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Two-step relaxation mode analysis with multiple evolution times applied to all-atom molecular dynamics protein

N Karasawa1, A Mitsutake1, H Takano1

  • 1Department of Physics, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa 223-8522, Japan.

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|January 20, 2018
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Summary

This study refines protein dynamics analysis using a two-step relaxation mode analysis (RMA) method. The enhanced technique improves the accuracy of estimating protein structural fluctuations and relaxation times.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Protein Dynamics

Background:

  • Protein function is intrinsically linked to its 3D structure and dynamic behavior.
  • Relaxation mode analysis (RMA) has been used to study protein dynamics and structural fluctuations.
  • Previous RMA methods provided approximate estimations of slow relaxation modes and times.

Purpose of the Study:

  • To enhance the accuracy of estimating protein relaxation modes and times.
  • To apply an improved two-step RMA method to complex protein systems.
  • To investigate the dynamic properties of hen egg-white lysozyme.

Main Methods:

  • Principal Component Analysis (PCA) applied to a 2-μs molecular dynamics simulation of hen egg-white lysozyme.
  • Two-step Relaxation Mode Analysis (RMA) with multiple evolution times applied to PCA results.
  • Iterative refinement of slow relaxation modes and their corresponding times.

Main Results:

  • The two-step RMA method significantly improved the accuracy of estimating relaxation modes and times for protein principal components.
  • The refined analysis provided a more precise characterization of the slow dynamic motions within the protein.
  • Application to hen egg-white lysozyme demonstrated the method's effectiveness on complex heteropolymer systems.

Conclusions:

  • The two-step RMA with multiple evolution times offers a more accurate approach for analyzing protein dynamics.
  • This enhanced method is suitable for complex heteropolymer systems, including proteins.
  • Improved understanding of protein dynamics can be achieved through this refined computational technique.