PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to
  Graphs
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.
686dc2d1cb5725779c60b342
Xinzhe Zheng
claimed_verified
2025-07-09T08:50:37.420Z
6682a6a75f3099b62d7aa802
/avatars/ca9d41a94d508f0afd3516b1805cd2a2.svg
Xinzhe Zheng
user
piaolaidangqu
686dc2d1cb5725779c60b343
Hao Du
686dc2d1cb5725779c60b344
Fanding Xu
686dc2d1cb5725779c60b345
Jinzhe Li
claimed_verified
2025-07-09T08:50:35.772Z
67658bd7f7ac7e978ab6f957
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/c8VBgFckkZNUGeqUyotwq.png
Jinzhe Li
user
JinzheFudan
686dc2d1cb5725779c60b346
Zhiyuan Liu
686dc2d1cb5725779c60b347
Wenkang Wang
686dc2d1cb5725779c60b348
Tao Chen
686dc2d1cb5725779c60b349
Wanli Ouyang
686dc2d1cb5725779c60b34a
Stan Z. Li
686dc2d1cb5725779c60b34b
Yan Lu
686dc2d1cb5725779c60b34c
Nanqing Dong
claimed_verified
2025-09-26T12:24:53.909Z
68385a89ecf59de6ef0510ca
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/6qZ0KijBOhFri0ue3T1pB.jpeg
Nanqing Dong
user
eveningdong
686dc2d1cb5725779c60b34d
Yang Zhang
PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to
  Graphs
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.
PRING is a comprehensive benchmark for evaluating protein-protein interaction prediction from a graph-level perspective, addressing both network topology and protein function.
protein-protein interactions PPIs graph-level perspective topology-oriented tasks function-oriented tasks protein complex pathway prediction GO module analysis essential protein justification sequence similarity-based naive sequence-based protein language model-based structure-based approaches