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

Naoki Tomita1, George Chikenji1

  • 1Department of Applied Physics, Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan.

Protein Science : a Publication of the Protein Society
|July 10, 2026
PubMed
Summary

A new machine learning model, RINAMI, accurately predicts protein folding free energy (ΔG) using sequence and structure. This computational tool helps evaluate novel protein designs before experimental testing.

Keywords:
machine learningprotein language modelprotein stability

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Area of Science:

  • Computational Biology
  • Protein Engineering
  • Machine Learning

Background:

  • De novo protein design generates novel proteins, but assessing folding stability remains a challenge.
  • Reliable computational methods are needed to quantify folding free energy (ΔG) and distinguish successful designs.
  • Current methods struggle to accurately predict if a designed protein's target structure is thermodynamically favored.

Purpose of the Study:

  • To develop an accurate and interpretable machine learning model for predicting absolute protein folding free energy (ΔG).
  • To provide a computational tool for evaluating and prioritizing de novo protein designs.
  • To address the fundamental challenge of assessing folding stability in novel protein sequences.

Main Methods:

  • Developed RINAMI (Residue-attributed Interpretable Neural network for predicting Absolute folding free energy by Merging structure and sequence Information).
  • Integrated structure-based and sequence-based representations using ProteinMPNN and Evolutionary Scale Modeling 2 (ESM2).
  • Employed a multi-head cross-attention mechanism to contextualize sequence signals within the structural environment.

Main Results:

  • RINAMI accurately predicts absolute folding free energy (ΔG) for both natural and designed proteins.
  • The model outperforms existing approaches in correlation with experimental measurements and prediction errors.
  • An ablation study confirmed the importance of integrating sequence and structure information for accuracy.
  • RINAMI demonstrated interpretability by identifying key physicochemical effects influencing protein stability.

Conclusions:

  • RINAMI provides an accurate and interpretable framework for predicting protein folding free energy (ΔG).
  • The model serves as a practical computational tool for assessing and prioritizing protein designs.
  • This advancement facilitates the reliable generation of novel proteins with desired stability.