Methods for the parallel and distributed analysis and mining of the Protein Data Bank using MMTF and Apache Spark.
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README.rst

MMTF PySpark

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mmtfPyspark is a python package that provides APIs and sample applications for distributed analysis and scalable mining of 3D biomacromolecular structures, such as the Protein Data Bank (PDB) archive. mmtfPyspark uses Big Data technologies to enable high-performance parallel processing of macromolecular structures. mmtfPyspark use the following technology stack:

  • Apache Spark a fast and general engine for large-scale distributed data processing.
  • MMTF the Macromolecular Transmission Format for compact data storage, transmission and high-performance parsing
  • Hadoop Sequence File a Big Data file format for parallel I/O
  • Apache Parquet a columnar data format to store dataframes

This project is under development.

Documentation

Documentation In Depth Tutorial

Installation

Python

We strongly recommend that you have anaconda and we require at least python 3.6 installed. To check your python version:

python --version

If Anaconda is installed, and if you have python 3.6, the above command should return:

Python 3.6.4 :: Anaconda, Inc.

mmtfPyspark and dependencies

Since mmtfPyspark uses parallel computing to ensure high-performance, it requires additional dependencies such as Apache Spark. Therefore, please read follow the installation instructions for your OS carefully:

MacOS and LINUX

Windows

Hadoop Sequence Files

This project uses the PDB archive in the form of MMTF Hadoop Sequence File. The files can be downloaded by:

curl -O https://mmtf.rcsb.org/v1.0/hadoopfiles/full.tar
tar -xvf full.tar

curl -O https://mmtf.rcsb.org/v1.0/hadoopfiles/reduced.tar
tar -xvf reduced.tar

For Mac and Linux, the Hadoop sequence files can be downloaded and saved as environmental variables by running the following command:

curl https://raw.githubusercontent.com/sbl-sdsc/mmtf-pyspark/master/bin/download_mmtf_files.sh -o download_mmtf_files.sh
. ./download_mmtf_files.sh

How to Cite this Work

Bradley AR, Rose AS, Pavelka A, Valasatava Y, Duarte JM, Prlić A, Rose PW (2017) MMTF - an efficient file format for the transmission, visualization, and analysis of macromolecular structures. PLOS Computational Biology 13(6): e1005575. doi: 10.1371/journal.pcbi.1005575

Valasatava Y, Bradley AR, Rose AS, Duarte JM, Prlić A, Rose PW (2017) Towards an efficient compression of 3D coordinates of macromolecular structures. PLOS ONE 12(3): e0174846. doi: 10.1371/journal.pone.01748464

Rose AS, Bradley AR, Valasatava Y, Duarte JM, Prlić A, Rose PW (2018) NGL viewer: web-based molecular graphics for large complexes, Bioinformatics, bty419. doi: 10.1093/bioinformatics/bty419

Rose AS, Bradley AR, Valasatava Y, Duarte JM, Prlić A, Rose PW (2016) Web-based molecular graphics for large complexes. In Proceedings of the 21st International Conference on Web3D Technology (Web3D '16). ACM, New York, NY, USA, 185-186. doi: 10.1145/2945292.2945324

Funding

This project is supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.