plm-cs 1.4

Protein chemical shift prediction based on Protein Language Model

PLM-CS

Predict protein chemical shifts from sequence

Train your model

If you want to train your own PLM-CS model, this repository provides all the tools and data. Just follow these steps.

Requirement

'torch == 2.5.0',
'torchaudio == 2.5.0',
'torchvision == 0.20.0',
'fair-esm == 2.0.0',
'numpy == 2.1.2',
'biopython == 1.84',
'pandas == 2.2.3'

Training set

We provide the complete training set data in RefDB training dataset. Each file in this folder is in nmrstar format, and each file corresponds to a protein. All proteins contained in the SHIFTX test are removed from it.

Training set processing

For convenience, the reasoning process of the ESM model is separate from the training process of our regression model. Therefore, we first use ESM-650M to process the data. Change the "save_path" in esm_process.py to your own path. A tensordataset containing the training data will be generated.

Train

Modify the path in the train.py to your own parh. Also, be aware that this can only train a model of one type of atom at a time.

Training parameters

Different atom types correspond to different optimizer strategies.You can modify the corresponding parameters in the train.py according to your trained model. The default number of steps for an iteration is 20,000, but you can change it to 5,000 to achieve very close performance while reducing training time

parameters	Cα	Cβ	C	Hα	H	N
learning rate	0.02	5e-4	0.002	0.01	5e-4	5e-4
optimizer	SGD	Adam	Adam	SGD	Adam	Adam

Evaluate

Use PLM-CS through python SDK