Skip to content

Latest commit

 

History

History
58 lines (40 loc) · 2.36 KB

README.md

File metadata and controls

58 lines (40 loc) · 2.36 KB
Raw

Classification of Contacts in Protein Structures

Structural Bioinformatics Project, Data Science @ UniPD, A.Y. 2022/23.

Authors: Tanner Graves, Marco Uderzo, Nour Alhousseini, Hazeezat Adebimpe Adebayo.

image

Project Context

Residue Interaction Networks are derived from protein structures based on geometrical and physico-chemical properties of the amino acids. RING is a software that takes a PDB file as input and returns the list of contacts (residue-residue pairs) and their types in a protein structure. RING contact types include:

  • Hydrogen bonds (HBOND)
  • Van der Waals interactions (VDW)
  • Disulfide bridges (SBOND)
  • Salt bridges (IONIC)
  • π-π stacking (PIPISTACK)
  • π-cation (PICATION)
  • Unclassified contacts

This project aims at predicting the RING classification of a contact based on supervised methods, rather than geometrical constraints. The software is able to calculate the propensity (or probability) of a contact belonging to each of the different contact types defined by RING, starting from the protein structure.

Repository Overview and Files

The repository is organized as follows (only relevant files and folders mentioned):

↳ data: folder containing all data.
        ↳ features_ring: folder containing all of the training data in .tsv format.
                        🗎 *.tsv
        ↳ output: output folder containing all output files of the software.

        🗎 atchley.tsv: utility file to compute features.
        🗎 ramachandran.dat: utility file to compute features.

↳ docs: folder containing the software documentation.
        🗎 documentation.md: software documentation.

↳ model: folder containing the trained model, loaded at prediction time.
        🗎 model.keras: keras model file.
      
🗎 contact_net.py: main python script. run for inference or training, specifying arguments.
🗎 model.py: python script containing the untrained model.
🗎 calc-features.py: python script to compute the proteins features.
🗎 configuration.json: JSON file containing configuration settings.
🗎 contacts_classification_keras.ipynb: Jupyter notebook of ContactNet.
🗎 project_report.pdf: final project report for the Structural Bioinformatics course.