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Updated: Jan 18, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

SPACE: STRING proteins as complementary embeddings.

Dewei Hu¹, Damian Szklarczyk^2,3, Christian von Mering^2,3

¹Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark.

Bioinformatics (Oxford, England)

|September 9, 2025

Summary

This summary is machine-generated.

We developed cross-species protein embeddings using protein networks and orthology, improving protein function and localization prediction. These network embeddings complement sequence data for enhanced machine learning in eukaryotes.

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Last Updated: Jan 18, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Representation learning advances sequence-based protein function and localization prediction.
Protein networks offer complementary data but pose cross-species machine learning challenges.

Purpose of the Study:

To generate cross-species protein embeddings from protein networks for 1322 eukaryotes.
To align species-specific network embeddings using orthology for cross-species comparisons.
To evaluate the utility of these aligned embeddings for protein function and localization prediction.

Main Methods:

Leveraged the STRING database for protein networks and orthology relations.
Generated species-specific network embeddings, then aligned them using orthology.
Validated aligned network embeddings with sequence embeddings for prediction tasks.

Main Results:

Aligned network embeddings maintain quality and consistency across species.
Aligned network embeddings are complementary to sequence embeddings.
Using aligned network and sequence embeddings improved prediction accuracy for subcellular localization and protein function.

Conclusions:

Cross-species protein network embeddings enhance predictive model performance.
The developed method provides a robust way to integrate network information across species.
This approach approaches state-of-the-art deep learning performance for protein prediction tasks.