构建了人类蛋白质多模态嵌入数据库，助力药物靶点发现。
A Multimodal Human Protein Embeddings Database: DeepDrug Protein Embeddings Bank (DPEB)
- 整合四种蛋白嵌入：结构、序列、进化模式和词袋统计。
- 图神经网络在蛋白互作预测上达87.37% AUROC，分类准确率超77%。
- 适合系统生物学与药物研发人员使用，支持多种建模方法。
由于缺乏集成的多模态蛋白表示，计算预测蛋白-蛋白互作（PPI）面临挑战。DPEB 是一个精心整理的人类蛋白嵌入库，包含 22,043 个蛋白质，融合了四种嵌入类型：基于 AlphaFold2 的结构嵌入、基于 Transformer 的序列嵌入（BioEmbeddings）、基于 ESM-2 的上下文氨基酸模式嵌入，以及基于 n-gram 统计的序列嵌入（ProtVec）。尽管 AlphaFold2 蛋白结构可通过公开数据库获取，其内部神经网络嵌入仍不可得。DPEB 填补了这一空白，提供了 AlphaFold2 衍生的嵌入以支持计算建模。基准评估显示，结合 BioEmbedding 的 GraphSAGE 在 PPI 预测中表现最佳，达到 87.37% AUROC 与 79.16% 准确率；在酶分类任务中准确率为 77.42%，蛋白家族分类为 86.04%。DPEB 支持多种图神经网络方法，可用于系统生物学、药物靶点识别、通路分析及疾病机制研究。
原文摘要 · Abstract (English)
Computationally predicting protein-protein interactions (PPIs) is challenging due to the lack of integrated, multimodal protein representations. DPEB is a curated collection of 22,043 human proteins that integrates four embedding types: structural (AlphaFold2), transformer-based sequence (BioEmbeddings), contextual amino acid patterns (ESM-2: Evolutionary Scale Modeling), and sequence-based n-gram statistics (ProtVec]). AlphaFold2 protein structures are available through public databases (e.g., AlphaFold2 Protein Structure Database), but the internal neural network embeddings are not. DPEB addresses this gap by providing AlphaFold2-derived embeddings for computational modeling. Our benchmark evaluations show GraphSAGE with BioEmbedding achieved the highest PPI prediction performance (87.37% AUROC, 79.16% accuracy). The framework also achieved 77.42% accuracy for enzyme classification and 86.04% accuracy for protein family classification. DPEB supports multiple graph neural network methods for PPI prediction, enabling applications in systems biology, drug target identification, pathway analysis, and disease mechanism studies.
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