A Multimodal Human Protein Embeddings Database: DeepDrug Protein Embeddings Bank (DPEB)

• URL: http://arxiv.org/abs/2510.22008v1
• Date: Fri, 24 Oct 2025 20:22:17 GMT
• Title: A Multimodal Human Protein Embeddings Database: DeepDrug Protein Embeddings Bank (DPEB)
• Authors: Md Saiful Islam Sajol, Magesh Rajasekaran, Hayden Gemeinhardt, Adam Bess, Chris Alvin, Supratik Mukhopadhyay,
• Abstract summary: DPEB is a curated collection of 22,043 human proteins that integrates four embedding types.<n> DPEB supports multiple graph neural network methods for PPI prediction.
• Score: 0.3822990432531661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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