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Conservation of Protein Domains Over Different Proteins02:26

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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The organelle-specific signaling sequences direct proteins synthesized in the cytosol to their final destination like ER, mitochondria, peroxisomes, etc. Some of the proteins directed to ER are then trafficked via vesicles to other organelles within the cell or the extracellular environment through the Golgi complex. For example, the rough ER synthesizes soluble proteins for transportation to the lysosomes or secretion out of the cell. It can also synthesize transmembrane proteins that can...
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Updated: Sep 20, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Deep learning-guided design of dynamic proteins.

Amy B Guo1,2, Deniz Akpinaroglu1,2, Christina A Stephens3,4

  • 1The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, USA.

Science (New York, N.Y.)
|May 22, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a deep learning method to design dynamic protein structures, enabling precise control over protein movements for the first time. This breakthrough allows for the creation of novel, controllable protein signaling behaviors.

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Area of Science:

  • Protein Engineering
  • Computational Biology
  • Biophysics

Background:

  • Deep learning has enabled the design of static protein structures.
  • Designing dynamic conformational changes in proteins, crucial for signaling, remains a significant challenge.

Purpose of the Study:

  • To develop a general deep learning-guided approach for the de novo design of dynamic protein conformational changes.
  • To enable precise, atomic-level control over protein movements, mimicking natural signaling mechanisms.

Main Methods:

  • Utilized a deep learning framework to design novel protein structures with specific dynamic motions.
  • Experimentally validated designed protein conformations using structural biology techniques.
  • Investigated the modulation of designed conformational landscapes by ligands and mutations.
  • Employed physics-based simulations to compare with deep learning predictions and experimental data.

Main Results:

  • Successfully designed and validated four protein structures exhibiting controlled dynamic changes.
  • Demonstrated that orthosteric ligands and allosteric mutations can modulate the designed conformational landscape.
  • Physics-based simulations corroborated the deep learning predictions and experimental findings.

Conclusions:

  • The developed deep learning approach enables the de novo design of new protein motions.
  • Provides a framework for creating synthetic proteins with tunable and controllable signaling behavior.
  • Opens new avenues for engineering biology-inspired dynamic protein functions.