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Related Concept Videos

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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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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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 WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Guaranteed Discrete Energy Optimization on Large Protein Design Problems.

David Simoncini1, David Allouche1, Simon de Givry1

  • 1INRA MIAT, UR 875 , Castanet-Tolosan, 31326 Cedex, France.

Journal of Chemical Theory and Computation
|November 27, 2015
PubMed
Summary
This summary is machine-generated.

Computational Protein Design (CPD) now has an exact method to find optimal protein sequences and conformations, solving NP-hard problems. This new approach outperforms existing methods like simulated annealing, which often miss the true optimum.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Bioinformatics

Background:

  • Finding optimal protein sequences and conformations in Computational Protein Design (CPD) is an NP-hard problem.
  • Existing methods like simulated annealing may fail to find the global optimum.

Purpose of the Study:

  • To develop and apply an exact deterministic method for solving full-redesign problems in CPD.
  • To rigorously evaluate the performance of existing CPD algorithms against proven optimal solutions.

Main Methods:

  • Utilized branch and bound, arc consistency, and tree-decomposition for an exact deterministic approach.
  • Employed Dunbrack's rotamer library and Talaris2014 energy function.
  • Developed a variant to enumerate all near-optimal sequence-conformations.

Main Results:

  • Successfully identified global minimum energy sequence-conformations for problems with search spaces up to 10^234.
  • Demonstrated that simulated annealing implementations frequently miss the optimum, with designs differing by over 30% in amino acids.
  • Showcased the method's efficiency, requiring only a standard server and 66GB RAM.

Conclusions:

  • The developed exact method provides a benchmark for evaluating CPD algorithms.
  • Highlights the limitations of current heuristic approaches in achieving globally optimal protein designs.
  • Paves the way for more accurate and reliable protein design strategies.