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Identifying and correcting non-Markov states in peptide conformational dynamics
Dmitry Nerukh1, Christian H Jensen, Robert C Glen
1Department of Chemistry, Unilever Centre for Molecular Sciences Informatics, Cambridge University, CB2 1EW Cambridge, United Kingdom. dn232@cam.ac.uk
Protein dynamics are Markovian at longer timescales but lose this property at shorter timescales. This study reveals why, offering a method to redefine states for accurate Markovian analysis in protein conformational transitions.
Area of Science:
- Computational biology
- Biophysics
- Molecular dynamics
Background:
- Protein conformational transitions are crucial for biological function.
- Markov state models (MSMs) are widely used to study these transitions.
- The Markov property is a fundamental assumption in MSMs.
Purpose of the Study:
- To test the Markovian assumption in protein dynamics.
- To identify the timescales at which the Markov property breaks down.
- To develop a method for redefining states to satisfy the Markov property.
Main Methods:
- Molecular dynamics simulations of a valine-proline-alanine-leucine peptide.
- Analysis of conformational transitions between states.
- Clustering in dihedral space to define states phenomenologically.
- Testing the Markov property of state transitions.
Main Results:
- Protein dynamics exhibit Markovian behavior at timescales of approximately 50 ps and longer.
- The Markov property is lost at timescales between 30-40 ps.
- A methodology was developed to identify the mechanism of non-Markovian behavior.
Conclusions:
- The Markov assumption in protein dynamics is timescale-dependent.
- The developed methodology can reveal mechanisms of non-Markovian dynamics.
- Conformations can be regrouped into new states that exhibit Markovian dynamics.
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