# Protein Function Prediction: Graph Neural Networks as Multimodal Aggregators of Sequences, Networks, and Ontologies

Antoine Toffano, Jérôme Azé, Pierre Larmande

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Antoine Toffano, Jérôme Azé, Pierre Larmande. Protein Function Prediction: Graph Neural Networks as Multimodal Aggregators of Sequences, Networks, and Ontologies. JOBIM 2025 - 25es Journées Ouvertes en Biologie, Informatique et Mathématiques, Jul 2025, Bordeaux, France. . hal-05194109

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Submitted on 31 Jul 2025

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# Protein Function Prediction: Graph Neural Networks as Multimodal Aggregators of Sequences, Networks, and Ontologies

Antoine Toffano<fnref n="1" />, Jérôme Azé<fnref n="1" /> and Pierre Larmande<fnref n="2" />

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## Problem Statement

Manual curation of protein functions in the form of Gene Ontology terms is notoriously slow, and machine learning has been the go-to solution.

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**GENEONTOLOGY**
Unifying Biology [1]

Problem modelisation as a multi-label prediction task, where each label is a term in the Gene Ontology.

However, little is known about which data modalities contribute the most to accurate predictions.

To assess their respective impact, we run experiments that isolate and combine sequence, structure, network, and ontology features.

## Data & Modalities

For a given protein, the following informations are encoded and integrated to the model:

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## Model

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The model uses a feature fusion layer (A) to merge a protein's informations and a graph neural network layer (B) to incorporate its neighbor's.

## Modalities Contribution

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## Network Thresholds

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Link existence in the network was set according to threshold scores for both link types.

## Benchmark Comparison

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## Conclusion

* Network information is essential to good prediction performance.

* Modalities complement each other, up to a certain point.

* Combining sequence, network and adjacent proteins GO annotations works best.

* Too much information leads to noise and worse models.

## References

[1] Ashburner et al., 2000 - Gene Ontology: Tool for the Unification of Biology

[2] Paysan-Lafosse et al., 2022 - InterPro in 2022

[3] Buchfink et al., 2015 - Fast and Sensitive Protein Alignment Using DIAMOND

[4] Szklarczyk et al., 2025 - STRING Database in 2025: Protein Networks with Directionality of Regulation

<footnote n="1">¹LIRMM, Univ. Montpellier, Montpellier France,</footnote>

<footnote n="2">²DIADE, IRD, Univ. Montpellier, CIRAD, Montpellier, France</footnote>