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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Published on: July 25, 2013

Protein inherent structures by different minimization strategies.

Francesco Rao1

  • 1Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Albertstr. 19, 79104 Freiburg, Germany. francesco.rao@frias.uni-freiburg.de

Journal of Computational Chemistry
|March 10, 2011
PubMed
Summary
This summary is machine-generated.

This study compares methods for analyzing molecular dynamics simulations of peptides. A quasi-Newtonian algorithm efficiently identifies inherent structures (ISs) comparable to traditional methods, aiding free-energy landscape analysis.

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular dynamics simulations

Background:

  • Network-based methods accurately describe peptide and protein free-energy landscapes from molecular dynamics (MD) simulations.
  • Meaningful grouping of MD snapshots into microstates is crucial for these analyses.
  • Inherent structures (ISs) offer a robust discretization of trajectories, overcoming limitations of previous clustering algorithms.

Purpose of the Study:

  • To investigate and compare different minimization protocols for obtaining inherent structures (ISs) of peptides.
  • To evaluate the efficiency and accuracy of a quasi-Newtonian algorithm against the conjugate gradient method for IS calculation.
  • To assess the impact of atom permutations on IS calculations and the need for improved potential energy functions.

Main Methods:

  • Utilized molecular dynamics simulations to generate peptide trajectories.
  • Employed various minimization protocols, including quasi-Newtonian and conjugate gradient methods, to determine inherent structures (ISs).
  • Analyzed free-energy profiles based on cut-offs to compare the effectiveness of different minimization strategies.

Main Results:

  • The quasi-Newtonian algorithm demonstrated quantitative agreement with the conjugate gradient method in characterizing peptide substates and energy barriers.
  • Despite occasional differences in quenching snapshots to distinct minima, the overall system properties remained unaffected by the choice of algorithm.
  • Confirmed that atom permutations influence IS calculations, necessitating enhancements in potential energy function implementations.

Conclusions:

  • A computationally efficient quasi-Newtonian algorithm can reliably determine inherent structures (ISs) for peptide free-energy landscape analysis.
  • The choice of minimization algorithm does not significantly alter the global properties of the system's free-energy landscape.
  • Improvements in potential energy functions are required to accurately handle atom permutations in IS calculations.