标题： MP-GCAN：一种高精度的分类器，用于$α$-螺旋膜蛋白和$β$-桶状蛋白 显示英文标题

标题： MP-GCAN: a highly accurate classifier for $α$-helical membrane proteins and $β$-barrel proteins

摘要： 膜蛋白分类是结构生物信息学中的基础任务，对于理解蛋白质功能和加速药物发现至关重要。 在本研究中，我们提出了MP-GCAN，一种基于图的分类模型，利用蛋白质的空间和序列特征。 MP-GCAN结合了GCN、GAT和GIN层，从3D蛋白质图中捕获分层结构表示，这些图是从包含$\alpha$-碳坐标和残基类型的高分辨率PDB文件构建的。 为了评估性能，我们整理了一个高质量的数据集，包含500个膜蛋白和500个非膜蛋白，并将MP-GCAN与两个基线进行了比较：一个基于结构置信度的SGD分类器，利用AlphaFold的pLDDT得分，以及DeepTMHMM，一个基于序列的深度学习模型。 我们的实验表明，MP-GCAN显著优于基线，在两类上均达到了96%的准确率和强大的F1分数。 结果强调了将预训练的GNN架构与领域特定的结构数据相结合的重要性，以提高膜蛋白分类的效果。 显示英文摘要

摘要： Membrane protein classification is a fundamental task in structural bioinformatics, critical to understanding protein functions and accelerating drug discovery. In this study, we propose MP-GCAN, a novel graph-based classification model that leverages both spatial and sequential features of proteins. MP-GCAN combines GCN, GAT, and GIN layers to capture hierarchical structural representations from 3D protein graphs, constructed from high-resolution PDB files with $\alpha$-carbon coordinates and residue types. To evaluate performance, we curated a high-quality dataset of 500 membrane and 500 non-membrane proteins, and compared MP-GCAN with two baselines: a structure-confidence-based SGD classifier utilizing AlphaFold's pLDDT scores, and DeepTMHMM, a sequence-based deep learning model. Our experiments demonstrate that MP-GCAN significantly outperforms baselines, achieving an accuracy of 96% and strong F1-scores on both classes. The results highlight the importance of integrating pretrained GNN architectures with domain-specific structural data to enhance membrane protein classification.