Constructs core features table for the application to machine learning models
Project description
sbml-core
Collection of core classes and functions for structure-based machine learning to predict antimicrobial resistance.
This is a pre-release alpha version - it may not be fully functional for your requirements and it is also subject to change with no notice!
See notebooks walkthrough.ipynb and addition_methods_walkthrough.ipynb for quick user tutorials.
Included features
Changes in Amino Acid Properties
- Volume
- Hydropathy scales: Kyte-Doolittle (paper) and WimleyWhite (paper)
- Molecular weight
- Isoelectric point
Secondary structure
Solvent accessible surface areas
Likelihood of changes in protein function
Effect of mutation on protein stability
- DeepDDG: a more recent neural network that claims to outperform DUET, PopMusic etc. (paper and server). Can do all possible mutations in one job.
Structural distances
- Distances between mutated residues and any atom/group of atoms of interest. Uses MDAnalysis (paper1 and paper2).
To potentially include at a later stage
- Secondary structure: DSSP (do not anticipate much difference to STRIDE)
- Protein stability:
Project details
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sbmlcore-0.1.4.tar.gz
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sbmlcore-0.1.4-py3-none-any.whl
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