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Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force. 
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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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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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How much can physics do for protein design?

Eleni Michael1, Thomas Simonson1

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|August 30, 2021
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Summary
This summary is machine-generated.

Computational protein design leverages physics and physical chemistry for molecular mechanics, enabling protein redesign and optimization of binding energy and catalytic efficiency. These physics-based methods offer robust sampling and insights into conformational flexibility.

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Area of Science:

  • Computational protein design
  • Biophysics
  • Physical chemistry

Background:

  • Physics and physical chemistry complement knowledge-based tools in computational protein design.
  • They offer molecular mechanics scoring functions and methods for sampling equilibrium ensembles.
  • Approximations for conformational flexibility are key aspects.

Purpose of the Study:

  • To highlight the role of physics and physical chemistry in advancing computational protein design.
  • To showcase the application of physics-based energy calculations in protein redesign.
  • To explore advanced simulation techniques for optimizing protein function.

Main Methods:

  • Utilizing molecular mechanics scoring functions with minimal parameter adjustment.
  • Employing adaptive Monte Carlo and molecular dynamics for ensemble sampling.
  • Applying implicit solvent models and polarizable force fields.
  • Incorporating molecular dynamics for backbone flexibility analysis.

Main Results:

  • Successful redesign of a small protein using physics-based folded state energy.
  • Development of schemes to populate protein variants based on ligand-binding free energy or catalytic efficiency.
  • Gained significant physical insights into protein behavior and properties.

Conclusions:

  • Physics-based approaches are crucial for sophisticated computational protein design.
  • These methods enable precise optimization of protein properties like binding and catalysis.
  • Continued refinement of physical models and simulation techniques will drive future advancements.