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Mechanical Protein Functions

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Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.

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Related Experiment Video

Updated: Feb 13, 2026

Rapid Assessment of Membrane Protein Quality by Fluorescent Size Exclusion Chromatography

Rapid Assessment of Membrane Protein Quality by Fluorescent Size Exclusion Chromatography

Published on: January 6, 2023

Improved protein model quality assessments by changing the target function.

Karolis Uziela¹, David Menéndez Hurtado¹, Nanjiang Shu^1,2

¹Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.

|March 11, 2018

Summary

This summary is machine-generated.

We evaluated different target functions for protein model quality prediction. Contact-based methods improve identifying the best models, while S-score best correlates with global quality (GDT_TS).

Keywords:

CASP deep learning estimation of model accuracy model quality assessments protein structure prediction

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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

Area of Science:

Computational biology
Structural bioinformatics
Machine learning in protein science

Background:

Protein structure prediction accuracy is crucial for biological research.
Protein model quality estimation methods are essential for assessing predicted structures.
ProQ has historically used the S-score, a superposition-based target function, for training.

Purpose of the Study:

To investigate the impact of different target functions on protein model quality prediction.
To compare the performance of contact-based versus superposition-based quality estimation methods.
To develop an improved protein quality predictor by integrating local and global quality estimates.

Main Methods:

Retraining the ProQ3D predictor with identical inputs but varying target functions.
Evaluating contact-based and superposition-based (S-score) quality estimation metrics.
Assessing predictor performance using correlation with the GDT_TS score and model identification capabilities.

Main Results:

Contact-based quality estimation methods are easier to predict and aid in identifying the best protein models.
Predictors trained on contact-based methods show advantages for multi-domain protein targets.
Training on the S-score yields the highest correlation with the global GDT_TS score.

Conclusions:

Different target functions offer distinct advantages for protein model quality assessment.
An updated ProQ3D version integrates local and global quality predictions for comprehensive assessment.
Combining insights from various quality estimation methods enhances protein structure prediction evaluation.