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Second derivatives in generalized Born theory.

Russell A Brown1, David A Case

  • 1Sun Microsystems, Inc., Menlo Park, California 94025, USA.

Journal of Computational Chemistry
|August 11, 2006
PubMed
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This study presents efficient computation of second derivatives for Generalized Born solvation models, enabling advanced analyses like molecular optimization and mechanical property calculations for large biomolecules. The methods are parallelized for high performance computing.

Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Biophysics

Background:

  • Generalized Born (GB) models are widely used for electrostatic solvation free energies.
  • These models rely on atomic coordinates, charges, and dielectric radii.
  • Efficient computation of higher-order derivatives is crucial for advanced molecular simulations.

Purpose of the Study:

  • To develop and implement efficient methods for computing second derivatives of GB solvation models with respect to Cartesian coordinates.
  • To enable new analytical methods for implicit solvation models.
  • To demonstrate the utility of these methods in molecular optimization, mechanical property analysis, and entropy calculations.

Main Methods:

  • Efficient computation of second derivatives for Generalized Born solvation models.

Related Experiment Videos

  • Parallel implementation using OpenMP and MPI for distributed computing.
  • Application of Newton-Raphson optimization, normal mode analysis, and MM/GBSA configurational entropy calculations.

    • Main Results:

    • Demonstrated efficient parallel computation of second derivatives for GB models.
    • Successfully applied the methods to optimize protein structures in solution and analyze DNA mechanical properties.
    • Calculated configurational entropies within the MM/GBSA framework for large systems (up to 45,000 atoms).
    • Achieved good parallel scaling on up to 144 processing threads/tasks.

    Conclusions:

    • The developed methods significantly enhance the capabilities of implicit solvation models.
    • Efficient second derivative calculations open new avenues for molecular analysis in solution.
    • The parallel implementation facilitates the study of large biomolecular systems.