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Automated Optimization of Potential Parameters.

Di Pierro Michele¹, Elber Ron

¹Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin TX 78712.

Journal of Chemical Theory and Computation

|September 10, 2013

Summary

This study presents an algorithm for refining molecular simulation force field parameters using experimental data. Results show that accurately reproducing experimental melting curves for short peptides with standard force fields is challenging due to parameter sensitivity.

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Area of Science:

Computational chemistry
Molecular dynamics
Biophysics

Background:

Empirical energy functions are crucial for molecular simulations.
Refining these functions with experimental data is essential for accuracy.
Current methods face challenges in parameter optimization.

Purpose of the Study:

To develop and apply an algorithm for refining empirical energy function parameters.
To assess the sensitivity of simulation results to parameter changes.
To evaluate the feasibility of reproducing experimental condensed phase measurements.

Main Methods:

Algorithm based on sensitivity analysis and local minimization.
Comparison of simulation results with experimental measurements.
Application to alanine dipeptide and WAAAH peptide folding.

Main Results:

The helix fraction is highly sensitive to potential parameters.
The slope of the melting curve shows low sensitivity to parameters.
Simultaneous fitting of both experimental observables is difficult.

Conclusions:

Standard force field functional forms may not reproduce experimental melting curves for short peptides.
Parameter optimization requires careful consideration of experimental observable sensitivity.
Further development of energy functions may be needed for accurate peptide simulations.