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This paper addresses the misinterpretation of AlphaFold confidence scores as direct probability estimates of structural quality by calibrating these scores to predict the probability of a protein complex exceeding a specific DockQ threshold. Through post-hoc isotonic calibration on antibody-antigen and general multimer datasets, the study demonstrates improved agreement between predicted probabilities and observed success rates, while preserving the original score ordering. The calibrated probabilities offer more accurate estimations of the proportion of selected models likely to meet desired structural quality benchmarks.
AlphaFold confidence scores can be recalibrated to accurately predict the probability of achieving a desired interface quality, transforming rankings into actionable probabilistic assessments.
AlphaFold confidence scores are widely used to rank protein complex predictions and to select subsets of models for downstream applications. Although these scores provide strong discriminatory power for distinguishing prediction quality, their numerical values were never intended to represent the probability that a prediction satisfies a given structural quality criterion. In this work, we calibrate commonly used confidence scores to estimate the probability that a predicted complex exceeds a chosen threshold of DockQ, an established measure of interface quality. On antibody-antigen and general multimer datasets, post-hoc isotonic calibration consistently improves agreement between predicted probabilities and observed DockQ success rates while preserving the underlying score ordering. The calibrated probabilities also provide more accurate estimates of the fraction of selected models expected to pass a given structural quality threshold. Although the mapping is fitted within a given prediction distribution and therefore transfers imperfectly across prediction domains, we show that reliable estimates are recovered from a small number of labeled target-domain samples. Because the calibration is entirely post hoc and requires no underlying AlphaFold model fine-tuning, it provides a straightforward and customizable way to convert existing model quality rankings into calibrated probabilities of passing a chosen structural quality threshold.
Max Planck