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Locating Transition States for Biomolecular Dynamics via Invertible Dimensionality Reduction.

Jianyu Yang1, Huanlei Guo2, Song Liu2

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This study integrates reaction coordinate flows (RCF) with gentlest ascent dynamics (GAD) to efficiently locate transition states in biomolecular simulations. The combined method successfully identifies key conformational changes in biomacromolecules, overcoming dimensionality challenges.

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular dynamics

Background:

  • Locating transition states (TS) is crucial for understanding biomacromolecular conformational changes and mechanisms.
  • High dimensionality of biomolecules poses a significant challenge for identifying short-lived TSs from simulation data.
  • Existing dimensionality reduction (DR) algorithms often distort TS regions, hindering subsequent analysis.

Purpose of the Study:

  • To develop and validate an integrated approach combining reaction coordinate flows (RCF) and gentlest ascent dynamics (GAD) for efficient TS identification.
  • To overcome the limitations of traditional DR methods in preserving TS information during dimensionality reduction.
  • To demonstrate the applicability of the RCF-GAD integration for complex biomolecular systems.

Main Methods:

  • Utilized reaction coordinate flows (RCF) for invertible dimensionality reduction, preserving kinetic information and TS regions.
  • Applied gentlest ascent dynamics (GAD) in the reduced RCF space to efficiently search for saddle points (TSs).
  • Validated identified TS candidates using reverse RCF mapping and committor analysis in the original high-dimensional space.

Main Results:

  • The RCF-GAD integration successfully identified transition states for alanine dipeptide and the T4 lysozyme L99A variant.
  • For alanine dipeptide, the number of identified TSs depended on the number of RCs, highlighting the importance of intrinsic dimensionality assessment.
  • For T4 lysozyme L99A, GAD in the RCF-reduced space located TSs consistent with previous findings, demonstrating feasibility for realistic systems.

Conclusions:

  • The RCF-GAD integration offers a robust method for locating transition states in high-dimensional biomolecular systems.
  • This approach effectively preserves crucial kinetic information and TS characteristics during dimensionality reduction.
  • The study validates the RCF-GAD method for analyzing conformational dynamics in complex biomolecules.