Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology

Arrow left icon
Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781782175117
Length 306 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Bioinformatics with Python Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Python and the Surrounding Software Ecology FREE CHAPTER 2. Next-generation Sequencing 3. Working with Genomes 4. Population Genetics 5. Population Genetics Simulation 6. Phylogenetics 7. Using the Protein Data Bank 8. Other Topics in Bioinformatics 9. Python for Big Genomics Datasets Index

Parsing mmCIF files using Biopython


The mmCIF file format is probably the future. Biopython does not have yet full functionality to work with it, but we will take a look at what is here now.

Getting ready

As Bio.PDB is not able to automatically download mmCIF files, you need to get your protein file and rename it as 1tup.cif. This can be found at https://github.com/tiagoantao/bioinf-python/blob/master/notebooks/Datasets.ipynb under the 1TUP.cif name.

You can find this content in the 06_Prot/mmCIF.ipynb notebook.

How to do it...

Take a look at the following steps:

  1. Let's parse the file. We just use the mmCIF parser instead of the PDB parser:

    from __future__ import print_function
    from Bio import PDB
    parser = PDB.MMCIFParser()
    p53_1tup = parser.get_structure('P53', '1tup.cif')
  2. Let's inspect the following chains:

    def describe_model(name, pdb):
        print()
        for model in p53_1tup:
            for chain in model:
                print('%s - Chain: %s. Number of residues: %d. Number of atoms: %d.' %
             ...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime
Visually different images