Datasets:

RosettaCommons
/

AfCycDesign

@@ -25,17 +25,14 @@ Sets 7-16 of hallucinated peptide cif files.
 This dataset contains hallucinated cyclic peptide scaffold structures (in CIF format) generated using AfCycDesign, a deep learning approach built on AlphaFold2 for de novo design of cyclic peptides. The scaffolds span peptide lengths of 7–16 residues and were generated through a hallucination procedure that simultaneously samples sequence and structure to produce well-ordered cyclic peptides. Structures in this dataset were filtered for high prediction confidence (pLDDT > 0.9) by AfCycDesign, representing 24,104 structurally diverse peptides predicted to fold into their designed conformations. These scaffolds serve as starting points for incorporating functional elements such as target-binding motifs for therapeutic applications.
-- **Curated by:** Zachary Drake
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Language(s) (NLP):** Not applicable (structural biology data)
 - **License:** MIT
-### Dataset Sources [optional]
-- **Repository:** [More Information Needed]
 - **Paper [optional]:** Rettie, S.A., Campbell, K.V., Bera, A.K. et al. Cyclic peptide structure prediction and design using AlphaFold2. *Nature Communications* **16**, 4730 (2025). https://doi.org/10.1038/s41467-025-59940-7
-- **Demo [optional]:** [More Information Needed]
 ## Uses
@@ -59,6 +56,8 @@ The scaffolds were generated to provide a large, structurally diverse library of
 ### Source Data
 #### Data Collection and Processing
 Scaffolds were generated using the AfCycDesign hallucination pipeline, which modifies AlphaFold2 with a custom cyclic offset matrix for relative positional encoding. For each peptide size (7–13 residues initially, later extended to 16), 48,000 hallucinated models were generated. Structures were clustered using a torsion bin-based approach, and the highest-confidence member from each cluster (pLDDT > 0.9) was selected. The hallucination is guided by losses that optimize prediction confidence metrics (pLDDT and predicted alignment error) and the number of intramolecular contacts. The pipeline was implemented within the ColabDesign v1.1.2 framework.
@@ -67,8 +66,6 @@ Scaffolds were generated using the AfCycDesign hallucination pipeline, which mod
 The data was computationally generated by researchers at the Institute for Protein Design, University of Washington, and collaborators at Harvard University. Key contributors include Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Sergey Ovchinnikov, and Gaurav Bhardwaj.
-### Annotations [optional]
 #### Annotation process
 No manual annotations were applied. Structural quality metrics (pLDDT, PAE) are computed automatically by the AfCycDesign pipeline. Torsion bin strings are assigned computationally based on backbone dihedral angles.

 This dataset contains hallucinated cyclic peptide scaffold structures (in CIF format) generated using AfCycDesign, a deep learning approach built on AlphaFold2 for de novo design of cyclic peptides. The scaffolds span peptide lengths of 7–16 residues and were generated through a hallucination procedure that simultaneously samples sequence and structure to produce well-ordered cyclic peptides. Structures in this dataset were filtered for high prediction confidence (pLDDT > 0.9) by AfCycDesign, representing 24,104 structurally diverse peptides predicted to fold into their designed conformations. These scaffolds serve as starting points for incorporating functional elements such as target-binding motifs for therapeutic applications.
+- **Curated by:** Akshaya Narayanasamy, Jie Chen, Lina Maria Pena, Zachary Drake
 - **Language(s) (NLP):** Not applicable (structural biology data)
 - **License:** MIT
+### Dataset Sources
+- **Repository:** https://zenodo.org/records/15164650
 - **Paper [optional]:** Rettie, S.A., Campbell, K.V., Bera, A.K. et al. Cyclic peptide structure prediction and design using AlphaFold2. *Nature Communications* **16**, 4730 (2025). https://doi.org/10.1038/s41467-025-59940-7
 ## Uses
 ### Source Data
+https://zenodo.org/records/15164650
 #### Data Collection and Processing
 Scaffolds were generated using the AfCycDesign hallucination pipeline, which modifies AlphaFold2 with a custom cyclic offset matrix for relative positional encoding. For each peptide size (7–13 residues initially, later extended to 16), 48,000 hallucinated models were generated. Structures were clustered using a torsion bin-based approach, and the highest-confidence member from each cluster (pLDDT > 0.9) was selected. The hallucination is guided by losses that optimize prediction confidence metrics (pLDDT and predicted alignment error) and the number of intramolecular contacts. The pipeline was implemented within the ColabDesign v1.1.2 framework.
 The data was computationally generated by researchers at the Institute for Protein Design, University of Washington, and collaborators at Harvard University. Key contributors include Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Sergey Ovchinnikov, and Gaurav Bhardwaj.
 #### Annotation process
 No manual annotations were applied. Structural quality metrics (pLDDT, PAE) are computed automatically by the AfCycDesign pipeline. Torsion bin strings are assigned computationally based on backbone dihedral angles.