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Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field
Elmar Krieger1, Günther Koraimann, Gert Vriend
1Center for Molecular and Biomolecular Informatics, Nijmegen, The Netherlands. elmar.krieger@cmbi.kun.nl
Proteins
|April 12, 2002
Summary
A new all-atom force field, NOVA, optimizes protein and nucleotide structures in vacuo. It improves homology models and structure refinement without worsening experimental data.
Area of Science:
- Computational Biology
- Structural Biology
- Biophysics
Background:
- Template-based modeling and structure refinement are crucial in computational biology.
- Existing force fields can inaccurately alter experimentally determined coordinates during refinement.
Purpose of the Study:
- To develop an all-atom force field (NOVA) for in vacuo protein and nucleotide optimization.
- To address limitations of current force fields in refining template-based models and experimental structures.
Main Methods:
- Developed NOVA with automatic parameterization focused on preserving X-ray structures and improving homology models.
- Optimized force-field parameters using Monte Carlo moves and simulated annealing on a 50-protein set.
- Introduced a novel method for assigning force-field parameters based on molecular trees.
Main Results:
- NOVA demonstrated effectiveness on both optimization and independent validation sets.
- The force field successfully moves in silico models closer to experimentally determined reality.
- NOVA showed comparable performance to existing methods in structure refinement.
Conclusions:
- NOVA is a robust force field for in vacuo molecular optimization.
- It offers improvements for homology modeling and refinement of X-ray and NMR structures.
- The NOVA server is publicly accessible for broader application.