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Energy metric prediction for double insertion mutants via the RoseNet deep learning framework.

Sarah Coffland¹, Katie Christensen¹, Brian Hutchinson^1,2

¹Computer Science Department, Western Washington University, Washington, 98225, United States.

Bioinformatics Advances

|June 4, 2025

Summary

RoseNet predicts protein energy changes from double amino acid insertions or deletions (InDels). The neural network generalizes better to new residue combinations and performs well in beta-sheets and high SASA regions.

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Area of Science:

Computational biology
Bioinformatics
Protein engineering

Background:

Understanding protein mutation impacts is crucial.
Previous work introduced the RoseNet neural network architecture.
RoseNet predicts energy metrics for protein mutations.

Purpose of the Study:

Evaluate RoseNet's prediction accuracy for double amino acid insertions/deletions (InDels).
Analyze how protein domain features influence RoseNet's performance.
Expand RoseNet's applicability to new proteins and mutation types.

Main Methods:

Trained RoseNet models on benchmark datasets with exhaustive and random double InDel mutations.
Evaluated model performance across six proteins.
Analyzed the impact of secondary structures (alpha-helix, beta-sheet) and solvent accessible surface area (SASA) on predictions.

Main Results:

RoseNet demonstrates superior generalization to unseen residue combinations compared to unseen insertion positions.
Predictions are more accurate when InDels occur in beta-sheets versus alpha-helices.
Higher performance is observed when InDels are in regions with high SASA.

Conclusions:

RoseNet is a proficient tool for predicting energy metrics of proteins with double InDels.
Protein structural features significantly impact RoseNet's prediction accuracy.
Further research can refine RoseNet for specific structural contexts.