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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.
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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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Protein-protein Interfaces

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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 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 Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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TopModel: Template-Based Protein Structure Prediction at Low Sequence Identity Using Top-Down Consensus and Deep

Daniel Mulnaes1, Nicola Porta1, Rebecca Clemens2

  • 1Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany.

Journal of Chemical Theory and Computation
|January 23, 2020
PubMed
Summary

TopModel, a new protein structure prediction tool, uses advanced consensus and deep learning to improve accuracy. It outperforms existing methods and aids in integrating experimental data for better protein models.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Experimental protein structure determination lags behind sequence data generation.
  • Accurate protein structures are crucial for understanding function, evolution, and drug design.
  • Existing meta-methods can be limited by ensemble averaging and majority voting.

Purpose of the Study:

  • To develop an automated meta-method for enhanced protein structure prediction.
  • To improve template selection, alignment accuracy, and model quality.
  • To demonstrate the utility of combining computational prediction with experimental data.

Main Methods:

  • Developed TopModel, a fully automated meta-method for protein structure prediction.
  • Utilized top-down consensus and deep neural networks for template selection and error correction.
  • Integrated state-of-the-art methods for threading, alignment, and quality estimation.

Main Results:

  • TopModel demonstrated superior template selection, alignment accuracy, and model quality on CASP10-12 datasets.
  • Outperformed 12 state-of-the-art primary predictors in template-based structure prediction.
  • Prospective predictions for NSR and LipoP proteins showed improved agreement with experimental data.

Conclusions:

  • TopModel offers a versatile workflow for template-based protein structure prediction.
  • The method effectively identifies and corrects wrongly modeled regions.
  • Combining TopModel with sparse experimental data enhances final protein model quality.