The document discusses various topics related to analyzing protein sequences using Python and Biopython. It provides examples of using Biopython to parse sequence data from UniProt, calculate lengths and translations of sequences. It also discusses analyzing properties of sequences like molecular weight, isoelectric point, transmembrane regions, and comparing sequences to find conserved motifs. Finally, it introduces hydropathy indices and tools for predicting properties like transmembrane helices from primary sequences.

2. FBW
20-11-2018
Wim Van Criekinge

3. Google Calendar

5. Control Structures
if condition:
statements
[elif condition:
statements] ...
else:
statements
while condition:
statements
for var in sequence:
statements
break
continue

6. Lists
• Flexible arrays, not Lisp-like linked
lists
• a = [99, "bottles of beer", ["on", "the",
"wall"]]
• Same operators as for strings
• a+b, a*3, a[0], a[-1], a[1:], len(a)
• Item and slice assignment
• a[0] = 98
• a[1:2] = ["bottles", "of", "beer"]
-> [98, "bottles", "of", "beer", ["on", "the", "wall"]]
• del a[-1] # -> [98, "bottles", "of", "beer"]

7. Dictionaries
• Hash tables, "associative arrays"
• d = {"duck": "eend", "water": "water"}
• Lookup:
• d["duck"] -> "eend"
• d["back"] # raises KeyError exception
• Delete, insert, overwrite:
• del d["water"] # {"duck": "eend", "back": "rug"}
• d["back"] = "rug" # {"duck": "eend", "back":
"rug"}
• d["duck"] = "duik" # {"duck": "duik", "back":
"rug"}

9. Prelude
• Erdos
• Gaussian - towards random
DNAwalks
• AAindex parser

10. Erdos.py

12. Gaussian.py

13. Prelude
• Erdos
• Gaussian - towards random
DNAwalks

14. import Bio
print(Bio.__version__)
from Bio.Seq import Seq
my_seq = Seq('CATGTAGACTAG')
#print out some details about it
print ('seq %s is %i bases long' % (my_seq, len(my_seq)))
print ('reverse complement is %s' %
my_seq.reverse_complement())
print ('protein translation is %s' % my_seq.translate())

15. from Bio import SeqIO
c=0
handle = open(r'/Users/wvcrieki/Downloads/uniprot_sprot.dat')
for seq_rec in SeqIO.parse(handle, "swiss"):
print (seq_rec.id)
print (seq_rec.seq)
print (len(seq_rec))
c+=1
if c>5:
break

16. BioPython
• Make a histogram of the MW (in kDa) of all proteins in
Swiss-Prot
• Find the most basic and most acidic protein in Swiss-Prot?
• Biological relevance of the results ?
From AAIndex
H ZIMJ680104
D Isoelectric point (Zimmerman et al., 1968)
R LIT:2004109b PMID:5700434
A Zimmerman, J.M., Eliezer, N. and Simha, R.
T The characterization of amino acid sequences in proteins by
statistical
methods
J J. Theor. Biol. 21, 170-201 (1968)
C KLEP840101 0.941 FAUJ880111 0.813 FINA910103 0.805
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
6.00 10.76 5.41 2.77 5.05 5.65 3.22 5.97 7.59 6.02
5.98 9.74 5.74 5.48 6.30 5.68 5.66 5.89 5.66 5.96

18. Find_Most_Basic_Protein.py

19. Find_Most_Basic_Protein.py

21. Extra Questions
• How many human proteins in Swiss Prot ?
• What is the longest human protein ? The shortest ?
• Calculate for all human proteins their MW and pI, display as
two histograms (2D scatter ?)
• How many human proteins have “cancer” in their description?
• Which genes has the highest number of SNPs/somatic
mutations (COSMIC)
• How many human DNA-repair enzymes are represented in
Swiss Prot (using description / GO)?
• List proteins that only contain alpha-helices based on the
Chou-Fasman algorithm
• List proteins based on the number of predicted
transmembrane regions (Kyte-Doollittle)

23. Biopython AAindex ? Dictionary
Hydrophobicity = {A:6.00,L:5.98,R:10.76,K:9.74,N:5.41,M:5.74,D:2.77,F:5.48,
C:5.05,P:6.30,Q:5.65,S:5.68,E:3.22,T:5.66,G:5.97,W:5.89,
H:7.59,Y:5.66,I:6.02,V:5.96}
from Bio import SeqIO
c=0
handle = open(r'/Users/wvcrieki/Downloads/uniprot_sprot.dat')
for seq_rec in SeqIO.parse(handle, "swiss"):
print (seq_rec.id)
print (repr(seq_rec.seq))
print (len(seq_rec))
c+=1
if c>5:
break

24. Extra Questions (2)
• How many human proteins in Swiss Prot ?
• What is the longest human protein ? The shortest ?
• Calculate for all human proteins their MW and pI, display as
two histograms (2D scatter ?)
• How many human proteins have “cancer” in their description?
• Which genes has the highest number of SNPs/somatic
mutations (COSMIC)
• How many human DNA-repair enzymes are represented in
Swiss Prot (using description / GO)?
• List proteins that only contain alpha-helices based on the
Chou-Fasman algorithm
• List proteins based on the number of predicted
transmembrane regions (Kyte-Doollittle)

25. Primary sequence reveals important clues about a protein
DnaG E. coli ...EPNRLLVVEGYMDVVAL...
DnaG S. typ ...EPQRLLVVEGYMDVVAL...
DnaG B. subt ...KQERAVLFEGFADVYTA...
gp4 T3 ...GGKKIVVTEGEIDMLTV...
gp4 T7 ...GGKKIVVTEGEIDALTV...
: *: :: * * : :
small hydrophobic
large hydrophobic
polar
positive charge
negative charge
• Evolution conserves amino acids that are important to protein
structure and function across species. Sequence comparison of
multiple “homologs” of a particular protein reveals highly
conserved regions that are important for function.
• Clusters of conserved residues are called “motifs” -- motifs
carry out a particular function or form a particular structure
that is important for the conserved protein.
motif

26.  The hydropathy index of an amino acid is a number
representing the hydrophobic or hydrophilic properties of its
side-chain.
 It was proposed by Jack Kyte and Russell Doolittle in 1982.
 The larger the number is, the more hydrophobic the amino
acid. The most hydrophobic amino acids are isoleucine (4.5)
and valine (4.2). The most hydrophilic ones are arginine (-4.5)
and lysine (-3.9).
 This is very important in protein structure; hydrophobic
amino acids tend to be internal in the protein 3D structure,
while hydrophilic amino acids are more commonly found
towards the protein surface.
Hydropathy index of amino acids

27. (http://gcat.davidson.edu/DGPB/kd/kyte-doolittle.htm)Kyte Doolittle Hydropathy Plot

28. Possible transmembrane fragment

29. Window size – 9, strong negative peaks indicate possible surface regions
Surface region of a protein

30. Prediction of transmembrane helices in proteins
(TMHMM)

31. 5-hydroxytryptamine receptor 2A (Mus musculus)

32. 5-hydroxytryptamine receptor 2 (Grapical output)