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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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Updated: Jun 25, 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

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features.

Aman Sawhney¹, Jiefu Li², Li Liao¹

¹Department of Computer and Information Sciences, University of Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE 19716, USA.

International Journal of Molecular Sciences

|May 25, 2024

Summary

This summary is machine-generated.

Predicted protein 3D structures from AlphaFold2 can directly improve residue contact prediction. Leveraging structural features from these models significantly enhances contact prediction accuracy beyond current state-of-the-art methods.

Keywords:

Alpha helix AlphaFold contact map prediction machine learning neural networks protein structure protein structure modeling transmembrane proteins

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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
Bioinformatics

Background:

Residue contact maps are crucial for understanding protein structure and function.
Traditional contact prediction relies solely on sequential features.
Recent advances in 3D structure prediction, like AlphaFold2, offer new opportunities.

Purpose of the Study:

To evaluate the direct use of AlphaFold2 predicted 3D structures for residue contact prediction.
To investigate leveraging 3D structural features for improved contact prediction.
To develop and test novel methods for enhanced residue contact prediction.

Main Methods:

Utilized a benchmark dataset for inter-helical residue contact prediction.
Assessed contact prediction accuracy using AlphaFold2 predicted structures.
Developed a feature extraction method from atomic structures near residue pairs.
Trained models using features from experimentally determined and AlphaFold2-predicted structures.

Main Results:

Directly using AlphaFold2 structures yielded 83% average precision, outperforming sequential methods.
Incorporating 3D structural features significantly improved contact prediction.
Achieved over 91.9% average precision on a held-out set and 89.5% in cross-validation.

Conclusions:

Predicted 3D protein structures are valuable for direct residue contact prediction.
Leveraging 3D structural features offers a powerful approach to enhance contact prediction accuracy.
This work surpasses the performance of AlphaFold2 in residue contact prediction tasks.