Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of...

Area of Science:

• Computational Chemistry
• Biophysics
• Molecular Dynamics

Background:

• The Outlier FLOODing (OFLOOD) method uses outliers detected by FlexDice clustering to identify potential conformational transitions.
• Outliers are employed as starting points for molecular dynamics simulations to resample conformations.

Purpose of the Study:

• To develop a sparsity-weighted OFLOOD method for more efficient conformational searching.
• To investigate the impact of outlier rank on conformational exploration.

Main Methods:

• Implemented a sparsity-weighted OFLOOD approach using a hierarchical clustering algorithm (FlexDice).
• Categorized outliers into three ranks based on their sparsity (distance from clusters).
• Applied the method to Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein.

Main Results:

• The sparsity-weighted OFLOOD method successfully reproduced transitions among metastable states in all tested systems.
• First-ranked OFLOOD (highly sparse outliers) significantly accelerated conformational space exploration.
• Third-ranked OFLOOD (moderately sparse outliers) effectively sampled local transitions between neighboring states.
• Free energy calculations confirmed the rigorous conformational landscapes.

Conclusions:

• Sparsity-weighted OFLOOD offers an efficient strategy for conformational searching in biomolecules.
• The choice of outlier rank allows for targeted exploration of global or local conformational space.
• This method provides a robust approach for understanding biomolecular dynamics and free energy landscapes.