sbmlcore 0.2.8

Description:

sbmlcore 0.2.8

sbmlcore
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.
Included features
Changes in Amino Acid Properties

Volume
Hydropathy scales: Kyte-Doolittle (paper) and WimleyWhite (paper)
Molecular weight
Isoelectric point

Secondary structure

STRIDE (website and paper)

Solvent accessible surface areas

FreeSASA (paper)
STRIDE (website and paper)

Likelihood of changes in protein function

SNAP2 (server and paper)

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:

StabilityPredict. Online metapredictor, single amino acid at a time. Josh used in the pncA paper but had to contact them directly to run the entirity of PncA. (paper)
DynaMUT. Also claims to outperform DUET etc. (paper). Can process a list of specified mutations in one job. (server)



PWF, 9 May 2023