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Updated: May 12, 2026

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

FoldScript: a web server for the efficient analysis of AI-generated 3D protein models.

Xavier Robert¹, Christophe Guillon¹, Patrice Gouet¹

¹Retroviruses and Structural Biochemistry Team, Molecular Microbiology and Structural Biochemistry, MMSB, CNRS UMR 5086, Université de Lyon, 69007 Lyon, France.

Nucleic Acids Research

|April 25, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) for 3D protein modeling provides many models. FoldScript helps select the best AI-predicted protein structure by analyzing multiple models and experimental data for accuracy.

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Last Updated: May 12, 2026

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

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Area of Science:

Structural biology
Computational biology
Biophysics

Background:

Artificial intelligence (AI) has transformed 3D protein structure prediction.
Relying solely on the top-ranked AI model can be misleading, especially for complex structures.
Comparing multiple AI models and integrating experimental data is crucial for accurate structure selection.

Purpose of the Study:

To present FoldScript, a web server designed to aid in selecting the most accurate AI-generated protein models.
To provide tools for analyzing and comparing multiple AI-predicted protein structures.

Main Methods:

Developed the FoldScript web server to process up to 25 AI-generated protein models.
Implemented modules for synthesizing primary to quaternary structure information.
Integrated visualization tools including flat figures and 3D structure mosaics.
Added a module to identify models with known intermolecular contacts.

Main Results:

FoldScript enables comprehensive analysis and comparison of multiple AI protein models.
Visualizations facilitate efficient inspection of structural variations.
The server aids in identifying models consistent with experimental knowledge, particularly for oligomeric structures.

Conclusions:

FoldScript acts as a decision support tool for selecting optimal AI-driven protein models.
It empowers researchers, including non-specialists, to rationally choose accurate protein structures.
The web server enhances the reliability of AI-based structural biology workflows.