Advanced Electrostatic Computation in Molecular Dynamics

Period of Performance: 03/10/2010 - 02/28/2011


Phase 2 SBIR

Recipient Firm

Continuum Dynamics, Inc.
34 Lexington Avenue Array
Ewing, NJ 08618
Principal Investigator


DESCRIPTION (provided by applicant): Advances in computational hardware and molecular modeling techniques have revolutionized our ability to simulate electrostatic interactions, which play a fundamental role in the conformational stability, structure, folding and function of biomolecules. One important near-term application of these developments drawing both theoretical and commercial interests is drug design where successful docking requires both shape and electrostatic complementarity. Currently, the continuum electrostatic description based upon the Poisson- Boltzmann Equation (PBE) offers the best combination of modeling fidelity and computational cost, however, numerical issues associated with nonlinear behavior in highly charged systems and solution convergence at the dielectric interface, have impaired accuracy and calculation time. As a result, adoption of PBE-based solvers in energy minimization, Monte Carlo and molecular dynamics codes has been limited. In Phase I, the numerical stability of the electrostatic solution, particularly the gradient contributions from the surface, were successfully addressed using a boundary-conforming mesh so that reliably convergent and accurate force predictions are achieved. The challenge of reliable convergence for highly charged systems was also resolved. These advances were implemented on an adaptive Cartesian mesh structure that offers unique intrinsic advantages over competing grid arrangements (i.e. lattices and unstructured tetrahedral grids) with regard to multigrid implementation, mesh generation and solution adaptation. The Phase II effort builds upon these successes by providing additional capabilities focused on drug design and packaged to facilitate transition and distribution of the software tools to end-users in the medical and pharmaceutical industries. The main technical developments envisioned to support this goal are: (i) Methods will be formulated and implemented to calculate the electrostatic interaction or binding potential and energy with greater accuracy and/or speed, thus promoting higher reliability and throughput in drug screening and design efforts. (ii) Short- range forces will be incorporated to model molecular flexibility thus providing a more complete description of the molecular dynamics for drug design application and understanding of biologicial function. (iii) New methods for evaluating metrics to assess docking probability and reject decoys will be developed along with an efficient formulation for estimating the gradients/sensitivities of these metrics. In the context of drug design these gradients would indicate changes to the drug geometry and charge distribution favorable for selective binding. Our biomolecular applications will build upon strengths of our current state-of-the-art PBE solver in providing accurate and fast predictions of electrostatic solvation free energies, binding free energies, surface electrostatic potential and derived quantitative metrics for highly charged and large-scale systems such as nucleic acids and its assemblies such as nucleosome and ribosome. PUBLIC HEALTH RELEVANCE: The research effort will develop and provide software tools and analysis and visualization techniques that: (i) are tailored towards improved protein and drug design and (ii) can be used to relate biological function of solvated biomolecules to its geometric, structural and electrostatic properties. For drug applications, the analysis will provide designers with the information needed to enhance drug affinity and specificity, ensuring it binds to target sites and rejects decoy locations, thus, ultimately, lowering drug development costs and times, and improving drug efficacy with reduced side-effects. Improved insights into the relationship between the physiochemical and geometric properties of biomolecules and their environment to biological function are useful for enhancing bioinformatics tools and achieving a better foundational understanding of the progression of diseases at the molecular level and the means to counter them.