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Protein Folding

Protein Folding

Protein Folding Quality Check in the RER

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...

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
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Updated: Sep 23, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

AlphaFold2: A Role for Disordered Protein/Region Prediction?

Carter J Wilson^1,2, Wing-Yiu Choy³, Mikko Karttunen^2,4,5

¹Department of Mathematics, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada.

International Journal of Molecular Sciences

|May 14, 2022

Summary

AlphaFold2 effectively predicts disordered protein regions, but accuracy depends on the prediction method. The predicted local distance difference test (pLDDT) shows promise for characterizing protein dynamics.

Keywords:

AlphaFold2 IDPs/IDRs biophysics disordered proteins machine-learning molecular dynamics simulation structural bioinformatics

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Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

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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

Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

AlphaFold2 has revolutionized protein structure prediction, impacting structural biology.
Accurate prediction of intrinsically disordered proteins and regions (IDPs/IDRs) remains a challenge.
Traditional sequence-based disorder predictors have limitations.

Purpose of the Study:

To evaluate AlphaFold2's capability in predicting disordered protein regions.
To compare AlphaFold2's performance against established sequence-based disorder predictors.
To investigate methods for deriving disorder predictions from AlphaFold2 structures.

Main Methods:

Comparative analysis of AlphaFold2 predictions with traditional sequence-based disorder predictors.
Development and testing of different disorder prediction strategies from AlphaFold2 structural outputs.
Utilizing molecular dynamics (MD) simulations to explore relationships between predicted structure and dynamics.

Main Results:

AlphaFold2 demonstrates good performance in discriminating ordered from disordered regions.
The accuracy of disorder prediction is highly dependent on the method used to interpret AlphaFold2 structures.
A simple heuristic classifying secondary structure elements as ordered leads to overestimation of disorder.
The predicted local distance difference test (pLDDT) emerges as a robust indicator of residue-wise disorder.
MD simulations reveal a correlation between pLDDT and secondary structure, offering insights into local dynamics.

Conclusions:

AlphaFold2 is a valuable tool for identifying disordered regions, but requires careful interpretation of its output.
The pLDDT metric derived from AlphaFold2 structures is a reliable proxy for residue disorder and local dynamics.
Findings suggest potential for pLDDT in characterizing the dynamic behavior of IDPs/IDRs beyond simple disorder prediction.