nz/OpenFold

Mirror of OpenFold parameters as provided in https://github.com/aqlaboratory/openfold. Stopgap solution as the original download link was down. Updated based on the s3 bucket parameter update. All rights to the authors.

OpenFold model parameters, v. 06_22.

Training details:

Trained using OpenFold on 44 A100s using the training schedule from Table 4 in the AlphaFold supplement. AlphaFold was used as the pre-distillation model. Training data is hosted publicly in the "OpenFold Training Data" RODA repository.

To improve model diversity, we forked training after the initial training phase and finetuned an additonal branch without templates.

Parameter files:

Parameter files fall into the following categories:

initial_training.pt: 
    OpenFold at the end of the initial training phase.
finetuning_x.pt:
    Checkpoints in chronological order corresponding to peaks in the 
    validation LDDT-Ca during the finetuning phase. Roughly evenly spaced 
    across the 45 finetuning epochs.
    
    NOTE: finetuning_1.pt, which was included in a previous release, has
    been deprecated.
finetuning_no_templ_x.pt
    Checkpoints in chronological order corresponding to peaks during an
    additional finetuning phase also starting from the 'initial_training.pt' 
    checkpoint but with templates disabled.
finetuning_no_templ_ptm_x.pt
    Checkpoints in chronological order corresponding to peaks during the 
    pTM training phase of the `no_templ` branch. Models in this category
    include the pTM module and comprise the most recent of the checkpoints
    in said branch.
finetuning_ptm_x.pt:
    Checkpoints in chronological order corresponding to peaks in the pTM
    training phase of the mainline branch. Models in this category include 
    the pTM module and comprise the most recent of the checkpoints in said
    branch.

Average validation LDDT-Ca scores for each of the checkpoints are listed below. The validation set contains approximately 180 chains drawn from CAMEO over a three-month period at the end of 2021.

initial_training: 0.9088
finetuning_2: 0.9061
finetuning_3: 0.9075
finetuning_4: 0.9059
finetuning_5: 0.9054
finetuning_no_templ_1: 0.9014
finetuning_no_templ_2: 0.9032
finetuning_no_templ_ptm_1: 0.9025
finetuning_ptm_1: 0.9075
finetuning_ptm_2: 0.9097