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Synthesis and Structure Determination of µ-Conotoxin PIIIA Isomers with Different Disulfide Connectivities
Published on: October 2, 2018
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Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy
Huanyu Tao1, Xuejun Zhao1, Keqiong Zhang1
1School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Bioinformatics (Oxford, England)
|July 8, 2022
Summary
A new computational method, HPEPDOCK2.0, improves the prediction of cyclic peptide-protein interactions. This tool enhances the development of cyclic peptide drugs by accurately modeling peptide flexibility.
Area of Science:
- Computational chemistry
- Biophysics
- Drug discovery
Background:
- Cyclic peptides are crucial in drug development, with disulfide-driven cyclic peptides being a prevalent category.
- Accurately predicting the binding modes of cyclic peptides to proteins is challenging due to their conformational flexibility.
Purpose of the Study:
- To develop and evaluate HPEPDOCK2.0, an enhanced hierarchical algorithm for predicting the binding modes of disulfide-driven cyclic peptides against proteins.
- To address the challenge of peptide flexibility in protein-cyclic peptide docking.
Main Methods:
- Integration of the MODPEP2.0 peptide 3D conformation sampling algorithm and the ITScorePP knowledge-based scoring function.
- Development of HPEPDOCK2.0, an extended hierarchical peptide docking algorithm.
- Extensive evaluation on diverse benchmark datasets and comparison with AutoDock CrankPep (ADCP).
Main Results:
- HPEPDOCK2.0 achieved a native contact fraction of >0.5 in 61% of cases on an 18-complex dataset, outperforming ADCP (39%).
- On a 25-complex dataset, HPEPDOCK2.0 showed a 44% success rate for top predictions, compared to 20% for ADCP.
- HPEPDOCK2.0 is computationally efficient, with an average docking time of 34 minutes per cyclic peptide on a single CPU core, significantly faster than ADCP (496 minutes).
Conclusions:
- HPEPDOCK2.0 effectively predicts the binding modes of disulfide-driven cyclic peptides, addressing key challenges in conformational flexibility.
- The method demonstrates superior performance and computational efficiency compared to existing tools.
- HPEPDOCK2.0 is a valuable tool for advancing the study of cyclic peptide-protein interactions and facilitating the development of novel cyclic peptide therapeutics.
