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Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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A fully open-source framework for deep learning protein real-valued distances.

Badri Adhikari¹

¹Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO, 63132, USA. adhikarib@umsl.edu.

Scientific Reports

|August 10, 2020

Summary

This summary is machine-generated.

We introduce Protein Distance NET (PDNET), a framework with a curated dataset and scripts to accelerate deep learning for protein structure prediction. PDNET aids in developing novel methods for inter-residue distance prediction.

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Area of Science:

Computational biology
Structural bioinformatics
Deep learning

Background:

Deep learning significantly advances protein structure prediction.
Inter-residue distance prediction is crucial for accurate protein models.
Current deep learning methods for distance prediction are nascent.

Purpose of the Study:

To develop a specialized dataset and framework for advancing deep learning in protein structure prediction.
To facilitate faster development and testing of novel deep learning methods for inter-residue distance prediction.
To provide tools for training and testing models in accessible environments like Google Colab.

Main Methods:

Introduction of the Protein Distance NET (PDNET) framework.
Curated a small, representative dataset for protein structure prediction tasks.
Provided scripts for dataset curation, feature generation, and distance map creation.
Enabled training and testing of deep learning models via web browsers (e.g., Google Colab).

Main Results:

PDNET offers a streamlined approach to developing deep learning models for protein structure prediction.
The framework supports the prediction of contacts, distance intervals, and real-valued distances.
Facilitates reproducible research and faster iteration in the field.

Conclusions:

PDNET addresses the need for efficient datasets and tools in deep learning for protein structure prediction.
The framework is versatile, supporting various prediction tasks and accessible platforms.
PDNET is poised to accelerate progress in computational structural biology.