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Related Concept Videos

Conservation of Protein Domains02:26

Conservation of Protein Domains

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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

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Published on: July 25, 2013

Dead-end elimination for multistate protein design.

Chen Yanover1, Menachem Fromer, Julia M Shifman

  • 1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. cheny@cs.huji.ac.il

Journal of Computational Chemistry
|May 2, 2007
PubMed
Summary

Multistate protein design aims to find amino acid sequences that fold into a single target structure. A new method, type-dependent dead-end elimination (DEE), efficiently reduces computational complexity for this challenging optimization problem.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Biophysics

Background:

  • Multistate protein design seeks amino acid sequences for specific, stable protein structures among competing possibilities.
  • Current computational approaches rely on genetic algorithms or Monte Carlo methods due to the problem's complexity.
  • The standard dead-end elimination (DEE) theorem is not directly applicable to multistate design.

Purpose of the Study:

  • To develop a novel computational method for multistate protein design.
  • To address the limitations of existing algorithms in handling competing protein conformations.
  • To improve the efficiency of multistate protein design by reducing the search space.

Main Methods:

  • Introduction of a variant of the dead-end elimination theorem, termed type-dependent DEE.
  • Application of type-dependent DEE as a preprocessing step to reduce the conformational space.
  • Provable preservation of the minimal energy conformational assignment for any amino acid sequence.

Main Results:

  • Type-dependent DEE effectively reduces the computational search space for multistate protein design problems.
  • The method is demonstrated to be applicable to various multistate design scenarios.
  • The algorithm preserves the integrity of the energy minimization process.

Conclusions:

  • Type-dependent DEE offers a computationally efficient approach for multistate protein design.
  • This method can be integrated into existing computational design schemes.
  • The study highlights the strengths and limitations of the proposed type-dependent DEE algorithm.