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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
Efficient algorithms to explore conformation spaces of flexible protein loops
Peggy Yao1, Ankur Dhanik, Nathan Marz
1Computer Science Department, Stanford University, S240 Clark Center, 318 Campus Drive, Stanford, CA 94305, USA. peggyyao@stanford.edu
IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 8, 2008
Summary
This study presents two novel algorithms for efficiently sampling protein loop conformations, ensuring no steric clashes. These methods aid in understanding protein flexibility for applications like ligand docking and homology modeling.
Area of Science:
- Structural biology
- Computational biology
- Biophysics
Background:
- Protein flexibility is crucial for biological applications such as ligand docking and homology modeling.
- Accurately modeling protein loops requires sampling diverse conformations while avoiding steric clashes.
- Existing methods face challenges in simultaneously satisfying kinematic closure and clash avoidance constraints.
Purpose of the Study:
- To develop efficient algorithms for sampling closed, clash-free conformations of flexible protein loops.
- To provide tools for exploring the conformational space of protein loops for biological modeling.
- To enable the recognition of specific functional loop conformations, like calcium-binding loops.
Main Methods:
- Introduced two complementary sampling algorithms: "seed sampling" for broad exploration and "deformation sampling" for fine-grained analysis.
- Developed a toolkit (LoopTK) implementing these sampling algorithms.
- Integrated the sampling algorithms with functional site prediction software (FEATURE).
Main Results:
- Demonstrated the efficiency of the algorithms in sampling closed, clash-free loop conformations for loops of 5 to 25 residues.
- Showcased the ability to compute and recognize calcium-binding loop conformations through the combined approach.
- The toolkit (LoopTK) is available for broader scientific use.
Conclusions:
- The developed algorithms provide an efficient means to explore the conformational space of protein loops.
- The combination of sampling and prediction tools facilitates the identification of functionally relevant loop structures.
- These advancements support various applications in structural biology and drug discovery.
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