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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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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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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.
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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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New Frontiers for Machine Learning in Protein Science.

Alexey S Morgunov1, Kadi L Saar2, Michele Vendruscolo3

  • 1Yusuf Hamied Department of Chemistry, University of Cambridge, CB2 1EW, United Kingdom; Fluidic Analytics Ltd, Cambridge, United Kingdom. Electronic address: https://twitter.com/AlexeyMorgunov.

Journal of Molecular Biology
|September 9, 2021
PubMed
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Protein interactions drive cell functions and form complex structures. Machine learning advances offer new ways to study these dynamic protein interactions and their role in health and disease.

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data scienceliquid-liquid phase separationmachine learningprotein aggregationprotein foldingprotein-protein interactions

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Area of Science:

  • Molecular biosciences
  • Biochemistry
  • Structural biology

Background:

  • Protein function relies on inter-molecular interactions forming complexes essential for cellular processes.
  • These interactions involve proteins with diverse conformational states and intrinsic disorder, forming structures from binary complexes to large biomolecular condensates.
  • Understanding these interactions is key to comprehending biological functions and diseases.

Purpose of the Study:

  • To discuss the challenges and opportunities presented by machine learning (ML) advancements in studying protein interactions.
  • To explore how data-driven approaches can advance biomolecular science.
  • To highlight the significance of understanding protein complex formation and its implications in disease.

Main Methods:

  • Review of recent advances in machine learning applied to the protein folding problem.
  • Discussion of data-driven approaches for analyzing protein interactions and biomolecular structures.
  • Conceptual framework for exploring protein dynamics and complex formation.

Main Results:

  • Machine learning has significantly advanced the understanding of the protein folding problem.
  • New data-driven methods offer powerful tools for investigating complex protein interactions.
  • These advances open new avenues for studying biomolecular mechanisms and disease.

Conclusions:

  • Recent ML breakthroughs provide unprecedented opportunities to study dynamic protein interactions.
  • Data-driven approaches are crucial for tackling the next frontiers in biomolecular science.
  • Further research is needed to fully leverage ML for understanding protein function, disease, and developing therapeutics.