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Related Experiment Video

Updated: Jul 4, 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

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2.

Ben Shor¹, Dina Schneidman-Duhovny²

¹The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.

|February 7, 2024

Summary

This summary is machine-generated.

CombFold accurately predicts large protein complex structures using AlphaFold2 subunit interactions. This new method improves structural coverage and supports experimental data integration for complex assembly prediction.

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Area of Science:

Structural biology
Computational biology
Biophysics

Background:

Deep learning models like AlphaFold2 and RosettaFold excel at monomeric protein structure prediction.
Predicting the structure of large protein complexes remains a significant challenge due to their scale and subunit interaction complexity.

Purpose of the Study:

To develop a novel algorithm, CombFold, for accurate prediction of large protein complex structures.
To enhance the structural coverage of protein complexes beyond individual subunit predictions.

Main Methods:

CombFold employs a combinatorial and hierarchical assembly strategy.
It leverages pairwise subunit interactions predicted by AlphaFold2.
The algorithm integrates distance restraints from crosslinking mass spectrometry and enumerates stoichiometries.

Main Results:

CombFold achieved high accuracy (TM-score >0.7) for 72% of large, asymmetric assemblies in top-10 predictions.
Predicted complex structures showed 20% greater structural coverage than Protein Data Bank entries.
High-confidence predictions were obtained for complexes with known stoichiometry but unknown structures.

Conclusions:

CombFold is a powerful tool for predicting the structure of large protein complexes.
The method significantly expands structural coverage beyond monomeric proteins.
CombFold facilitates the integration of experimental data for more accurate complex modeling.