B-P.06: Evaluating Confidence Scores in OpenFold3 for Protein Interaction Prediction
Accurate prediction of protein protein interactions remains a central challenge in computational biology, particularly in the context of scalable, high-throughput analyses. Recent advances in structure prediction models have introduced confidence metrics, such as interface predicted TM-score (ipTM), that are increasingly used as proxies for interaction likelihood in protein complexes. However, the extent to which such metrics reliably capture true interaction signals remains insufficiently understood.
In this work, we investigate the determinants and limitations of ipTM as a scoring function for protein protein interactions. We analyze how residue-level properties, such as structural confidence and spatial relationships, contribute to the resulting scores, with the aim of clarifying the signals that drive scores predictions. We further explore how broader contextual factors, such as the composition of protein assemblies, influence these metrics, and assess their robustness under varying modeling conditions.
In addition, we examine practical considerations related to the computational cost of large-scale interaction prediction, exploring avenues to make such approaches more efficient and broadly applicable. We also consider potential directions for improving current scoring strategies, with the goal of enabling more reliable assessment of protein protein interactions.
Together, our findings contribute to a deeper understanding of confidence metrics in protein complex prediction.
Co-authors: Damien Legros, Joana Pereira, Iker Reinares Zapata
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