Update README.md

README.md

@@ -127,6 +127,21 @@ The scaffolds are computational predictions and may not all fold as designed in
 
 Users should be aware that most of these are computationally predicted structures, not experimentally determined ones.
 
+
+## 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.
+- **PAE**: Predicted Alignment Error — a metric from AlphaFold2 that estimates the error in the relative position of pairs of residues.
+- **Hallucination**: A de novo design approach that simultaneously generates sequence and structure by optimizing AlphaFold2 confidence metrics starting from a random sequence.
+- **Pnear**: A Rosetta-derived metric (0 to 1) indicating folding propensity; a value of 1 means the designed structure is the single lowest-energy conformation.
+- **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.
+
 ## Citation
 
 **BibTeX:**
@@ -145,22 +160,9 @@ Users should be aware that most of these are computationally predicted structure
 ```
 
 **APA:**
-
+```
 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.
-- **PAE**: Predicted Alignment Error — a metric from AlphaFold2 that estimates the error in the relative position of pairs of residues.
-- **Hallucination**: A de novo design approach that simultaneously generates sequence and structure by optimizing AlphaFold2 confidence metrics starting from a random sequence.
-- **Pnear**: A Rosetta-derived metric (0 to 1) indicating folding propensity; a value of 1 means the designed structure is the single lowest-energy conformation.
-- **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