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Protein Folding

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...

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AlphaFold3 for Noncanonical Cyclic Peptide Modeling: Hierarchical Benchmarking Reveals Accuracy and Practical

Chengyun Zhang¹, Wentong Wang², Ning Zhu¹

¹Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Journal of Chemical Information and Modeling

|August 29, 2025

Summary

AlphaFold3's ability to predict noncanonical cyclic peptides was evaluated. This study provides guidelines for using AlphaFold3 in cyclic peptide structure prediction for therapeutic applications.

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Area of Science:

Structural Biology
Computational Chemistry
Medicinal Chemistry

Background:

AlphaFold3 has revolutionized structural biology but its performance on noncanonical cyclic peptides is unknown.
Cyclic peptides with unnatural residues are clinically significant therapeutic agents.

Purpose of the Study:

To systematically evaluate AlphaFold3 for predicting noncanonical cyclic peptide structures.
To quantify prediction accuracy, assess confidence metrics, and identify biases.
To provide guidelines for AlphaFold3 application in cyclic peptide research.

Main Methods:

Developed an automated pipeline for AlphaFold3 predictions of cyclic peptides.
Quantified hierarchical accuracy (all atoms, Cα, unnatural residues) for monomers and complexes.
Assessed AlphaFold3 confidence metrics and the impact of MSAs and templates.

Main Results:

First systematic evaluation of AlphaFold3 for noncanonical cyclic peptides.
Detailed analysis of prediction accuracy across different structural levels.
Evaluation of AlphaFold3's confidence scores and potential biases.

Conclusions:

AlphaFold3 shows potential for predicting noncanonical cyclic peptide structures.
Understanding AlphaFold3's performance and limitations is crucial for its application.
Guidelines are provided to aid researchers in utilizing AlphaFold3 for bioactive cyclic peptide discovery.