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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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Protein Organization01:13

Protein Organization

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Overview
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Protein and Protein Structure02:15

Protein and Protein Structure

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Protein and Protein Structures02:15

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Protein-protein Interfaces02:04

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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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Conserved Binding Sites01:49

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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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An information gain-based approach for evaluating protein structure models.

Guillaume Postic1,2,3,4, Nathalie Janel2, Pierre Tufféry1,4

  • 1Université de Paris, BFA, UMR 8251, CNRS, ERL U1133, Inserm, F-75013 Paris, France.

Computational and Structural Biotechnology Journal
|August 25, 2020
PubMed
Summary
This summary is machine-generated.

A new scoring method for protein structures, based on information gain and statistics, outperforms traditional potential of mean force (PMF) methods. This approach offers a novel way to rank protein models by quality.

Keywords:
Knowledge-based scoring functionsModel quality assessmentProtein structure predictionStatistical potentials

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Knowledge-based scoring functions using the potential of mean force (PMF) approach have been used for protein structure analysis for 30 years.
  • These statistical potentials are often misinterpreted as approximations of free energy, a justification that remains controversial.
  • Alternative statistical interpretations exist, but the use of inverse Boltzmann law formalism persists in literature.

Purpose of the Study:

  • To introduce a novel method for ranking protein structure models based on quality.
  • To develop a scoring approach independent of physics-based explanations, grounded in statistics and information gain.
  • To provide new insights into the workings of statistical PMFs by comparing them with the proposed method.

Main Methods:

  • Developed a new scoring formalism based on statistics and information gain.
  • Constructed interatomic distance-dependent scoring functions using both traditional and new equations.
  • Compared the performance of scoring functions on a large benchmark dataset of 60,000 protein structures.

Main Results:

  • The new statistical formalism demonstrated superior performance in evaluating the quality of protein structural decoys compared to traditional statistical PMFs.
  • The developed scoring functions, based on the new equations, showed improved accuracy in ranking protein models.
  • The study validates the effectiveness of the information gain approach for protein structure assessment.

Conclusions:

  • The novel scoring method offers a statistically robust and information-theoretic alternative to existing knowledge-based potentials for protein structure evaluation.
  • This approach provides a more accurate assessment of protein structural decoys, potentially improving downstream applications in structural biology.
  • The open-source code is available, facilitating the adoption and further development of this new scoring paradigm.