Collection of core classes to help with building structure- and chemistry-based feature datasets to train machine learning models to predict antimicrobial resistance.
This is under active development and so is subject to change with no notice.
We will be making a series of jupyter-notebooks demonstrating how to use the classes available here.
- Volume
- Hydropathy scales: Kyte-Doolittle (paper) and WimleyWhite (paper)
- Molecular weight
- Isoelectric point
- DeepDDG: a more recent neural network that claims to outperform DUET, PopMusic etc. (paper and server). Can do all possible mutations in one job.
- Distances between mutated residues and any atom/group of atoms of interest. Uses MDAnalysis (paper1 and paper2).
- Secondary structure: DSSP (do not anticipate much difference to STRIDE)
- Protein stability:
PWF, 9 May 2023