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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

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Related Experiment Video

Updated: Jun 21, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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

Published on: July 25, 2013

An efficient algorithm for multistate protein design based on FASTER.

Benjamin D Allen1, Stephen L Mayo

  • 1Division of Chemistry and Chemical Engineering, California Institute of Technology, MC 114-96, 1200 E. California Blvd., Pasadena, California 91125, USA.

Journal of Computational Chemistry
|July 29, 2009
PubMed
Summary
This summary is machine-generated.

Computational protein design can now optimize sequences for multiple states simultaneously using the new MSD-FASTER algorithm. This efficient method consistently identifies low-energy sequences, accelerating protein design and development.

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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
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational biology
  • Protein engineering
  • Biochemistry

Background:

  • Traditional computational protein design focuses on single, fixed main-chain structures.
  • Multistate design (MSD) enables sequence selection based on multiple structural or chemical states.
  • MSD is crucial for proteins functioning across different conformations or for specific binding/catalytic properties.

Purpose of the Study:

  • To implement and evaluate an efficient multistate design optimization algorithm, MSD-FASTER.
  • To demonstrate the applicability and performance of MSD-FASTER across various design challenges.
  • To compare MSD-FASTER with existing multistate design Monte Carlo methods.

Main Methods:

  • Development of an efficient multistate design optimization algorithm (MSD-FASTER).
  • Rigorous computational testing on diverse multistate design problems.
  • Direct comparison of MSD-FASTER with multistate design Monte Carlo algorithms.

Main Results:

  • MSD-FASTER is broadly applicable to multistate design problems, including those with many states and designed positions.
  • MSD-FASTER consistently identifies lower-energy sequences compared to multistate design Monte Carlo.
  • The algorithm's efficiency stems from energetic-basis amino acid substitutions and simultaneous multiple substitutions.

Conclusions:

  • MSD-FASTER offers a significant advancement in computational protein design efficiency and consistency.
  • The algorithm facilitates the testing of improved sequences, accelerating the development of better scoring functions and models.
  • This enhanced capability is vital for designing proteins with specific multi-state functionalities and specificities.