Implementation of pKALM: Accurate and Rapid Prediction of Protein pKa: Protein Language Models Reveal the Sequence-pKa Relationship
Python
6
3 commits
updated Feb 24, 2026
This is the official implementation of the paper "Accurate and Rapid Prediction of Protein pKa: Protein Language Models Reveal the Sequence-pKa Relationship".
Our pKALM server is available: Access pKALM
Update 2026/2/24. Please download the checkpoints and configuration files at https://huggingface.co/xushijie/pKALM.
Update 2025/9/12. Our server is under maintenance, please be patient to wait until it recovers.
conda create -n pkalm python=3.12
conda activate pkalm
pip install -r requirements.txt
Use predict_batch.py to predict pKa values using a FASTA file:
--input: a FASTA-formatted file containing protein sequences.--out_dir: the output directory.Use predict.py to predict pKa values using an input file:
--input: a CSV-formatted file containing idx, res, and seq columns.--out_dir: the output directory.data/seq_train.csv and data/seq_test.csv: The training and testing datasets used in the paper.data/PKAD2_DOWNLOAD.xlsx: The raw PKAD-2 dataset.data/process_PKAD2_DOWNLOAD_rev.xlsx: The processed PKAD-2 dataset.data/IPC2_peptide_25.csv and data/IPC2_peptide_75.csv: The peptide IPC2 datasets for testing and training.data/IPC2_protein_25.csv and data/IPC2_protein_75.csv: The protein IPC2 datasets for testing and training.data/UP000005640_9606.fasta: The human proteome dataset for benchmarking speeds.If you have any questions, don't hesitate to get in touch with me at [email protected].
Implementation of pKALM: Accurate and Rapid Prediction of Protein pKa: Protein Language Models Reveal the Sequence-pKa Relationship
Python
6
3 commits
updated Feb 24, 2026
This is the official implementation of the paper "Accurate and Rapid Prediction of Protein pKa: Protein Language Models Reveal the Sequence-pKa Relationship".
Our pKALM server is available: Access pKALM
Update 2026/2/24. Please download the checkpoints and configuration files at https://huggingface.co/xushijie/pKALM.
Update 2025/9/12. Our server is under maintenance, please be patient to wait until it recovers.
conda create -n pkalm python=3.12
conda activate pkalm
pip install -r requirements.txt
Use predict_batch.py to predict pKa values using a FASTA file:
--input: a FASTA-formatted file containing protein sequences.--out_dir: the output directory.Use predict.py to predict pKa values using an input file:
--input: a CSV-formatted file containing idx, res, and seq columns.--out_dir: the output directory.data/seq_train.csv and data/seq_test.csv: The training and testing datasets used in the paper.data/PKAD2_DOWNLOAD.xlsx: The raw PKAD-2 dataset.data/process_PKAD2_DOWNLOAD_rev.xlsx: The processed PKAD-2 dataset.data/IPC2_peptide_25.csv and data/IPC2_peptide_75.csv: The peptide IPC2 datasets for testing and training.data/IPC2_protein_25.csv and data/IPC2_protein_75.csv: The protein IPC2 datasets for testing and training.data/UP000005640_9606.fasta: The human proteome dataset for benchmarking speeds.If you have any questions, don't hesitate to get in touch with me at [email protected].