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Exploring the conformational space of cyclic peptides by a stochastic search method
Anwar Rayan1, Hanoch Senderowitz, Amiram Goldblum
1Department of Medicinal Chemistry and Natural Products, David R. Bloom Center for Pharmacy, School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem 91120, Israel. anvarr@md.huji.ac.il
Journal of Molecular Graphics & Modelling
|April 22, 2004
Summary
A novel stochastic search algorithm efficiently predicts cyclic peptide conformations. This method accurately determines global minimum structures, crucial for understanding peptide behavior and drug design.
Area of Science:
- Computational chemistry
- Molecular modeling
- Biophysics
Background:
- Cyclic peptides play vital roles in biological processes.
- Predicting their three-dimensional structures is challenging due to conformational flexibility.
- Accurate conformational prediction is essential for drug discovery and understanding molecular interactions.
Purpose of the Study:
- To develop and validate a novel stochastic search algorithm for predicting cyclic peptide conformations.
- To assess the algorithm's efficiency and accuracy compared to exhaustive search methods and experimental data.
Main Methods:
- A two-stage stochastic search algorithm was employed.
- Stage 1: Stepwise construction of cyclic peptides with conformational angle selection based on a penalty function for ring closure ability, utilizing dihedral angle data from diverse proteins.
- Stage 2: Side chain addition and fast optimization using a united atoms approach and the Kollman forcefield (Sybyl 6.8).
Main Results:
- The algorithm successfully identified global minimum conformations for cyclic peptides.
- Comparison with exhaustive searches showed comparable or superior results for smaller systems.
- For larger cyclic peptides (up to 15 amino acids), the root mean square deviation (RMSD) to experimental crystal structures was generally below 1.0 Å for up to 8-mers and below 2.0 Å for larger peptides.
Conclusions:
- The developed iterative stochastic elimination algorithm is an effective method for exploring the conformational space of cyclic peptides.
- The algorithm provides accurate predictions of cyclic peptide structures, demonstrating its utility in computational drug design and structural biology.