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Related Concept Videos

Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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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Protein Folding01:25

Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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Related Experiment Video

Updated: Sep 30, 2025

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

Published on: July 8, 2025

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Large-scale design and refinement of stable proteins using sequence-only models.

Jedediah M Singer1, Scott Novotney1, Devin Strickland2

  • 1Two Six Technologies, Arlington, Virginia, United States of America.

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|March 14, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed a neural network to predict and generate stable protein sequences, significantly improving protein design efficiency and stability through high-throughput screening.

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Area of Science:

  • Protein Engineering
  • Computational Biology
  • Biophysics

Background:

  • Engineered proteins require stable structures for function, but stable designs are rare among all possible amino acid sequences.
  • Finding stable protein designs is resource-intensive due to extensive computational and experimental testing.
  • A need exists for efficient methods to identify and generate stable protein sequences.

Purpose of the Study:

  • To experimentally evaluate the stability of a large set of novel proteins using a high-throughput assay.
  • To develop and validate a neural network model for predicting protein stability from amino acid sequences.
  • To create a generative model for designing novel stable protein sequences.

Main Methods:

  • Utilized a high-throughput, low-fidelity assay to assess the stability of approximately 200,000 novel proteins.
  • Constructed a neural network model to predict protein stability based on amino acid sequences.
  • Developed a second neural network for generating amino acid sequences of stable proteins.

Main Results:

  • Experimentally evaluated stability for a large dataset of novel protein sequences.
  • Developed a predictive neural network model correlating sequence with stability.
  • Created a generative neural network capable of designing novel stable proteins.
  • Demonstrated that the predictive model can enhance protein stability in both expert-designed and model-generated proteins.

Conclusions:

  • High-throughput screening and neural network modeling can accelerate the discovery of stable engineered proteins.
  • Predictive and generative models offer powerful tools for overcoming the rarity of stable protein designs.
  • The developed models show potential for substantially improving protein stability in future designs.