Grape-Pi: Graph-Based Neural Networks for Enhanced Protein Identification in Proteomics Pipelines

Authors

Chunhui Gu
Seyyed Mahmood Ghasemi
Yining Cai
Johannes F Fahrmann
James P Long
Hiroyuki Katayama
Chong Wu
Jody Vykoukal
Jennifer B Dennison
Samir Hanash
Kim-Anh Do
Ehsan Irajizad

Publication Date

1-1-2025

Journal

Bioinformatics Advances

Abstract

Motivation: Protein identification via mass spectrometry (MS) is the primary method for untargeted protein detection. However, the identification process is challenging due to data complexity and the need to control false discovery rates (FDR) of protein identification. To address these challenges, we developed a graph neural network (GNN)-based model, Graph Neural Network using Protein-Protein Interaction for Enhancing Protein Identification (Grape-Pi), which is applicable to all proteomics pipelines. This model leverages protein-protein interaction (PPI) data and employs two types of message-passing layers to integrate evidence from both the target protein and its interactors, thereby improving identification accuracy.

Results: Grape-Pi achieved significant improvements in area under receiver-operating characteristic curve (AUC) in differentiating present and absent proteins: 18% and 7% in two yeast samples and 9% in gastric samples over traditional methods in the test dataset. Additionally, proteins identified via Grape-Pi in gastric samples demonstrated a high correlation with mRNA data and identified gastric cancer proteins, like MAP4K4, missed by conventional methods.

Availability and implementation: Grape-Pi is freely available at https://zenodo.org/records/11310518 and https://github.com/FDUguchunhui/GrapePi.

DOI

10.1093/bioadv/vbaf095

PMID

40406669

PMCID

PMC12096076

PubMedCentral® Posted Date

4-26-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes