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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
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Pairwise decomposition of an MMGBSA energy function for computational protein design
Thomas Gaillard1, Thomas Simonson
1Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
Journal of Computational Chemistry
|May 24, 2014
Summary
Computational protein design (CPD) simplifies energy calculations by decomposing protein MMGBSA energy functions. This method improves efficiency for predicting new proteins while maintaining reasonable error margins.
Area of Science:
- Computational biology
- Biophysics
- Protein engineering
Background:
- Computational protein design (CPD) faces challenges due to vast sequence and conformation spaces.
- Strategies like fixed backbones and precalculated energy matrices reduce complexity.
- Pairwise decomposition of energy functions is a key technique in CPD.
Purpose of the Study:
- To theoretically examine the pairwise decomposition of protein MMGBSA energy functions.
- To evaluate an existing implementation of this decomposition for CPD.
- To present an improved pairwise decomposition for the Surface Area term.
Main Methods:
- Theoretical analysis of protein MMGBSA energy functions.
- Investigating Generalized Born and Surface Area terms.
- Detailed error evaluation of the decomposition on energy components.
Main Results:
- The many-body character of the Generalized Born term is addressed via an effective dielectric environment.
- An improved pairwise decomposition is presented for the Surface Area term.
- The error introduced by the decomposition is quantified and found to be reasonable.
Conclusions:
- Pairwise decomposition of MMGBSA energy functions is a viable strategy for CPD.
- The proposed improvements maintain reasonable error levels, aiding protein design.
- This work contributes to more efficient and accurate computational protein design.
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