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Cross_validation
Cross_validation
 
 
Data_analysis
Data_analysis
 
 
SWI
SWI
 
 
Surface_amino_acid_test
Surface_amino_acid_test
 
 
Testing_other_tools
Testing_other_tools
 
 
benchmarking
benchmarking
 
 
datasets_used
datasets_used
 
 
protr_R_Package_analysis
protr_R_Package_analysis
 
 
solubility_statistics
solubility_statistics
 
 
.gitignore
.gitignore
 
 
Bit_scores.ipynb
Bit_scores.ipynb
 
 
Commercial_monoclonal_antibodies.ipynb
Commercial_monoclonal_antibodies.ipynb
 
 
Comparing_with_and_without_sliding_window.ipynb
Comparing_with_and_without_sliding_window.ipynb
 
 
Data_cleanup.ipynb
Data_cleanup.ipynb
 
 
Feature analysis_pET_without_SWI.ipynb
Feature analysis_pET_without_SWI.ipynb
 
 
Feature analysis_with_SWI.ipynb
Feature analysis_with_SWI.ipynb
 
 
README.md
README.md
 
 
Random_sequences.ipynb
Random_sequences.ipynb
 
 
functions.py
functions.py
 
 

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Bikash K Bhandari, Paul P Gardner, Chun Shen Lim. (2020). Solubility-Weighted Index: fast and accurate prediction of protein solubility. Bioinformatics. DOI:10.1093/bioinformatics/btaa578

This repository contains the scripts and Jupyter notebooks to reproduce the results and figures of this preprint. The source code of TIsigner/SoDoPE web server is available here.

Run these Jupyter notebooks in the following order:

  • Data_cleanup
  • The dataset reqired for the cross-validation step can be found in result/cross_val

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