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Structural Protein Function

Structural Protein Function

Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity. In bones and teeth, it mineralizes to...

Structural Protein Function

Structural Protein Function

Mechanical Protein Functions

Mechanical Protein Functions

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.

Mutations

Mutations

Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key...

Mechanical Protein Function

Mechanical Protein Function

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Related Experiment Video

Updated: Feb 13, 2026

Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer

Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer

Published on: May 26, 2014

Deconvolving mutation effects on protein stability and function with disentangled protein language models.

Kerr Ding¹, Ziang Li¹, Tony Tu¹

¹School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Biorxiv : the Preprint Server for Biology

|February 12, 2026

Summary

DETANGO, a new deep learning framework, separates protein function from stability effects. It identifies stable but inactive variants and critical functional residues, advancing protein engineering.

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Last Updated: Feb 13, 2026

Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer

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Published on: May 26, 2014

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Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli

Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli

Published on: January 6, 2015

Area of Science:

Molecular Biology
Computational Biology
Biophysics

Background:

Evolutionary constraints shape protein sequences, influencing stability and function.
Protein language models (pLMs) predict mutation effects but conflate stability and function.
Disentangling these effects is crucial for understanding mutation mechanisms and protein engineering.

Purpose of the Study:

Introduce DETANGO, a deep learning framework to deconvolute mutation effects on protein function.
Estimate functional plausibility scores by separating stability effects from pLM predictions.
Identify stable-but-inactive (SBI) variants and functionally critical residues.

Main Methods:

Developed DETANGO, a deep learning framework utilizing pLM predictions and stability data.
Estimated functional plausibility scores for single-point mutations.
Benchmarked DETANGO on various functional contexts (ligand binding, catalysis, allostery) and protein families.

Main Results:

DETANGO accurately identifies SBI variants and functionally critical residues.
The framework reveals shared and distinct functional patterns across homologous protein families.
DETANGO effectively disentangles evolutionary pressures on protein stability and function.

Conclusions:

DETANGO provides a biologically grounded framework for analyzing evolutionary constraints.
Advances mechanistic understanding of protein function and mutation effects.
Informs rational protein engineering and therapeutics development.