recipe b2btools

The bio2Byte software suite to predict protein biophysical properties

Homepage:

https://bio2byte.be/b2btools

Documentation:

https://pypi.org/project/b2bTools/

Developer docs:

http://bitbucket.org/bio2byte/b2btools_releases

License:

GPL3 / GNU General Public License v3 (GPLv3)

Recipe:

/b2btools/meta.yaml

Links:

doi: 10.48550/arXiv.2405.02136, biotools: b2btools

This package provides you with structural predictions for protein sequences made by the Bio2Byte group which researches the relation between protein sequence and biophysical behavior.

List of available predictors: 1. Dynamine: Fast predictor of protein backbone dynamics using only sequence information as input. The version here also predicts side-chain dynamics and secondary structure predictors using the same principle. 2. Disomine: Predicts protein disorder with recurrent neural networks not directly from the amino acid sequence, but instead from more generic predictions of key biophysical properties, here protein dynamics, secondary structure, and early folding. 3. EfoldMine: Predicts from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. 4. AgMata: Single-sequence-based predictor of protein regions that are likely to cause beta-aggregation. 5. PSPer: PSP (Phase Separating Protein) predicts whether a protein is likely to phase-separate with a particular mechanism involving RNA interacts (FUS-like proteins). 6. ShiftCrypt: Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

package b2btools

versions:

3.0.7-0,  3.0.6-0,  3.0.5-0,  3.0.4-0

depends biopython:

>=1.80,<2

depends hmmer:

depends libgcc-ng:

>=12

depends libstdcxx-ng:

>=12

depends matplotlib-base:

>=3.5.3,<3.6

depends numpy:

1.24.4

depends pandas:

>=1.5.3,<1.6

depends pomegranate:

>=0.14.8,<=0.14.9

depends python:

>=3.10,<3.11.0a0

depends python_abi:

3.10.* *_cp310

depends pytorch:

>=1.11.0,<=1.13.1

depends requests:

>=2.0

depends scikit-learn:

1.0.2

depends scipy:

1.10.1

depends t-coffee:

depends urllib3:

>=1.26.6,<1.27

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install b2btools

and update with::

   mamba update b2btools

To create a new environment, run:

mamba create --name myenvname b2btools

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/b2btools:<tag>

(see `b2btools/tags`_ for valid values for ``<tag>``)

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