# Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction

## TL;DR

This paper introduces a new framework for predicting protein functions using improved attention mechanisms and multimodal data fusion, achieving better performance than existing methods.

## Contribution

The novel Graph-CBAM module and dual-head self-attention pooling enhance multimodal fusion for protein function prediction.

## Key findings

- The model outperforms benchmarks on all Gene Ontology branches with 2.9% Fmax improvement on Biological Process.
- AUPR increases by 3.9% on Cellular Component branch.
- Ablation studies confirm the effectiveness of structural embeddings and Graph-CBAM.

## Abstract

Protein function
prediction has advanced substantially
with the
integration of AlphaFold2 structural information, yet current models
remain constrained by incomplete multimodal feature fusion and limited
attention mechanisms for capturing structural–functional relationships.
Here, we present an enhanced framework that overcomes these limitations
through three innovations: (i) Graph-CBAM, the first adaptation of
convolutional block attention to graph neural networks for fine-grained
structural feature extraction; (ii) complete multimodal fusion of
Node2vec structural embeddings with amino acid one-hot encodings;
and (iii) a dual-head self-attention pooling module that stabilizes
node importance estimation. Extensive experiments on human protein
data sets demonstrate that our model consistently outperforms existing
benchmarks across all Gene Ontology branches. We report pronounced
improvements, including an increase in Fmax by 2.9% on the Biological
Process (BP) branch (0.481 to 0.495) and an enhancement of AUPR by
3.9% on the Cellular Component (CC) branch (0.763 to 0.793). Performance
for Molecular Function (MF) remains competitive. Ablation analyses
further confirm the independent contributions of structural embeddings,
one-hot encodings, and Graph-CBAM. Overall, this work provides a more
complete and practical solution for AlphaFold2-based protein function
prediction, with particular advantages in predicting functions of
proteins lacking protein–protein interaction data.