DOI: 10.5281/zenodo.15600639
title: SPACE: STRING proteins as complementary embeddings

description: This repository holds the dataset used and generated in the research: SPACE: STRING proteins as complementary embeddings (biorxiv). For reimplementation, please refer to the GitHub repository: https://github.com/deweihu96/SPACE Data descriptions:



benchmarks.zip: benchmark datasets, including the subcellular localization dataset from DeepLoc 2.0 and the function prediction dataset from NetGO 2.0; please cite the corresponding papers if you use them:


Thumuluri, Vineet, et al. "DeepLoc 2.0: multi-label subcellular localization prediction using protein language models." Nucleic acids research 50.W1 (2022): W228-W234.

Yao, Shuwei, et al. "NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information." Nucleic acids research 49.W1 (2021): W469-W475.



benchmark_results.zip: benchmark results.

eggnog.zip: ortholog groups organized per taxonomy level, sourced from eggNOG 6.0; please cite the eggNOG database if you use this dataset:


Hernández-Plaza, Ana, et al. "eggNOG 6.0: enabling comparative genomics across 12 535 organisms." Nucleic Acids Research 51.D1 (2023): D389-D394.



euk_seed_groups.json: seed species kingdoms.

euks_ancestors.tsv: species pairs and their latest common ancestor.

euks.txt: all eukaryotic species used in the project.

functional_embeddings.zip: the aligned, cross-species functional network embeddings, 512 dimensions. Files are per species.

node2vec.zip: single species network embeddings generated by node2vec, 128 dimensions.


The aligned functional embeddings and ProtT5 embeddings are also available from the STRING database.
description: Representation learning has revolutionized sequence-based prediction of protein function and subcellular localization. Protein networks are an important source of information complementary to sequences, but the use of protein networks has proven to be challenging in the context of machine learning, especially in a cross-species setting. To address this, we leveraged the STRING database of protein networks and orthology relations for 1,322 eukaryotes to generate network-based cross-species protein embeddings. We did this by first creating species-specific network embeddings and subsequently aligning them based on orthology relations to facilitate direct cross-species comparisons. We show that these aligned network embeddings ensure consistency across species without sacrificing quality compared to species-specific network embeddings. We also show that the aligned network embeddings are complementary to sequence embedding techniques, despite the use of seqeuence-based orthology relations in the alignment process. Finally, we validated the embeddings by using them for two well-established tasks: subcellular localization prediction and protein function prediction. Training logistic regression classifiers on aligned network embeddings and sequence embeddings improved the accuracy over using sequence alone, reaching performance numbers close to state-of-the-art deep-learning methods. The precomputed cross-species network embeddings and ProtT5 embeddings for all eukaryotic proteins have been included in STRING version 12.0