The clinical classification of germline missense variants and single-amino-acid deletions is challenging. The BayesDel and Align-GVGD bioinformatic prediction tools currently used for ClinGen TP53 variant curation expert panel (VCEP) classification do not directly capture changes in protein folding stability, measured using computed destabilization energies (ΔΔG scores). The AlphaMissense tool recently developed by Google DeepMind to predict pathogenicity for all human proteome missense variants is trained in part using AlphaFold2 architecture. Our study investigated whether protein folding stability and/or AlphaMissense scores could improve impact prediction for p53 missense and single-amino-acid deletion variants. ΔΔG scores were calculated for missense variants using FoldX and for single-amino-acid deletions using an AlphaFold2/RosettaRelax protocol. Residue surface exposure was categorized using relative solvent accessibility (RSA) measures. The predictive values of ΔΔG scores, AlphaMissense, BayesDel, and Align-GVGD were examined using Boruta and binary logistic regression based on functionally defined reference sets. The likelihood ratio (LR) toward pathogenicity was estimated and used to refine optimal categories for predicting variant pathogenicity for different RSA values. We showed that current VCEP predictive approaches for missense variants were improved by integrating ΔΔG scores ≥2.5 kcal/mol for partially buried and buried residues, but better performance was achieved using AlphaMissense with ΔΔG and RSA. For deletion variants, ΔΔG scores ≥4.8 Rosetta energy unit (REU) in buried residues outperformed currently used predictive approaches. Future TP53 VCEP specifications for p53 missense impact prediction may consider AlphaMissense, ΔΔG score, and RSA combined for substitution variants and ΔΔG score alone for deletion variants.