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Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Related Experiment Video

Updated: Oct 1, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline.

Gregory A Babbitt¹, Ernest P Fokoue², Harsh R Srivastava¹

¹Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA.

|March 7, 2022

Summary

This summary is machine-generated.

This study introduces a computational tool for analyzing protein dynamics and function, moving beyond static structural comparisons. It uses machine learning to identify conserved dynamic motions in protein variants for better drug and genetic analysis.

Keywords:

Bioinformatics Biophysics Computer sciences Evolutionary biology Protein Biochemistry Structural Biology Systems biology

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Last Updated: Oct 1, 2025

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Published on: July 14, 2015

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

Biophysics
Computational Biology
Structural Biology

Background:

Traditional protein analysis often overlooks crucial dynamic and functional aspects.
Understanding protein dynamics is key to comprehending biological function and disease mechanisms.

Purpose of the Study:

To develop and present a computational pipeline with a graphical user interface for analyzing protein dynamics.
To enable biophysical simulation and statistical comparison of two-state protein dynamics.
To identify and compare functionally conserved dynamic motions in protein variants using machine learning.

Main Methods:

Development of a computing pipeline integrated with molecular visualization tools.
Biophysical simulation of two-state protein dynamics (unbound vs. complex states).
Application of multi-agent machine learning classifiers to identify conserved dynamic motions.

Main Results:

The pipeline facilitates the comparison of protein dynamics beyond static sequence or structure alignments.
Identification of functionally conserved dynamic motions across different protein variants.
Enables statistical comparison of dynamic differences in genetic or drug-class variants.

Conclusions:

The developed tool enhances the analysis of protein function by incorporating dynamic information.
Machine learning classifiers effectively identify conserved dynamic motions relevant to protein function.
This approach provides a novel method for comparing functional dynamics in protein variants.