PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to
  Graphs
Authors: name: Xinzhe Zheng; name: Hao Du; name: Fanding Xu; name: Jinzhe Li; name: Zhiyuan Liu; name: Wenkang Wang; name: Tao Chen; name: Wanli Ouyang; name: Stan Z. Li; name: Yan Lu; name: Nanqing Dong; name: Yang Zhang
Published: 2025-07-07 15:21:05

Abstract: Deep learning-based computational methods have achieved promising results in
predicting protein-protein interactions (PPIs). However, existing benchmarks
predominantly focus on isolated pairwise evaluations, overlooking a model's
capability to reconstruct biologically meaningful PPI networks, which is
crucial for biology research. To address this gap, we introduce PRING, the
first comprehensive benchmark that evaluates protein-protein interaction
prediction from a graph-level perspective. PRING curates a high-quality,
multi-species PPI network dataset comprising 21,484 proteins and 186,818
interactions, with well-designed strategies to address both data redundancy and
leakage. Building on this golden-standard dataset, we establish two
complementary evaluation paradigms: (1) topology-oriented tasks, which assess
intra and cross-species PPI network construction, and (2) function-oriented
tasks, including protein complex pathway prediction, GO module analysis, and
essential protein justification. These evaluations not only reflect the model's
capability to understand the network topology but also facilitate protein
function annotation, biological module detection, and even disease mechanism
analysis. Extensive experiments on four representative model categories,
consisting of sequence similarity-based, naive sequence-based, protein language
model-based, and structure-based approaches, demonstrate that current PPI
models have potential limitations in recovering both structural and functional
properties of PPI networks, highlighting the gap in supporting real-world
biological applications. We believe PRING provides a reliable platform to guide
the development of more effective PPI prediction models for the community. The
dataset and source code of PRING are available at
https://github.com/SophieSarceau/PRING.