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Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical

Eli J Draizen1,2, John Readey3, Cameron Mura4,5

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA. edraizen@gmail.com.

BMC Bioinformatics
|January 4, 2024
PubMed
Summary

Prop3D is a new platform for creating and sharing featurized protein domain datasets for machine learning (ML). It provides the Prop3D-20sf dataset, aiding reproducible ML pipeline development while preventing data leakage.

Keywords:
Deep learningMachine learningMassively parallel workflowsProtein structureStructural bioinformatics

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Area of Science:

  • Structural Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Machine learning (ML), particularly deep learning, is advancing structural bioinformatics by revealing complex relationships in biomolecular data.
  • High-quality, accessible datasets are crucial for ML but challenging to create, especially for complex 3D protein structures.
  • Reproducible training and testing datasets are essential for reliable ML model development in structural bioinformatics.

Purpose of the Study:

  • Introduce Prop3D, a platform for creating, sharing, and reusing featurized protein domain libraries.
  • Provide the Prop3D-20sf dataset, derived from CATH, for machine learning applications.
  • Facilitate the development of robust and reproducible ML pipelines in structural bioinformatics.

Main Methods:

  • Developed the Prop3D framework for systematic and reproducible dataset creation using the Highly Scalable Data Service (HSDS).
  • Featurized protein domains with diverse biophysical and evolutionary properties, from atomic details to residue-level conservation.
  • Deployed the Prop3D framework on cloud and HPC resources for scalable data generation.

Main Results:

  • Prop3D enables the creation and sharing of protein domain libraries with rich feature sets.
  • The Prop3D-20sf dataset, containing featurized protein domains from 20 CATH families, is now available.
  • Datasets are accessible via a public HSDS instance and compatible with popular ML frameworks through Python wrappers.

Conclusions:

  • Prop3D offers a customizable workflow for scalable dataset generation on cloud platforms via HSDS.
  • The Prop3D-20sf dataset accelerates ML pipeline development and ensures reproducibility.
  • Dataset construction in Prop3D-20sf addresses data leakage concerns arising from protein evolutionary relationships.