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https://github.com/choderalab/missense-kinase-toolkit

A package to facilitate structure- and sequence-based ML for clinically relevant missense human kinase property prediction

https://github.com/choderalab/missense-kinase-toolkit

Science Score: 49.0%

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Last synced: 6 months ago · JSON representation

Repository

A package to facilitate structure- and sequence-based ML for clinically relevant missense human kinase property prediction

Basic Info
  • Host: GitHub
  • Owner: choderalab
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 264 MB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 0
  • Open Issues: 1
  • Releases: 1
Created about 2 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Code of conduct

README.md

missense-kinase-toolkit (mkt)

DOI codecov Documentation Status
pre-commit.ci status schema-ci databases-ci
Streamlit App

Intro

mkt is a Python package to generate sequence and structure-based representations for human kinase property prediction. While our application uses this data to predict the impact of clinically observed missense mutations on human kinase activity, we note that many of these tools can be used more extensively to characterize wild-type human kinases and any mutant forms. The databases sub-package can be used to query the APIs of a variety of protein resources that are not exclusive to either kinases or humans, including UniProt, Pfam, and cBioPortal.

Additional documentation can be found here.

Getting started

mkt is structured as a monorepo with sub-packages and directories described below for specific tasks.

| Subpackages | Description | | :-: | :- | | app | Streamlit app to visualize data contained in harmonized Pydantic models | | schema | Harmonized and pre-processed sequence and structure data along with Pydantic models to load, query, and validate this data | | databases | Package containing API clients and scrapers to collect and harmonize kinase data from various sources and generate schema objects | | ml | In-progress package to build machine learning models to predict kinase properties | | experiments | In-progress package to analyze experimental results for project |

Sub-packages can be installed directly from Github via pip using the following: pip install git+https://github.com/choderalab/missense-kinase-toolkit.git#subdirectory=missense_kinase_toolkit/<sub-package directory>

Copyright

Copyright (c) 2024, Jess White

Acknowledgements

We would like to express gratitude to the creators of the following resources on which we heavily rely: + UniProt + Pfam + cBioPortal + KinHub + KLIFS + KinCore + KinoML

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.

Owner

  • Name: Chodera lab // Memorial Sloan Kettering Cancer Center
  • Login: choderalab
  • Kind: organization
  • Email: john.chodera@choderalab.org
  • Location: Memorial Sloan-Kettering Cancer Center, Manhattan, NY

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  • jessicaw9910 (122)
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dependencies (14)

Dependencies

.github/workflows/CI.yaml actions
  • actions/checkout v3 composite
  • codecov/codecov-action v1 composite
  • mamba-org/provision-with-micromamba main composite
poetry.lock pypi
  • appdirs 1.4.4
  • attrs 23.2.0
  • black 24.2.0
  • cattrs 23.2.3
  • certifi 2024.2.2
  • charset-normalizer 3.3.2
  • click 8.1.7
  • colorama 0.4.6
  • exceptiongroup 1.2.0
  • flake8 7.0.0
  • idna 3.6
  • iniconfig 2.0.0
  • mccabe 0.7.0
  • mypy-extensions 1.0.0
  • numpy 1.26.4
  • packaging 23.2
  • pandas 2.2.0
  • pathspec 0.12.1
  • platformdirs 4.2.0
  • pluggy 1.4.0
  • pycodestyle 2.11.1
  • pydantic 1.10.14
  • pyflakes 3.2.0
  • pytest 8.0.1
  • pytest-runner 6.0.1
  • python-dateutil 2.8.2
  • pytz 2024.1
  • requests 2.31.0
  • requests-cache 0.9.8
  • setuptools 69.1.0
  • six 1.16.0
  • tomli 2.0.1
  • tqdm 4.64.0
  • typing-extensions 4.9.0
  • tzdata 2024.1
  • url-normalize 1.4.3
  • urllib3 2.2.1
pyproject.toml pypi
  • black ^24.2.0 develop
  • flake8 ^7.0.0 develop
  • pandas >=2,<3
  • pydantic >=1.10,<2
  • python ^3.9
  • requests >=2.28.1,<3
  • requests-cache >=0.9.7,<1
  • setuptools ^69.1.0
  • tqdm 4.64.0
  • pytest ^8.0.1 test
  • pytest-runner ^6.0.1 test