Update README.md

README.md

@@ -52,7 +52,8 @@ These scaffolds are computational design models and have not all been experiment
 
 ## Dataset Structure
 
-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.
+Each entry in the dataset includes a PDB file for hallucinated data 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.
+Experimental entries include CIF files.
 
 ### Curation Rationale
 
@@ -70,10 +71,6 @@ 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.
 
-#### 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.
-
 #### Who are the annotators?
 
 Annotations are algorithmically generated by the AfCycDesign pipeline and AlphaFold2 confidence metrics.
@@ -84,11 +81,11 @@ This dataset does not contain any personal, sensitive, or private information. A
 
 ## Bias, Risks, and Limitations
 
-The scaffolds are computational predictions and may not all fold as designed in experimental conditions. Validation was performed on a representative subset (8 X-ray crystal structures, all with RMSD < 1.0 Å to design models), but the full set has not been experimentally characterized. The hallucination approach is limited to the 20 canonical L-amino acids and does not natively incorporate D-amino acids or other non-canonical residues. For peptide sizes of 11+ residues, the 48,000 hallucination runs may not have fully sampled the available structural space, meaning additional unique scaffolds likely exist. The confidence metric (pLDDT) used for filtering, while strongly predictive of structural accuracy, is not a guarantee of experimental success.
+The scaffolds are computational predictions and may not all fold as designed in experimental conditions. Validation were performed on a representative subset (8 X-ray crystal structures, all with RMSD < 1.0 Å to design models), but the full set has not been experimentally characterized. The hallucination approach is limited to the 20 canonical L-amino acids and does not natively incorporate D-amino acids or other non-canonical residues. For peptide sizes of 11+ residues, the 48,000 hallucination runs may not have fully sampled the available structural space, meaning additional unique scaffolds likely exist. The confidence metric (pLDDT) used for filtering, while strongly predictive of structural accuracy, is not a guarantee of experimental success.
 
 ### Recommendations
 
-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.
+Users should be aware that most of these are computationally predicted structures, not experimentally determined ones.
 
 ## Citation