Update README.md

README.md

@@ -25,7 +25,8 @@ configs:
 
 # Protenix Data
 
-This dataset preserves the source folder as tar shards. `metadata.csv` has one row per original file and is configured as the default Dataset Viewer table, while the actual bytes live in `shards/*.tar`.
+Protenix is ByteDance's open-source PyTorch reproduction of AlphaFold3, a biomolecular structure predictor that handles proteins, DNA, RNA, ligands, ions, and modifications under a unified all-atom diffusion model. Alongside the model code and weights, the team released the full preprocessed training dataset used to train Protenix and its successors, making it one of the largest publicly available AF3-style training corpora.
+The released data is built from the wwPDB and is intended to be drop-in usable for AF3-style training. The original v0.5.0 release uses a PDB cutoff of 2021-09-30 (matching AF3), and Protenix-v1-20250630 extends the cutoff to 2025-06-30 on a larger curated set. MSAs are generated with the ColabFold pipeline against UniRef30 and the ColabFold environmental database, with UniRef sequences retained for pairing via taxonomy IDs and the remainder folded into the unpaired MSA features. Protenix-v1 also adds RNA MSAs and HMMER-derived structural templates aligned with the AF3 template strategy.
 
 ## Summary
 
@@ -172,3 +173,17 @@ for row in files:
 ## Notes
 
 The tar shards are uncompressed by design. This keeps packaging and random extraction simple and avoids spending CPU compressing data that is often already compressed.
+
+# Citation
+
+```
+@article{bytedance2025protenix,
+  title     = {Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction},
+  author    = {ByteDance AML AI4Science Team and Chen, Xinshi and Zhang, Yuxuan and Lu, Chan and Ma, Wenzhi and Guan, Jiaqi and Gong, Chengyue and Yang, Jincai and Zhang, Hanyu and Zhang, Ke and Wu, Shenghao and Zhou, Kuangqi and Yang, Yanping and Liu, Zhenyu and Wang, Lan and Shi, Bo and Shi, Shaochen and Xiao, Wenzhi},
+  journal   = {bioRxiv},
+  year      = {2025},
+  publisher = {Cold Spring Harbor Laboratory},
+  doi       = {10.1101/2025.01.08.631967},
+  url       = {https://www.biorxiv.org/content/10.1101/2025.01.08.631967v1}
+}
+```