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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

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789344691
Length 360 pages
Edition 2nd Edition
Languages
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Author (1):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
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Toc

Table of Contents (12) Chapters Close

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. Bioinformatics Pipelines 9. Python for Big Genomics Datasets 10. Other Topics in Bioinformatics 11. Advanced NGS Processing

Computing molecular distances on a PDB file


Here, we will find atoms closer to three zincs in the 1TUP model. We will consider several distances to these zincs. We will take this opportunity to discuss the performance of algorithms.

Getting ready

You can find this content in the Chapter07/Distance.ipynb Notebook file. Take a look at the Introducing Bio.PDB recipe in this chapter for more information.

How to do it...

Take a look at the following steps:

  1. Let's load our model, as follows:
from Bio import PDB
repository = PDB.PDBList()
parser = PDB.PDBParser()
repository.retrieve_pdb_file('1TUP', pdir='.', file_format='pdb')
p53_1tup = parser.get_structure('P 53', 'pdb1tup.ent')
  1. We will now get our zincs, against which we will perform later comparisons:
zns = []for atom in p53_1tup.get_atoms():
if atom.element == 'ZN':
zns.append(atom)
for zn in zns:
    print(zn, zn.coord)

You should see three zinc atoms.

  1. Now, let's define a function to get the distance between one atom and a set of other atoms, as follows...
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