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Related Concept Videos

Protein-protein Interfaces02:04

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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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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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
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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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Protein Complexes with Interchangeable Parts01:57

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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Accurate refinement of docked protein complexes using evolutionary information and deep learning.

Bahar Akbal-Delibas1, Roshanak Farhoodi1, Marc Pomplun1

  • 11 Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA 02125, USA.

Journal of Bioinformatics and Computational Biology
|February 6, 2016
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Summary

A new deep learning model accurately predicts protein complex structures, improving upon existing protein docking refinement tools. This advancement helps distinguish correct protein interactions from false positives with a low error margin.

Keywords:
Protein dockingdeep learning neural networksranking and scoring functions

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Protein docking methods struggle to accurately identify native-like protein complexes from false positives.
  • Refinement and re-ranking are crucial steps to improve the accuracy of protein-protein docking results.

Purpose of the Study:

  • To develop and evaluate a deep learning network for accurate prediction of root-mean-square deviation (RMSD) in protein docking.
  • To enhance the AccuRefiner tool's capability in selecting refined protein complex structures with lower RMSD.

Main Methods:

  • A deep learning network with five layers was trained on 35,000 unbound docking complexes generated by RosettaDock.
  • The network utilizes a comprehensive set of features to approximate the relationship between scoring function terms and RMSD.
  • The refined method was tested on 25 distinct docked complexes for five proteins not included in the training dataset.

Main Results:

  • The deep learning network achieved an average error margin of 1.40 Å in predicting the RMSD of docked protein complexes.
  • The trained network demonstrated high accuracy in ranking docked complexes based on their predicted RMSD.
  • AccuRefiner, utilizing the new ranking tool, consistently selected refinement candidates with lower RMSD values than the initial docked structures.

Conclusions:

  • Deep learning significantly improves the accuracy of predicting RMSD for protein docking, outperforming previous methods.
  • The enhanced AccuRefiner tool effectively refines protein-protein complex structures by leveraging accurate RMSD predictions.
  • This approach offers a robust solution for discriminating native-like protein complexes and reducing false positives in computational structural biology.