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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:22

Protein Folding

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Related Experiment Videos

Leveraging Neural Networks to Correct FoldX Free Energy Estimates.

Jonathan E Barnes1, L América Chi1,2,3, Shubham Kumar Pandey2

  • 1Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States.

ACS Omega
|June 1, 2026
PubMed
Summary

This study introduces dFX, a neural network that enhances FoldX's protein mutation stability predictions. dFX improves accuracy by learning from FoldX energy terms, aiding in faster and more reliable computational structural biology.

Related Experiment Videos

Area of Science:

  • Computational structural biology
  • Protein engineering
  • Bioinformatics

Background:

  • Predicting protein folding stability and binding affinity changes from mutations is crucial but challenging.
  • Experimental methods are accurate but slow and expensive.
  • Computational tools like FoldX offer speed but have limited accuracy.

Purpose of the Study:

  • To develop a computational framework, dFX, that improves the accuracy of FoldX free energy predictions for protein mutations.
  • To leverage neural networks to learn residual corrections based on FoldX energy terms.

Main Methods:

  • Assembled a dataset of experimental ΔΔG values and corresponding 3D protein structures.
  • Generated baseline FoldX ΔΔG predictions and their energy terms.
  • Trained a neural network (dFX) using FoldX energy terms as input to predict the difference between experimental and FoldX ΔΔG.

Main Results:

  • dFX models demonstrated improved prediction accuracy and Pearson correlation compared to FoldX.
  • dFX showed capability in predicting higher-order mutations and improved epistasis prediction.
  • An external SARS-CoV-2 dataset confirmed dFX's superior performance over FoldX and other ML methods for binding predictions.

Conclusions:

  • dFX offers a computationally efficient way to enhance FoldX predictions with minimal added time.
  • The framework shows potential for applications like predicting antibody escape and antibody development.
  • dFX represents a valuable addition to computational pipelines for protein stability and binding affinity analysis.