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Pre-trained Antibody generative large Language Model

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Hardware requirements

PALM-H3 package requires only a standard computer with enough RAM and a NVIDIA GPU to support operations. We ran the demo using the following specs:

  • CPU: 10 cores, 2.5 GHz/core
  • RAM: 40 GB
  • GPU: NVIDIA TESLA V100

System requirements

This tool is supported for Linux. The tool has been tested on the following system:

  • CentOS Linux release 8.2.2.2004

Installation

To install the required packages for running PALM-H3, please use the following command:

conda create -n <env_name> python==3.9
conda activate <env_name>
pip install -r requirements.txt

Time cost

Typical install time on a "normal" desktop computer is about 30 minutes.

How to train and use PALM-H3

The training of PALM-H3 and A2binder consists of three steps: first, we pre-train two language models on unpaired antibody heavy and light chain sequences, respectively. Then we construct A2binder, and fine-tune it using paired affinity data. Finally, we construct PALM-H3 by Roformer and ESM2 using paired data for designing and evaluating the AI-generated CDRH3. The details of each training are in the Code/config folder. Note that all the commands are run in the Code folder.

1. Pre-train on unpaired sequences

The MAA task is used for the self-training of HeavyRoformer and LightRoformer.

Due to the space limitation, we present demo unpaired data in the folder ProcessedData.

The training command for HeavyRoformer is:

python bert_pretrain_maa_main.py --config ./config/common/bert_pretrain_maa_common_heavy_covid.json

The training command for LightRoformer is:

python bert_pretrain_maa_main.py --config ./config/common/bert_pretrain_maa_common_light_covid.json

After the training, the pre-trained HeavyRoformer and LightRoformer will be saved in the ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX and ../Result_covid_light/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX folder, where XXXX_XXXXXX is the timestamp of the training.

Time cost

Expected run time for demo on a "normal" desktop computer is about 2 hours.

2. Training A2binder on paired affinity datasets

Before running the affinity predicition task, please copy the absolute path of pre-trained HeavyRoformer (../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX) and LightRoformer (../Result_covid_light/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX) to replace the corresponding file path in the config file bert_finetuning_er_common_Cov_abdab.json. In detail: please replace the "heavy_dir" using ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX ; replace the "light_dir" using ../Result_covid_light/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX. Besides, you should also replace the "antibody_tokenizer_dir" to the path ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX. The above path needs to be an absolute path.

The training command for A2binder is:

python bert_finetuning_er_main.py --config ./config/common/bert_finetuning_er_common_Cov_abdab.json

After the training, the trained A2binder will be saved in the ../Result_cov_adbab/checkpoints/BERT-Finetunning-Antibody-Binding-common-abdab/XXXX_XXXXXX folder. Due to the use of ESM2 model parameters as the antigen model, there may be network errors when downloading ESM2 model parameters. Please check the network settings or try again later. You can also download files in huggingface and put them in ./esm2/esm2_150m and ./cache for tokenizer and model.

3. Training PALM-H3 on seq2seq task

Before running the seq2seq task, please copy the absolute path of pre-trained HeavyRoformer (../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX) to replace the corresponding file path in the config file bert_finetuning_er_seq2seq_common.json. In detail: please replace the "AntibodyBert_dir" using ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX. Besides, you should also replace the "antibody_tokenizer_dir" and "antigen_tokenizer_dir" to ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX. The above path needs to be an absolute path.

The training command for PALM-H3 is:

python bert_finetuning_seq2seq_main.py --config ./config/common/bert_finetuning_er_seq2seq_common.json

After the training, the trained PALM-H3 will be saved in the ../Result_seq2seq/checkpoints/ABAG-Finetuning-Seq2seq-Common/XXXX_XXXXXX/ folder.

4. Generate artificial antibodies

Before running the generation task, please copy the absolute path of PLAM ../Result_seq2seq/checkpoints/ABAG-Finetuning-Seq2seq-Common/XXXX_XXXXXX/ to "resume", copy the absolute path of ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXXto "antibody_tokenizer_dir" and "antigen_tokenizer_dir" in the config file seq2seq_generate.json. The above path needs to be an absolute path.

Optionally, you can customize "origin_seq", "origin_light", "cdrh3'begin", "cdrh3_end", and "use_antigen" in the config file seq2seq_generate.json, which represent the original heavy chain, the original light chain, the index of the beginning and end of the cdrh3 region, and the sequence of the antigen, respectively. Please note that we input the original light and heavy chains, as well as the start and end indices of cdrh3, only to replace the generated cdrh3 for the convenience of subsequent evaluation steps

The generation command for PALM-H3 is:

python generate_antibody.py --config ./config/common/seq2seq_generate.json

Expected output

After the running, the artificial antibody will be saved in the ../Result_seq2seq_gen/datasplit/CoV_AbDab-Seq2seq-Evaluate-Common/XXXX_XXXXXX/result.csv.

The generated file contains five columns: Antigen, Generated_CDR_H3, Heavy_Chain, Light_Chain. Among them, Antigen and Light_Chain do not differ from the input, Generated_CDR_H3 is the cdrh3 region sequence generated by the model, Heavy_Chain replaces the natural heavy cdrh3 region with the generated Generated_CDR_H3.

5. Evaluate artificial antibodies

After generating antibodies, A2binder can be used to evaluate the affinity probability or affinity of the generated antibodies. Before evaluating, please copy the absolute path of A2binder ../Result_cov_adbab/checkpoints/BERT-Finetunning-Antibody-Binding-common-abdab/XXXX_XXXXXX/model_best.pth to "discriminator_resume", replace the "heavy_dir" using ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX, replace the "light_dir" using ../Result_covid_light/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX, and replace "antibody_tokenizer_dir" to ../Result_covid_heavy/checkpoints/BERT-Pretrain-common-MAA-NGPUs/XXXX_XXXXXX in the bert_eval_generation.json and change the "data_dir" to ../Result_seq2seq_gen/datasplit/CoV_AbDab-Seq2seq-Evaluate-Common/XXXX_XXXXXX/result.csv. The above path needs to be an absolute path.

The evalation command for PALM-H3 is:

python eval_generate_seq.py --config ./config/common/bert_eval_generation.json

Expected output

After the running, the evalation result will be saved in the ../Result_eval/datasplit/Eval-genetation/XXXX_XXXXXX/test_result.csv

The generated file contains four columns: heavy, light, antigen, y_pred refers to the evaluation results of the light heavy chain sequence, antigen sequence, and model output. In this case, the higher the result of y_pred, the greater the probability of affinity. However, in other cases, y_pred is related to the evaluation label of model training, depending on whether the training label is larger and better or smaller and better.

Model availability

PALM-H3 and A2binder on all the three tasks (Pre-training, Affinity predicition, and Seq2Seq) on the comprehensive training dataset are available on Zenodo: https://doi.org/10.5281/zenodo.7794583. And you can fine-tuning it on your own dataset and downstream tasks.

Data availability

Due to the space limitation, we present part of data used in this project in the folder ProcessedData. Full pre-training data are available from https://opig.stats.ox.ac.uk/webapps/oas/.

Contact

If you have any questions, please contact us via email: