RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging...

Recent advances in de novo protein design have enabled the generation of diverse novel proteins. However, a fundamental challenge remains: even when an amino acid sequence is designed with the target structure as the most stable conformation, there is currently no reliable computational method for assessing whether the target structure is sufficiently stabilized relative to alternative conformations. While experimental realization of the intended fold requires the target structure to be thermodynamically favored by a large free-energy gap, the absence of a quantitative measure of folding stability makes it difficult to distinguish reliable from unreliable designs. Here, we propose the Residue-attributed Interpretable Neural network for predicting Absolute folding free energy by Merging structure and sequence Information (RINAMI), a machine learning model that predicts the absolute folding free energy (ΔG) of proteins from their three-dimensional structures and amino acid sequences. RINAMI integrates structure- and sequence-based representations derived from ProteinMPNN and Evolutionary Scale Modeling 2 (ESM2) using a multi-head cross-attention mechanism that contextualizes sequence-derived signals within the structural environment. Benchmarking RINAMI on both natural and designed proteins from the Mega-scale and Maxwell datasets shows that it outperforms the tested existing approaches, achieving higher correlations with experimental measurements and improved or comparable prediction errors. An ablation study supports the contribution of sequence-structure integration for predictive accuracy. In addition, RINAMI exhibits strong interpretability by capturing key physicochemical effects, including the destabilizing effect of buried hydrophilic residues, the stabilizing effect of buried hydrophobic residues, and the characteristics of cysteine. Together, these results establish RINAMI as an accurate and interpretable framework for ΔG prediction and provide a practical computational tool for evaluating and prioritizing protein designs prior to experimental testing.