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Updated: Aug 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

Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE.

Robert D Oeffner¹, Tristan I Croll¹, Claudia Millán¹

¹Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge Biomedical Campus, The Keith Peters Building, Hills Road, Cambridge CB2 0XY, United Kingdom.

Acta Crystallographica. Section D, Structural Biology

|November 2, 2022

Summary

AlphaFold provides accurate protein models but requires refinement. New tools use AlphaFold

Keywords:

AlphaFold ISOLDE Phenix confidence measures process_predicted_model

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Area of Science:

Structural biology
Computational biology
Biophysics

Background:

AlphaFold is a powerful AI tool for predicting protein structures.
Predicted models often achieve high accuracy but can contain local errors.
Confidence metrics from AlphaFold are crucial for assessing model reliability.

Purpose of the Study:

To describe methods for refining AlphaFold models using confidence metrics.
To integrate AlphaFold predictions into experimental structure determination workflows.
To reduce manual rebuilding efforts in structural biology.

Main Methods:

Utilizing AlphaFold's predicted local distance difference test (pLDDT) scores.
Employing the predicted aligned error (PAE) matrix for domain orientation confidence.
Applying the phenix.process_predicted_model tool to filter low-confidence regions.
Using ISOLDE for interactive refinement guided by confidence metrics and experimental data.

Main Results:

Downweighting or removing low-confidence residues improves model quality.
Breaking models into confidently predicted domains facilitates molecular replacement and cryo-EM docking.
Interactive refinement in ISOLDE effectively adjusts AlphaFold models to fit experimental data.
Reduced need for extensive manual rebuilding of protein structures.

Conclusions:

Confidence metrics from AlphaFold are essential for reliable structure determination.
Integrated computational and experimental approaches enhance structural biology workflows.
These methods streamline the process of obtaining accurate protein structures from predictions.