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Related Experiment Video

Updated: Oct 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

Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions.

S M Mortuza¹, Wei Zheng¹, Chengxin Zhang¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Nature Communications

|August 19, 2021

Summary

C-QUARK integrates deep learning and coevolution to predict protein structures. This new method significantly improves protein folding accuracy, especially for challenging non-homologous proteins with limited data.

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Area of Science:

Computational Biology
Structural Bioinformatics
Protein Structure Prediction

Background:

Sequence-based contact prediction aids protein structure modeling but requires extensive homologous sequences and accurate contacts.
Existing methods struggle with non-homologous proteins or sparse/low-accuracy contact information.

Purpose of the Study:

To develop and validate C-QUARK, a novel method for protein structure modeling.
To enhance protein folding accuracy using integrated contact prediction strategies.

Main Methods:

Developed C-QUARK, integrating multiple deep-learning and coevolution-based contact maps.
Utilized contact maps to guide replica-exchange Monte Carlo fragment assembly simulations.
Tested C-QUARK on diverse datasets, including non-redundant proteins and CASP free-modeling targets.

Main Results:

C-QUARK successfully folded 75% of 247 non-redundant proteins with TM-scores ≥0.5, a 2.6-fold improvement over QUARK.
For difficult cases (low accuracy/sparse data), C-QUARK outperformed other contact-based methods sixfold.
Achieved a 5% higher average GDT_TS score than top predictors on CASP13 free-modeling targets.

Conclusions:

C-QUARK represents a significant advancement in modeling non-homologous protein structures.
The method demonstrates robust performance even with low-accuracy and sparse contact-map predictions.
Highlights the potential of integrated deep-learning and coevolutionary approaches in structural biology.