From ∆∆G Prediction to Mutation Prioritization with Conformal Prediction

Selecting mutations for protein engineering and studies of protein function requires deciding which predicted stability changes warrant experimental follow-up. A point estimate alone does not show whether uncertainty leaves both stabilizing and destabilizing effects plausible. To address this limitation, we introduce ProbJanusDDG, which combines a protein-stability predictor with standard and adaptive conformal prediction intervals. Calibration assigns equal total weight to each protein, accounting for uneven numbers of measured mutations. For previously uncharacterized mutations, intervals lying entirely above or below zero provide a criterion for selecting candidate stabilizing or destabilizing substitutions, while intervals containing zero leave the direction unresolved. On the external low-homology S669L benchmark, adaptive intervals at the 50% nominal level assigned directional calls to 34.2% of mutations, with 94.8% observed sign accuracy. At the 70% level, 15.5% received calls, with 99.0% sign accuracy. At the 90% nominal level, standard and adaptive intervals achieved mutation-level coverage of 91.6% and 92.5%, whereas coverage averaged equally across proteins was approximately 87%. Adaptive intervals redistributed width across mutations, with greater widening among predictions with larger observed errors and nearly unchanged mean width. These results support uncertainty-aware screening of uncharacterized variants: prioritizing candidate stabilizing mutations for protein engineering and candidate destabilizing mutations for investigating stability-related effects on protein function. The method provides a selective criterion for experimental follow-up while distinguishing predictions with a resolved direction from those requiring further evidence.