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Related Concept Videos

Protein Organization01:24

Protein Organization

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.
Conserved Binding Sites01:49

Conserved Binding Sites

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 analyses the...
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Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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

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

Published on: July 25, 2013

Optimizing Bayes error for protein structure model selection by stability mutagenesis.

Xiaoduan Ye1, Alan M Friedman, Chris Bailey-Kellogg

  • 1Department of Computer Science, Dartmouth College, Purdue Cancer Center, Purdue University, USA.

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|August 1, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a method for planning site-directed mutations to improve protein structure model selection. The approach minimizes the probability of choosing an incorrect model by analyzing stability changes (DeltaDeltaGo values).

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Area of Science:

  • Protein structure and stability analysis
  • Computational biology and bioinformatics
  • Molecular genetics

Background:

  • Protein stability is influenced by the local structural context of mutations.
  • Site-directed mutagenesis combined with stability measurements can validate protein structure models.
  • Evaluating predicted protein structures requires accurate assessment of experimental stability changes (DeltaDeltaGo values).

Purpose of the Study:

  • To develop a method for planning site-directed mutations to optimize protein structure model selection.
  • To minimize the Bayes error, reducing the likelihood of selecting an incorrect protein model.
  • To provide a framework for selecting informative mutations for structural elucidation.

Main Methods:

  • Leveraging the structure of "DeltaDeltaGo space" to derive upper and lower bounds for Bayes error.
  • Developing a lower bound on Bayes error for mutation plans with a fixed number of mutations.
  • Employing a branch-and-bound algorithm for planning optimal and near-optimal mutation sets.

Main Results:

  • The developed method provides tight upper and lower bounds for Bayes error in protein structure model selection.
  • An effective branch-and-bound algorithm was implemented for planning mutation strategies.
  • The approach was successfully demonstrated for elucidating the structure of the pTfa chaperone protein.

Conclusions:

  • The proposed mutation planning method enhances the accuracy of protein structure model selection.
  • This strategy effectively minimizes the probability of selecting incorrect structural models.
  • The approach offers a powerful tool for experimental structure determination and validation.