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Protein Folding

Protein Folding

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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.
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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...

Conservation of Protein Domains Over Different Proteins

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Protein and Protein Structure

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Related Experiment Video

Updated: Jan 10, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Low-N Protein Activity Optimization with FolDE.

Jacob B Roberts^1,2,3, Catherine R Ji⁴, Isaac Donnell^1,3,5

¹Joint BioEnergy Institute, Emeryville, CA, USA.

|November 24, 2025

Summary

This summary is machine-generated.

FolDE enhances protein optimization by intelligently selecting mutants for testing. This active learning-assisted directed evolution (ALDE) method improves prediction accuracy and discovers superior protein variants more efficiently.

Keywords:

ALDE Low-N Protein Engineering Reinforcement Learning

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Published on: November 8, 2024

Area of Science:

Protein engineering
Computational biology
Biotechnology

Background:

Protein optimization traditionally requires extensive experimental screening of mutants.
Active Learning-assisted Directed Evolution (ALDE) reduces costs by predicting beneficial mutations.
Current ALDE methods suffer from homogeneous training data, limiting prediction accuracy in later rounds.

Purpose of the Study:

To develop a novel ALDE method, FolDE, to maximize success in protein optimization campaigns.
To improve the accuracy and efficiency of predicting beneficial protein mutations.

Main Methods:

FolDE utilizes naturalness-based warm-starting, integrating protein language model outputs with experimental data.
A constant-liar batch selector is introduced to enhance batch diversity for multi-mutation campaigns.
The method was evaluated through simulations across 20 diverse protein targets.

Main Results:

FolDE identified 23% more top 10% mutants compared to baseline methods (p=0.005).
FolDE demonstrated a 55% higher likelihood of discovering top 1% mutants.
Naturalness-based warm-starting significantly improved activity prediction by augmenting limited measurements.

Conclusions:

FolDE represents a significant advancement in ALDE for protein engineering.
The method enhances the discovery of high-performing protein variants.
FolDE, available as open-source software, democratizes efficient protein optimization.