Abstract
An algorithm for finding an approximate global minimum of a funnel shaped function with many local minima is described. It is applied to compute the minimum energy docking position of a ligand with respect to a protein molecule. The method is based on the iterative use of a convex, general quadratic approximation that underestimates a set of local minima, where the error in the approximation is minimized in the L 1 norm. The quadratic approximation is used to generate a reduced domain, which is assumed to contain the global minimum of the funnel shaped function. Additional local minima are computed in this reduced domain, and an improved approximation is computed. This process is iterated until a convergence tolerance is satisfied. The algorithm has been applied to find the global minimum of the energy function generated by the Docking Mesh Evaluator program. Results for three different protein docking examples are presented. Each of these energy functions has thousands of local minima. Convergence of the algorithm to an approximate global minimum is shown for all three examples.
| Original language | English |
|---|---|
| Pages (from-to) | 285-297 |
| Number of pages | 13 |
| Journal | Computational Optimization and Applications |
| Volume | 32 |
| Issue number | 3 |
| DOIs | |
| State | Published - Dec 2005 |
| Externally published | Yes |
Funding
We thank Susan Lindsey for her contributions to DoME. The research of the authors was supported in part by DOE grant DE-FG03-01ER25497. J.B. Rosen was also supported in part by NSF/ITR grant #0082146.
Keywords
- Convex underestimator
- Docking mesh evaluator
- Global optimization
- Potential energy
- Protein docking