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H.U.M.A.N

Draft working store for Project H.U.M.A.N. (High Utility Molecular Affinity Nexus): Dual Personality Fragment (DPF) trajectories, PDB-cluster static structures.

Trajectories are derived from ATLAS. Structures are PDB entries. Encoder inputs are OpenFold recycle-3 embeddings of AlphaFold2. If you use this data, cite the sources below.

Catalog member paths are rooted at /workspace/rbase_data.

The ensemble, moving

Every conformation submitted for CASP17 target E2460, played one at a time and coloured by per-residue confidence on the AlphaFold pLDDT bands: dark blue above 90, light blue 90-70, orange 70-50, yellow below 50. Domain 1 holds its shape across the whole ensemble while domain 2 swings; the tag and the termini are where confidence falls away.

Layout

path what
Dual Personality Fragments/ 86 ATLAS DPF families: 81 with protein/<id>.pdb + replica XTCs; 5 test families are topology-only PDBs
DPF_folding_repr/ OpenFold recycle-3 representations for the 81 train+val DPF sequences
run/splits/0.json DPF family split (76 train / 5 val / 5 test)
catalog.json DPF catalog (86 families)
PDB_Clusters/ Static PDB members used by the cluster fine-tune
PDB_Cluster_folding_rep/ OpenFold recycle-3 representations for the cluster families (4,831 files)
TrainSplit_pdbcluster.json Cluster family split (1,442 train / 168 val / 68 test)
pdbcluster_catalog.json Cluster catalog (1,678 families)
confrover_base/ Published base weights file confrover_base_20m_v1_0.pt (ConfRover-base-20M-v1.0)
confrover_base_atlas_train_ids.csv 1,080 ATLAS IDs already in the published base train set (excludelist)
Run 1 PDBCluster_from base weights checkpoints/ Full PDB-cluster-from-base checkpoint set (70 files)
Run 2 DPF_from Clusterbase checkpoints/ Best total/iid and best forward checkpoints from DPF-on-cluster-weights
Run 3 DPF from base weights checkpoints/ Best total, forward, and iid checkpoints from DPF-from-base
Run 4 Reverse time from DPFBase checkpoints/ Best total/forward and best iid checkpoints from reverse-time DPF
code/ RBase.tar.gz plus COMMIT
bundles/ Packed cluster PDBs (pdbc.tar.gz)
compare_training_runs_1_to_4.png Overlay of Runs 1–4 (train / val / learning rate)
E2460_conformer_cloud_demo.mp4 Screen capture of the interactive viewer, stepping through all 1,000 submitted conformations
MANIFEST.sha256 Digests for the DPF payload files
CASP17 E2460 Submit/ The 1,000-conformation ensemble submitted for CASP17 target E2460, CASP identifiers removed

The five DPF test families keep topologies so the split fingerprint stays valid; fit never loads their trajectories.

Training curves

Runs 1–4 on a shared optimizer-step axis.

Compare training Runs 1 to 4

Box-run checkpoints

Four sequential box runs. Run 3 does not continue Run 2; it fine-tunes the published base in parallel with Run 1. Run 4 starts from Run 3's best-forward export.

folder started from what is stored
Run 1 PDBCluster_from base weights checkpoints/ published base entire Run 1 checkpoint set
Run 2 DPF_from Clusterbase checkpoints/ Run 1 export at step 8364 dpf-bestfwd-step00008296.ckpt (best total and iid val) and dpf-bestfwd-step00035764.ckpt (best forward val)
Run 3 DPF from base weights checkpoints/ published base dpfbase-stopped-step00005550.ckpt (best total), dpfbase-bestfwd-step00005722.ckpt (best forward), dpfbase-epoch008-step00002500.ckpt (nearest save to best iid)
Run 4 Reverse time from DPFBase checkpoints/ Run 3 export at step 5722 dpfrev4-bestfwd-step00000250.ckpt (best total and forward) and dpfrev4-bestfwd-step00000125.ckpt (best iid)

Those three val numbers are 9-frame windows.

Training notes

Practitioner notes from the four box runs. These are working observations that shaped the runs, not measured results from the evaluation suite.

Window size is hardware-bound, not method-bound. Training at 9 frames per protein per step holds one RTX PRO 6000 at 80-90 % utilisation with 128 GB of system RAM. Budget roughly one 6000 Pro Max-Q per 10 frames, which puts about 10 frames at the practical ceiling for a single card. More frames per window improves quality on large .XTC trajectory datasets, so the short window is a concession to hardware rather than a judgement that 9 frames is enough.

Epoch count compensates for the short window. Each epoch draws a different set of frames, so the frames a family contributes are re-sampled every pass. Total frame coverage therefore keeps growing with epochs even though the per-step window is fixed, which is why these runs use a large epoch count rather than a longer window.

20M parameters is a real capacity ceiling. At this size catastrophic forgetting is a live constraint and the network's ability to retain information across sequential fine-tuning stages is limited. That bounds how much a later stage can add before it starts displacing what the base model already holds. However inference can run on a single 4060 with 30GB(total used by system on Windows 11) of 128DDR5 generating around 250-1000 conformations in 3-4h depending on complexity. No results have been medically verified.

Reverse-time training cancels itself out unmodified. Run plainly, time-reversal training restores the previous weights: the reversed gradients undo the forward optimisation instead of adding to it. Run 4 behaves this way -- its forward validation loss never improved on step 250 across the remaining 1,935 steps and eight epochs, and its two bestfwd checkpoints both fall in the first epoch. The modified form takes the original optimizer state, compares it against the most likely negative value and adjusts the optimizer accordingly. It is very compute-heavy, but it is the route by which a limited training set might be made to go further.

Use

hf download AICanada/H.U.M.A.N --repo-type dataset --local-dir /workspace/rbase_data
python scripts/verify_remote_payload.py --root /workspace/rbase_data

From an RBase checkout: DPF scripts/vast_bootstrap_dpf.sh, cluster scripts/vast_bootstrap_pdbcluster.sh.

Citation

ATLAS

DPF trajectories in Dual Personality Fragments/ are derived from ATLAS.

Vander Meersche, Y., Cretin, G., Gheeraert, A., Gelly, J. C., & Galochkina, T. (2024). ATLAS: protein flexibility description from atomistic molecular dynamics simulations. Nucleic Acids Research, 52(D1), D384–D392. doi:10.1093/nar/gkad1084

@article{atlas2024,
  title={ATLAS: protein flexibility description from atomistic molecular dynamics simulations},
  author={Vander Meersche, Yann and Cretin, Gabriel and Gheeraert, Aria and Gelly, Jean-Christophe and Galochkina, Tatiana},
  journal={Nucleic Acids Research},
  volume={52},
  number={D1},
  pages={D384--D392},
  year={2024},
  doi={10.1093/nar/gkad1084}
}

PDB

Cluster members and DPF reference structures are Protein Data Bank entries.

Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242. doi:10.1093/nar/28.1.235

wwPDB consortium (2019). Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Research, 47(D1), D520–D528. doi:10.1093/nar/gky949

@article{berman2000pdb,
  title={The {Protein Data Bank}},
  author={Berman, Helen M. and Westbrook, John and Feng, Zukang and Gilliland, Gary and Bhat, T. N. and Weissig, Helge and Shindyalov, Ilya N. and Bourne, Philip E.},
  journal={Nucleic Acids Research},
  volume={28},
  number={1},
  pages={235--242},
  year={2000},
  doi={10.1093/nar/28.1.235}
}

@article{wwpdb2019,
  title={{Protein Data Bank}: the single global archive for {3D} macromolecular structure data},
  author={{wwPDB consortium}},
  journal={Nucleic Acids Research},
  volume={47},
  number={D1},
  pages={D520--D528},
  year={2019},
  doi={10.1093/nar/gky949}
}

UniProt

UniProt Consortium (2025). UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Research, 53(D1), D609–D617. doi:10.1093/nar/gkae1010

@article{uniprot2025,
  title={{UniProt}: the {Universal Protein Knowledgebase} in 2025},
  author={{UniProt Consortium}},
  journal={Nucleic Acids Research},
  volume={53},
  number={D1},
  pages={D609--D617},
  year={2025},
  doi={10.1093/nar/gkae1010}
}

OpenFold

Encoder representations are OpenFold recycle-3 embeddings.

Ahdritz, G., Bouatta, N., Floristean, C., et al. (2024). OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nature Methods, 21, 1514–1524. doi:10.1038/s41592-024-02272-z

@article{ahdritz2024openfold,
  title={{OpenFold}: retraining {AlphaFold2} yields new insights into its learning mechanisms and capacity for generalization},
  author={Ahdritz, Gustaf and Bouatta, Nazim and Floristean, Christina and others},
  journal={Nature Methods},
  volume={21},
  pages={1514--1524},
  year={2024},
  doi={10.1038/s41592-024-02272-z}
}

AlphaFold

OpenFold implements AlphaFold2.

Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. doi:10.1038/s41586-021-03819-2

@article{jumper2021alphafold,
  title={Highly accurate protein structure prediction with {AlphaFold}},
  author={Jumper, John and Evans, Richard and Pritzel, Alexander and others},
  journal={Nature},
  volume={596},
  number={7873},
  pages={583--589},
  year={2021},
  doi={10.1038/s41586-021-03819-2}
}

ConfRover

Published base weights ConfRover-base-20M-v1.0. and original inference code at https://github.com/ByteDance-Seed/ConfRover,
https://huggingface.co/papers/2505.17478, https://arxiv.org/abs/2505.17478,

@article{confrover2025,
  title={Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression},
  author={Shen, Yuning and Wang, Lihao and Yuan, Huizhuo and Wang, Yan and Yang, Bangji and Gu, Quanquan},
  journal={arXiv preprint arXiv:2505.17478},
  year={2025}
}

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