A Method for Parameter Optimization in Computational Biology
| Autor: | K.A. Dill, S. Y. Oh, J. B. Rosen, Andrew T. Phillips |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Protein Folding
Quantitative Biology::Biomolecules 0303 health sciences Computer science Biophysics Energy metabolism Computational Biology Proteins Model protein Computational biology Models Theoretical Structural error Models Biological 03 medical and health sciences 0302 clinical medicine Proof of concept Parameter Energy Metabolism Algorithms 030217 neurology & neurosurgery Research Article 030304 developmental biology Stable state |
| Zdroj: | Biophysical Journal. 79:2818-2824 |
| ISSN: | 0006-3495 |
| DOI: | 10.1016/s0006-3495(00)76520-9 |
| Popis: | Models in computational biology, such as those used in binding, docking, and folding, are often empirical and have adjustable parameters. Because few of these models are yet fully predictive, the problem may be nonoptimal choices of parameters. We describe an algorithm called ENPOP (energy function parameter optimization) that improves—and sometimes optimizes—the parameters for any given model and for any given search strategy that identifies the stable state of that model. ENPOP iteratively adjusts the parameters simultaneously to move the model global minimum energy conformation for each of m different molecules as close as possible to the true native conformations, based on some appropriate measure of structural error. A proof of principle is given for two very different test problems. The first involves three different two-dimensional model protein molecules having 12 to 37 monomers and four parameters in common. The parameters converge to the values used to design the model native structures. The second problem involves nine bumpy landscapes, each having between 4 and 12 degrees of freedom. For the three adjustable parameters, the globally optimal values are known in advance. ENPOP converges quickly to the correct parameter set. |
| Databáze: | OpenAIRE |
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