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Expected distributions of root-mean-square positional deviations in proteins.

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  • 1IBM Research - Almaden, 650 Harry Road, San Jose, California 95120, United States.

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This study provides an analytic form for protein structure comparison using root-mean-square deviation (RMSD). It reveals RMSD distribution characteristics for both native and random-coil states, aiding simulation analysis.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Structural Biology

Background:

  • Root-mean-square deviation (RMSD) is crucial for comparing molecular structures in simulations.
  • RMSD analysis informs biomolecular simulation quality, conformational clustering, and reaction coordinate identification.

Purpose of the Study:

  • To derive an approximate analytic form for the expected distribution of RMSD values for proteins and polymers.
  • To analyze RMSD distributions for both native-like fluctuations and random-coil ensembles.

Main Methods:

  • Developed an approximate analytic form for RMSD distributions of proteins near a stable native structure.
  • Generated numerical RMSD distributions for self-avoiding and non-self-avoiding random walks to model unfolded states.

Main Results:

  • For native states, mean and maximum RMSD are chain-length independent (for long chains) and proportional to root-mean-square fluctuations (RMSF).
  • For random-coil states, RMSD distributions show a power-law dependence on chain length, with maxima distant from the origin.
  • These findings highlight the entropic contribution to RMSD distributions in unfolded states.

Conclusions:

  • The derived analytic forms provide insights into RMSD behavior in biomolecular simulations.
  • Interpreting high-RMSD regions as stable intermediates requires caution due to entropic effects in unfolded chains.