Research Appraisalother

AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores

Protein science : a publication of the Protein SocietyDall'Armellina, Filippo, Urbé, Sylvie, Rigden, Daniel J1 May 2026DOI

Clinical Snapshot

95CEBM
Evidence: Moderateother

PICO Framework

P — PopulationOxysterol-binding protein-related proteins (ORPs) and phosphoinositide 3-, 4-, and 5-phosphatases (~200 protein pairs)
I — InterventionSystematic screening using AlphaPulldown2, AlphaFold2-Multimer, and AlphaFold3 with new generation confidence scoring
C — ComparatorNo direct comparator - computational prediction study
O — OutcomesProtein-protein interaction predictions, interface confidence scores, functionally conserved binding modes, particularly SAC1-ORP11 interactions

Bottom Line

This computational study systematically screened protein-protein interactions between oxysterol-binding proteins and phosphoinositide phosphatases using advanced structural prediction methods. The researchers identified functionally conserved binding modes, particularly between SAC1 phosphatase and ORP11, using multiple confidence scoring systems. While the computational approach is methodologically sound and comprehensive, the predictions require experimental validation before clinical application. The work provides valuable insights into membrane contact site biology and lipid transport mechanisms, offering a foundation for future therapeutic target identification in metabolic disorders. The study represents high-quality computational biology research but lacks the experimental validation necessary for immediate clinical translation.

Evidence: Moderate

Key Findings

  • P Value: Not applicable - computational prediction study

  • Effect Size: Strong predicted interaction between SAC1 and ORP11

  • Primary Outcome: Identification of functionally conserved binding modes between SAC1 lipid phosphatase and ORP family proteins

  • Nnt Or Sensitivity: Multiple confidence metrics: ipTM + pTM, actifpTM, ipSAE scoring for interaction assessment

  • Confidence Interval: Not applicable - computational confidence scores provided

Clinical Application

High feasibility for research laboratories with computational resources Relevant to Australian research institutions studying membrane biology and metabolic disorders, no direct TGA or PBS implications as this is basic research Research applications in understanding membrane biology and lipid transport disorders

Abstract

Non-vesicular lipid transport contributes to the regulation of membrane composition and organelle function at membrane contact sites. OSBP-related proteins (ORPs) are central to this process, yet their interaction networks remain incompletely defined. Here, we systematically screened potential interactions between ORPs and phosphoinositide 3-, 4-, and 5-phosphatases using AlphaPulldown2, AlphaFold2-Multimer, and AlphaFold3. We established a protocol for model generation by combining AlphaFold2-Multimer predictions (including five-replicates) with an AlphaPulldown2 interaction screen across around 200 protein pairs, and with AlphaFold3 predictions including lipid-bound and multimeric assemblies. Interface confidence was assessed for consistency using the weighted ipTM + pTM metric, actifpTM, new generation ipSAE scoring, and FoldSeek-Multimer clustering. We further evaluated the protein pairs' biological plausibility based on subcellular localization data, in silico membrane insertion, evolutionary conservation via ConSurf, and protein binding interface analysis using the deep learning tool PeSTo. This integrative protocol uncovered functionally conserved binding modes in the SAC1 lipid phosphatase with the ORP family, particularly with ORP11, and predicted functionally relevant protein-lipid interfaces.

References

  1. 1.Dall'Armellina, F., Urbé, S., & Rigden, D. J. (2026). AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores. Protein Science, 35(5). https://doi.org/10.1093/bioinformatics/btad424
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