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Authors: Francesco Ceccarelli 1 ; Lorenzo Giusti 2 ; Sean B. Holden 1 and Pietro Liò 1

Affiliations: 1 Department of Computer Science and Technology , University of Cambridge, Cambridge, U.K. ; 2 Department of Computer, Control and Management Engineering, Sapienza University, Rome, Italy

Keyword(s): Graph Neural Networks, Large Language Models, Protein Representation Learning.

Abstract: Proteins perform much of the work in living organisms, and consequently the development of efficient computational methods for protein representation is essential for advancing large-scale biological research. Most current approaches struggle to efficiently integrate the wealth of information contained in the protein sequence and structure. In this paper, we propose a novel framework for embedding protein graphs in geometric vector spaces, by learning an encoder function that preserves the structural distance between protein graphs. Utilizing Graph Neural Networks (GNNs) and Large Language Models (LLMs), the proposed framework generates structure- and sequence-aware protein representations. We demonstrate that our embeddings are successful in the task of comparing protein structures, while providing a significant speed-up compared to traditional approaches based on structural alignment. Our framework achieves remarkable results in the task of protein structure classification; in part icular, when compared to other work, the proposed method shows an average F1-Score improvement of 26% on out-of-distribution (OOD) samples and of 32% when tested on samples coming from the same distribution as the training data. Our approach finds applications in areas such as drug prioritization, drug re-purposing, disease sub-type analysis and elsewhere. (More)

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Paper citation in several formats:
Ceccarelli, F.; Giusti, L.; B. Holden, S. and Liò, P. (2024). Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMs. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 582-593. DOI: 10.5220/0012453600003654

@conference{icpram24,
author={Francesco Ceccarelli. and Lorenzo Giusti. and Sean {B. Holden}. and Pietro Liò.},
title={Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMs},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={582-593},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012453600003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMs
SN - 978-989-758-684-2
IS - 2184-4313
AU - Ceccarelli, F.
AU - Giusti, L.
AU - B. Holden, S.
AU - Liò, P.
PY - 2024
SP - 582
EP - 593
DO - 10.5220/0012453600003654
PB - SciTePress