Use AfCycDesign
Official Neurosnap webserver for accessing AfCycDesign online.
Overview
AfCycDesign is a cutting-edge deep learning model that leverages the power of AlphaFold2 to facilitate the precise design of macrocyclic peptides. Cyclic peptides have emerged as promising candidates in the realm of therapeutics, but designing them accurately has been a challenge due to limited structural data for molecules of this size. AfCycDesign addresses this gap by modifying the AlphaFold network to predict and design cyclic peptide structures effectively.
Neurosnap Overview
The AfCycDesign online webserver allows anybody with a Neurosnap account to run and access AfCycDesign, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.
View PaperFeatures
- Utilizes AlphaFold2 for macrocyclic peptide design.
- Designed for accurate structure prediction and design of cyclic peptides.
- Addresses challenges in designing cyclic peptides due to limited structural data.
- Predicts native cyclic peptide structures with high confidence.
- Impressive accuracy, with 36 out of 49 cases closely matching native structures (pLDDT > 0.85, RMSD < 1.5 Ã…).
- Offers computational methods for designing peptide backbones generated by different sampling techniques.
- Enables De novo design of novel macrocyclic peptides.
- Extensive structural diversity sampling for cyclic peptides (7-13 amino acids).
- Identifies around 10,000 unique design candidates with high-confidence folding predictions.
- Validation through X-ray crystal structures closely matching design models (RMSD < 1.0 Ã…).
- Provides a basis for custom-designing peptides for targeted therapeutic applications.
Statistics
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API Request
Access AfCycDesign using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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