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Updated: Aug 23, 2025
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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
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A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder
Apolline Bruley1, Tristan Bitard-Feildel1, Isabelle Callebaut1
1Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France.
Proteins
|October 28, 2022
Summary
We developed pyHCA, a Python package to predict protein foldability from amino acid sequences. This tool helps distinguish ordered and disordered protein regions, complementing AlphaFold2 predictions.
Area of Science:
- Protein structure and function
- Bioinformatics
- Computational biology
Background:
- Protein function is dictated by order and disorder, with diverse manifestations.
- Deciphering this diversity from amino acid sequences remains challenging.
Purpose of the Study:
- To develop a Python package, pyHCA, for estimating protein segment foldability using only amino acid sequence information.
- To analyze the ratio of order to disorder in protein sequences.
Main Methods:
- Utilized the hydrophobic cluster analysis (HCA) approach to measure density in regular secondary structures.
- Optimized the tool by separating foldable segments from disorder (DisProt) and order (SCOPe, OPM) databases.
- Applied pyHCA to proteomes of 21 species, comparing results with AlphaFold protein structure database models.
Main Results:
- pyHCA successfully estimates protein foldability based on sequence data.
- Distinguished low-confidence scores related to disorder from foldable sequences missed by AlphaFold2.
- Identified sequences excluded from accurate modeling due to lack of homologs or compositional biases.
Conclusions:
- pyHCA is a valuable tool for analyzing protein order and disorder.
- Complements AlphaFold2 by providing insights into structural innovations and evolutionary processes.
- Aids in understanding protein structural diversity at proteome and gene scales.
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