebook](notebooks/feature_generation.ipynb), we provide examples of how users can generate the protein features described in our [accompanying manuscript](https://arxiv.org/abs/2106.04362) for individual protein inputs.
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## How to use data
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In our [data usage notebook](notebooks/data_usage.ipynb), we provide examples of how users might use DIPS-Plus (or DB5-Plus) for downstream analysis or prediction tasks. For example, to train a new NeiA model with DB5-Plus as its cross-validation dataset, first download DB5-Plus' raw files and process them via the `data_usage` notebook:
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```bash
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mkdir -p project/datasets/DB5/final
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wget https://zenodo.org/record/5134732/files/final_raw_db5.tar.gz -O project/datasets/DB5/final/final_raw_db5.tar.gz
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tar -xzf project/datasets/DB5/final/final_raw_db5.tar.gz -C project/datasets/DB5/final/
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# To process these raw files for training and subsequently train a model:
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python3 notebooks/data_usage.py
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```
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## Standard DIPS-Plus directory structure
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```
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DIPS-Plus
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│
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└───project
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│ │
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│ └───datasets
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│ │ │
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│ │ └───builder
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│ │ │
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│ │ └───DB5
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│ │ │ │
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│ │ │ └───final
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│ │ │ │ │
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│ │ │ │ └───raw
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│ │ │ │
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│ │ │ └───interim
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│ │ │ │ │
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│ │ │ │ └───complexes
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│ │ │ │ │
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│ │ │ │ └───external_feats
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│ │ │ │ │
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│ │ │ │ └───pairs
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│ │ │ │
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│ │ │ └───raw
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│ │ │ │
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│ │ │ README
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│ │ │
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│ │ └───DIPS
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│ │ │
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│ │ └───filters
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│ │ │
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│ │ └───final
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│ │ │ │
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│ │ │ └───raw
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│ │ │
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│ │ └───interim
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│ │ │ │
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│ │ │ └───complexes
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│ │ │ │
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│ │ │ └───external_feats
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│ │ │ │
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│ │ │ └───pairs-pruned
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│ │ │
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│ │ └───raw
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│ │ │
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│ │ └───pdb
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│ │
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│ └───utils
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│ constants.py
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│ utils.py
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│
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.gitignore
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environment.yml
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LICENSE
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README.md
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requirements.txt
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setup.cfg
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setup.py
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│
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└───datasets
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│
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└───DB5
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│ │
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│ └───final
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│ │ │
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│ │ └───processed # task-ready features for each dataset example
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│ │ │
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│ │ └───raw # generic features for each dataset example
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│ │
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│ └───interim
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│ │ │
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│ │ └───complexes # metadata for each dataset example
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│ │ │
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│ │ └───external_feats # features curated for each dataset example using external tools
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│ │ │
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│ │ └───pairs # pair-wise features for each dataset example
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│ │
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│ └───raw # raw PDB data downloads for each dataset example
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│
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└───DIPS
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│
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└───filters # filters to apply to each (un-pruned) dataset example
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│
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└───final
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│ │
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│ └───processed # task-ready features for each dataset example
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│ │
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│ └───raw # generic features for each dataset example
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│
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└───interim
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│ │
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│ └───complexes # metadata for each dataset example
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│ │
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│ └───external_feats # features curated for each dataset example using external tools
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│ │
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│ └───pairs-pruned # filtered pair-wise features for each dataset example
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│ │
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│ └───parsed # pair-wise features for each dataset example after initial parsing
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│
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└───raw
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│
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└───pdb # raw PDB data downloads for each dataset example
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```
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## How to compile DIPS-Plus from scratch
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rm project/datasets/DIPS/final/raw/pairs-postprocessed.txt project/datasets/DIPS/final/raw/pairs-postprocessed-train.txt project/datasets/DIPS/final/raw/pairs-postprocessed-val.txt project/datasets/DIPS/final/raw/pairs-postprocessed-test.txt
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# Create data directories (if not already created):
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mkdir project/datasets/DIPS/raw project/datasets/DIPS/raw/pdb project/datasets/DIPS/interim project/datasets/DIPS/interim/external_feats project/datasets/DIPS/final project/datasets/DIPS/final/raw project/datasets/DIPS/final/processed
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mkdir project/datasets/DIPS/raw project/datasets/DIPS/raw/pdb project/datasets/DIPS/interim project/datasets/DIPS/interim/pairs-pruned project/datasets/DIPS/interim/external_feats project/datasets/DIPS/final project/datasets/DIPS/final/raw project/datasets/DIPS/final/processed
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# Download the raw PDB files:
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rsync -rlpt -v -z --delete --port=33444 --include='*.gz' --include='*.xz' --include='*/' --exclude '*' \
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# Generate externally-sourced features:
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python3 project/datasets/builder/generate_psaia_features.py "$PSAIADIR" "$PROJDIR"/project/datasets/builder/psaia_config_file_dips.txt "$PROJDIR"/project/datasets/DIPS/raw/pdb "$PROJDIR"/project/datasets/DIPS/interim/parsed "$PROJDIR"/project/datasets/DIPS/interim/pairs-pruned "$PROJDIR"/project/datasets/DIPS/interim/external_feats --source_type rcsb
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python3 project/datasets/builder/generate_hhsuite_features.py "$PROJDIR"/project/datasets/DIPS/interim/parsed "$PROJDIR"/project/datasets/DIPS/interim/pairs-pruned "$HHSUITE_DB" "$PROJDIR"/project/datasets/DIPS/interim/external_feats --num_cpu_jobs 4 --num_cpus_per_job 8 --num_iter 2 --source_type rcsb --write_file
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python3 project/datasets/builder/generate_hhsuite_features.py "$PROJDIR"/project/datasets/DIPS/interim/parsed "$PROJDIR"/project/datasets/DIPS/interim/pairs-pruned "$HHSUITE_DB" "$PROJDIR"/project/datasets/DIPS/interim/external_feats --num_cpu_jobs 4 --num_cpus_per_job 8 --num_iter 2 --source_type rcsb --write_file # Note: After this, one needs to re-run this command with `--read_file` instead
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# Generate multiple sequence alignments (MSAs) using a smaller sequence database (if not already created using the standard BFD):
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DOWNLOAD_DIR="$HHSUITE_DB_DIR" && ROOT_DIR="${DOWNLOAD_DIR}/small_bfd" && SOURCE_URL="https://storage.googleapis.com/alphafold-databases/reduced_dbs/bfd-first_non_consensus_sequences.fasta.gz" && BASENAME=$(basename "${SOURCE_URL}") && mkdir --parents "${ROOT_DIR}" && aria2c "${SOURCE_URL}" --dir="${ROOT_DIR}" && pushd "${ROOT_DIR}" && gunzip "${ROOT_DIR}/${BASENAME}" && popd # e.g., Download the small BFD
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python3 project/datasets/builder/generate_hhsuite_features.py "$PROJDIR"/project/datasets/DIPS/interim/parsed "$PROJDIR"/project/datasets/DIPS/interim/pairs-pruned "$HHSUITE_DB_DIR"/small_bfd "$PROJDIR"/project/datasets/DIPS/interim/external_feats --num_cpu_jobs 4 --num_cpus_per_job 8 --num_iter 2 --source_type rcsb --generate_msa_only --write_file # Note: After this, one needs to re-run this command with `--read_file` instead
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# Identify interfaces within intrinsically disordered regions (IDRs) #
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# (1) Pull down the Docker image for `flDPnn`
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docker pull docker.io/sinaghadermarzi/fldpnn
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# (2) For all sequences in the dataset, predict which interface residues reside within IDRs
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python3 project/datasets/builder/annotate_idr_interfaces.py "$PROJDIR"/project/datasets/DIPS/final/raw
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# Add new features to the filtered pairs, ensuring that the pruned pairs' original PDB files are stored locally for DSSP:
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python3 project/datasets/builder/download_missing_pruned_pair_pdbs.py "$PROJDIR"/project/datasets/DIPS/raw/pdb "$PROJDIR"/project/datasets/DIPS/interim/pairs-pruned --num_cpus 32 --rank "$1" --size "$2"

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