Neural Embeddings for Protein Graphs
Francesco Ceccarelli Lorenzo Giusti Sean B. Holden Pietro Liò
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 particular, 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.