Practical conversion from torsion space to Cartesian space for in silico protein synthesis
Jerod Parsons1, J Bradley Holmes, J Maurice Rojas
1Texas Agricultural Experiment Station, Texas A&M University, College Station, Texas 77843, USA.
Journal of Computational Chemistry
|May 18, 2005
Summary
A new method called the Natural Extension Reference Frame (NeRF) efficiently converts molecular structural data into precise atomic coordinates. This computational approach is faster and more stable than existing techniques for modeling proteins and DNA.
Area of Science:
- Computational chemistry
- Structural biology
- Robotics
Background:
- Translating bond angles and lengths to Cartesian coordinates is crucial for molecular modeling.
- Existing methods are computationally intensive and can lack numerical stability.
Purpose of the Study:
- To compare five established methods for protein backbone extension.
- To introduce and evaluate the Natural Extension Reference Frame (NeRF) algorithm.
Main Methods:
- Serial chain extension from bond angles and lengths.
- Comparison of algorithms based on operations, speed, stability, and parallelization.
- Implementation and benchmarking of the NeRF method.
Main Results:
- The NeRF algorithm requires 47% fewer floating point operations compared to traditional methods.
- NeRF demonstrates superior intrinsic numerical stability.
- NeRF offers potential for parallel processor acceleration.
Conclusions:
- The NeRF method is a more efficient and stable algorithm for converting structural data to atomic coordinates.
- NeRF's unique factorization may represent a minimal operation set for chain extension.
- This advancement benefits protein, DNA, and polymer modeling, as well as robotics.
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