About The Project

Protein pKa is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-pKa values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein pKa prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a minimal absolute error (MAE) of only 0.33 pKa units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.

Getting Started

Installation

1. Clone the repo

   git clone https://github.com/DeepSynthesis/PSPOC-version-1.0

2. Create new python environment

   conda create -n HPSPOC-env python=3.11
   conda activate HPSPOC-env
   pip install rdkit-pypi
   conda install pandas=2.0
   conda install numpy=1.24
   conda install scikit-learn=1.2.2
   conda install xgboost=1.7
   conda install matplotlib=3.7

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Usage

1. Descriptor generation for training and test set:

   cd script
   python PSPOC_web_v3.py

2. Model training and model test:

   python xgboostMethod.py

3. Predict pKa for your own data:

   open ./script/pspoc_v1.py ,then you can find codes as below around line 53.

   if __name__ == '__main__':    
   
    PATH='./Pred/'
    datafilename='your data file'

   change line 53 datafilename='your data file' to your datafile name. The data (.csv) must include column named "PDB_ID" "Chain_ID" "Residue_ID" "Residue" .

   then run

   python pspoc_v1.0.py

   After prediction, the descriptors of your data will be saved in PredicPSPOC.csv, and the result of your data will be saved in After******.csv .

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