{ "info": { "author": "Kumaran Baskaran", "author_email": "kbaskaran@bmrb.wisc.edu", "bugtrack_url": null, "classifiers": [], "description": "# PyBMRB\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/uwbmrb/PyBMRB/master?filepath=pybmrb%2Fexamples) Click the launcher to try PyBMRB on a live Jupyter Notebook.\n## Biological Magnetic Resonance data Bank (BMRB)\n[BMRB](http://www.bmrb.wisc.edu/) collects, annotates, archives, and disseminates (worldwide in the public domain)\n the important spectral and quantitative data derived from NMR spectroscopic \n investigations of biological macromolecules and metabolites. The goal is to empower \n scientists in their analysis of the structure, dynamics, and chemistry of biological \n systems and to support further development of the field of \n biomolecular NMR spectroscopy. \n\n BMRB maintains its data in a relational data base and also as flat files in [NMR-STAR](https://doi.org/10.1007/s10858-018-0220-3)\n format. It also provides data access via API and provide software tools to handle NMR-STAR files. \n [PyNMRSTAR](https://github.com/uwbmrb/PyNMRSTAR) is one such tool to facilitate read, write and parse\n NMR-STAR files. [PyBMRB](https://github.com/uwbmrb/PyBMRB) enhances the usability of BMRB data \n and data access and visualization of chemical shift data from BMRB. \n\n ## Data visualization\n\nPyBMRB uses modern python visualization tool [plotly](https://plot.ly/python/) for \nits visualization. It features chemical shift histograms of \nstandard amino acids from BMRB data base and N15-HSQC peak position simulation\nfor any given BMRB entry (or) list of BMRB entries (or) from a local assigned chemical \nshift list in NMR-STAR format. Random coil N15-HSQC simulation based on BMRB \ndatabase statistics is also included. \n\nThis library can produce stand alone interactive visualizations as an html file or it can \nbe used in a [Jupyter Notebook](https://jupyter.org/). Example notebooks can be found \n[here](https://github.com/uwbmrb/PyBMRB/tree/master/pybmrb/examples)\n\n### Installation\nPyBMRB is available in [Python Package Index (PIP)](https://pypi.org/project/pybmrb/). It can be easily \ninstalled with the following command\n>pip install pybmrb\n\nTo install from the source, clone or download the source and go to the folder which contains 'setup.py' \nand run the following command\n>pip install .\n\nIf necessary use 'sudo'. \n### Chemical shift Histogram\nBMRB serves Nuclear Magnetic Resonance(NMR) community by providing \nhigh quality curated chemical shift data of various biologically important \nmacro molecules like proteins and nucleic acids and small molecules like ligands , co-factors,\nsmall peptides and metabolites. Chemical shift distribution of an atom in an amino acid or \nnucleic acid helps us to understand its diverse chemical environment.\nChemical shift histograms from a database like BMRB will help\nus to understand the biophysical nature bio-molecules and provides a priory \nprobabilities for resonance assignments.\n\n#### Conditional Histogram\nChemical shift assignment in a multi-dimensional NMR experiment is \na biggest challenge. PyBMRB could help the process by generating \nconditional histogram. If partial assignments of a particular \namnio acid is available, then PyBMRB could estimate the chemical shift\ndistribution of an unassigned atom in that amino acid only from those entries\n whose chemical shifts are similar to the given list for that \n particular amino acid. \n\n\n#### Examples in Notebook\nChemical shift histogram [examples notebook](https://github.com/uwbmrb/PyBMRB/tree/master/pybmrb/examples/histogram.ipynb) \ncan be found [here](https://github.com/uwbmrb/PyBMRB/tree/master/pybmrb/examples/histogram.ipynb). \nThe output might look like [this](https://htmlpreview.github.io/?https://github.com/uwbmrb/PyBMRB/blob/master/pybmrb/examples/histogram.html) \n\n### N15-HSQC Simulation.\n\nN15-HSQC is one of the most common and useful NMR experiment.\nIt carries signatures of folded state, ligand interaction and experimental environments \nlike pH and temperature. BMRB contains thousands of assigned chemical shifts\nof various bio-molecules. PyBMRB make use of this data to simulate N15-HSQC \npeak positions for any BMRB entry. Its also possible to compare N15-HSQC \nspectra of different entries or BMRB entries with user generated\n chemical shift information in NMR-STAR format. \n\n #### N15-HSQC spectrum from sequence. \n As an experiment, BMRB developed tools to simulate random coil\n N15-HSQC spectrum for a given protein sequence using \n database statistics. 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