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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

Protein and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

Protein and Protein Structures

Protein and Protein Structures

Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key...

Conservation of Protein Domains

Conservation of Protein Domains

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Related Experiment Video

Updated: May 20, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

DeepProtein: deep learning library and benchmark for protein sequence learning.

Jiaqing Xie¹, Tianfan Fu², Tianfan Fu³

¹Computer Science Department, ETH Zurich, Zürich 8006, Switzerland.

Bioinformatics (Oxford, England)

|May 19, 2025

Summary

This summary is machine-generated.

DeepProtein is a new deep learning library for protein science tasks. It includes benchmark results and fine-tuned models (DeepProt-T5) that achieve state-of-the-art performance.

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Published on: July 16, 2017

Area of Science:

Computational Biology
Bioinformatics
Protein Science

Background:

Deep learning significantly impacts protein science, aiding in predicting protein properties, structures, and interactions.
Advancements in deep learning offer new avenues for analyzing complex biological data.

Purpose of the Study:

Introduce DeepProtein, a user-friendly deep learning library for protein-related research.
Provide a benchmark for evaluating deep learning models on various protein tasks.
Present DeepProt-T5 models that achieve state-of-the-art performance.

Main Methods:

Developed DeepProtein based on the DeepPurpose library.
Established a benchmark for evaluating deep learning architectures on protein function, subcellular localization, protein-protein interaction, and protein structure prediction.
Fine-tuned Prot-T5 models to create DeepProt-T5.

Main Results:

DeepProtein offers a comprehensive suite of tools for protein data analysis.
The benchmark evaluation provides insights into the performance of different deep learning architectures.
DeepProt-T5 models achieved state-of-the-art results on four benchmark tasks and competitive results on six others.

Conclusions:

DeepProtein enhances accessibility to deep learning for protein science.
The library and benchmark facilitate reproducible research and model development.
DeepProt-T5 models represent a significant advancement in predictive accuracy for protein-related tasks.