ArticleGenome biology2025
SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- NanoBind: Mechanism-Driven Deep Learning of Nanobody-Antigen Molecular Recognition.Research (Washington, D.C.) · 2026Article
- SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions.Genome biology · 2025Article
- DTBind: A Mechanism-Driven Deep Learning Framework for Accurate Prediction of Drug-Target Molecular Recognition.Research (Washington, D.C.) · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Intrinsically disordered proteins and regions (IDRs) lack stable 3D structures, posing challenges for interaction prediction. We present SpatPPI, a geometric deep learning model tailored for IDPPI prediction. SpatPPI leverages structural cues from folded domains to guide the dynamic adjustment of IDRs via geometric modeling, adaptive conformation refinement, and a two-stage decoding mechanism. It captures spatial variability without requiring supervised input and achieves state-of-the-art performance on benchmark datasets. Molecular dynamics simulations further validate its high adaptability to conformational changes in IDRs and strong capacity to generate distinct and structure-aware embeddings. A freely accessible server is available at http://liulab.top/SpatPPI/server .
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Registered trials
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