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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization.

Sitao Zhang1, Zixuan Jiang2, Rundong Huang3

  • 1State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

PRO-LDM, a novel protein design framework, uses diffusion models in latent space for efficient and accurate protein generation. This approach enhances protein properties like fluorescence, solubility, and stability.

Keywords:
conditional generationfunctional optimizationlatent diffusion modelsprotein sequence design

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Area of Science:

  • Computational biology
  • Protein engineering
  • Artificial intelligence in drug discovery

Background:

  • Diffusion models are gaining traction in computer vision and show promise for protein design.
  • Existing methods may lack efficiency or design fidelity in generating novel protein structures and sequences.

Purpose of the Study:

  • Introduce PRO-LDM, a modular, multi-tasking framework for protein design.
  • Integrate diffusion models in latent space for computational efficiency and design fidelity.
  • Enable the generation of natural-like proteins with enhanced diversity and optimized functions.

Main Methods:

  • PRO-LDM integrates diffusion models within a latent space framework.
  • The model learns local and global biological representations.
  • Modular design allows integration with alternative pre-trained encoders.
  • Classifier-free guidance is adjusted for outlier design exploration.

Main Results:

  • PRO-LDM successfully generated a novel green-fluorescence-protein variant.
  • The designed variant exhibited enhanced fluorescence, solubility, and stability.
  • The framework demonstrated versatility in extracting sequence information for protein optimization.

Conclusions:

  • PRO-LDM offers a versatile tool for designing novel proteins with improved properties.
  • The framework combines design fidelity with computational efficiency.
  • It effectively leverages physicochemical and evolutionary sequence information for optimized protein performance.