Datasets:

RosettaCommons
/

AfCycDesign

@@ -19,7 +19,7 @@ Hallucinated scaffolds used by AfCycDesign for cyclic peptide design.
 ## Dataset Details
-Sets 7-16 of hallucinated peptide cif files.
 ### Dataset Description
@@ -48,8 +48,6 @@ These scaffolds are computational design models and have not all been experiment
 Each entry in the dataset is a CIF (Crystallographic Information File) representing the 3D atomic coordinates of a hallucinated cyclic peptide scaffold. Scaffolds range from 7 to 16 residues in length and are organized by peptide size (sets 7–16). Each scaffold represents a unique structural cluster identified through torsion bin-based clustering, where each residue is assigned a bin (A, B, X, or Y) based on its φ, ψ, and ω backbone torsion angles. All scaffolds in this dataset were predicted to fold into their designed structures with high confidence (pLDDT > 0.9) by AfCycDesign.
-## Dataset Creation
 ### Curation Rationale
 The scaffolds were generated to provide a large, structurally diverse library of well-folded cyclic peptide backbones for downstream functional design. Prior physics-based methods (e.g., Rosetta kinematic closure) were computationally expensive and struggled with larger macrocycles (11–13 residues) without additional disulfide crosslinks. AfCycDesign's hallucination approach overcomes these limitations, enabling rapid enumeration of diverse cyclic peptide topologies including structures with short α-helices, β-sheets, and loop-only conformations.
@@ -86,7 +84,7 @@ The scaffolds are computational predictions and may not all fold as designed in
 Users should be aware that these are computationally predicted structures, not experimentally determined ones. For any downstream application (e.g., binder design), experimental validation of peptide folding and function is strongly recommended. Users are encouraged to evaluate all five AlphaFold2 output models rather than relying solely on the highest-pLDDT model. Orthogonal validation using Rosetta energy landscape calculations (Pnear) is recommended for prioritizing candidates for synthesis. When designing binders, combining AfCycDesign with ProteinMPNN for sequence design may yield a higher diversity of high-confidence designs.
-## Citation [optional]
 **BibTeX:**
@@ -107,7 +105,7 @@ Users should be aware that these are computationally predicted structures, not e
 Rettie, S. A., Campbell, K. V., Bera, A. K., Kang, A., Kozlov, S., Flores Bueso, Y., De La Cruz, J., Ahlrichs, M., Cheng, S., Gerben, S. R., Lamb, M., Murray, A., Adebomi, V., Zhou, G., DiMaio, F., Ovchinnikov, S., & Bhardwaj, G. (2025). Cyclic peptide structure prediction and design using AlphaFold2. *Nature Communications*, *16*, 4730. https://doi.org/10.1038/s41467-025-59940-7
-## Glossary [optional]
 - **AfCycDesign**: The deep learning framework described in this paper that adapts AlphaFold2 for cyclic peptide structure prediction and design by introducing cyclic relative positional encoding.
 - **pLDDT**: Predicted Local Distance Difference Test — a per-residue confidence metric from AlphaFold2, ranging from 0 to 1, where higher values indicate greater prediction confidence.
@@ -117,14 +115,10 @@ Rettie, S. A., Campbell, K. V., Bera, A. K., Kang, A., Kozlov, S., Flores Bueso,
 - **CIF**: Crystallographic Information File — a standard file format for representing 3D molecular structures.
 - **Torsion bin clustering**: A method for grouping peptide structures by assigning bins (A, B, X, Y) based on backbone φ, ψ, and ω dihedral angles.
-## More Information [optional]
 The AfCycDesign code is implemented within the ColabDesign framework. For additional details on methods and supplementary data, see the full paper and its supplementary materials at https://doi.org/10.1038/s41467-025-59940-7.
-## Dataset Card Authors [optional]
-Zachary Drake
-## Dataset Card Contact
-[More Information Needed]

 ## Dataset Details
+Sets 7-16 of hallucinated peptide cif files and experimental CCDC structures.
 ### Dataset Description
 Each entry in the dataset is a CIF (Crystallographic Information File) representing the 3D atomic coordinates of a hallucinated cyclic peptide scaffold. Scaffolds range from 7 to 16 residues in length and are organized by peptide size (sets 7–16). Each scaffold represents a unique structural cluster identified through torsion bin-based clustering, where each residue is assigned a bin (A, B, X, or Y) based on its φ, ψ, and ω backbone torsion angles. All scaffolds in this dataset were predicted to fold into their designed structures with high confidence (pLDDT > 0.9) by AfCycDesign.
 ### Curation Rationale
 The scaffolds were generated to provide a large, structurally diverse library of well-folded cyclic peptide backbones for downstream functional design. Prior physics-based methods (e.g., Rosetta kinematic closure) were computationally expensive and struggled with larger macrocycles (11–13 residues) without additional disulfide crosslinks. AfCycDesign's hallucination approach overcomes these limitations, enabling rapid enumeration of diverse cyclic peptide topologies including structures with short α-helices, β-sheets, and loop-only conformations.
 Users should be aware that these are computationally predicted structures, not experimentally determined ones. For any downstream application (e.g., binder design), experimental validation of peptide folding and function is strongly recommended. Users are encouraged to evaluate all five AlphaFold2 output models rather than relying solely on the highest-pLDDT model. Orthogonal validation using Rosetta energy landscape calculations (Pnear) is recommended for prioritizing candidates for synthesis. When designing binders, combining AfCycDesign with ProteinMPNN for sequence design may yield a higher diversity of high-confidence designs.
+## Citation
 **BibTeX:**
 Rettie, S. A., Campbell, K. V., Bera, A. K., Kang, A., Kozlov, S., Flores Bueso, Y., De La Cruz, J., Ahlrichs, M., Cheng, S., Gerben, S. R., Lamb, M., Murray, A., Adebomi, V., Zhou, G., DiMaio, F., Ovchinnikov, S., & Bhardwaj, G. (2025). Cyclic peptide structure prediction and design using AlphaFold2. *Nature Communications*, *16*, 4730. https://doi.org/10.1038/s41467-025-59940-7
+## Glossary
 - **AfCycDesign**: The deep learning framework described in this paper that adapts AlphaFold2 for cyclic peptide structure prediction and design by introducing cyclic relative positional encoding.
 - **pLDDT**: Predicted Local Distance Difference Test — a per-residue confidence metric from AlphaFold2, ranging from 0 to 1, where higher values indicate greater prediction confidence.
 - **CIF**: Crystallographic Information File — a standard file format for representing 3D molecular structures.
 - **Torsion bin clustering**: A method for grouping peptide structures by assigning bins (A, B, X, Y) based on backbone φ, ψ, and ω dihedral angles.
+## More Information
 The AfCycDesign code is implemented within the ColabDesign framework. For additional details on methods and supplementary data, see the full paper and its supplementary materials at https://doi.org/10.1038/s41467-025-59940-7.
+## Dataset Card Authors
+Jie Chen, Zachary Drake