7. Towards a protein prosite scanner
N-{P}-[ST]-{P}.
[RK](2)-x-[ST].
[ST]-x-[RK].
[ST]-x(2)-[DE].
[RK]-x(2,3)-[DE]-x(2,3)-Y.
G-{EDRKHPFYW}-x(2)-[STAGCN]-{P}.
x-G-[RK]-[RK].
C-x-[DN]-x(4)-[FY]-x-C-x-C.
E-x(2)-[ERK]-E-x-C-x(6)-[EDR]-x(10,11)-[FYA]-[YW].
[DEQGSTALMKRH]-[LIVMFYSTAC]-[GNQ]-[LIVMFYAG]-[DNEKHS]-S-[LIVMST]-{PCFY}-[STAGCPQLIVMF]-[LIVMATN]-[DENQGTAKRHLM]-[LIVMWSTA]-
[LIVGSTACR]-{LPIY}-{VY}-[LIVMFA].
[KRHQSA]-[DENQ]-E-L>.
R-G-D.
[AG]-x(4)-G-K-[ST].
D-{W}-[DNS]-{ILVFYW}-[DENSTG]-[DNQGHRK]-{GP}-[LIVMC]-[DENQSTAGC]-x(2)-[DE]-[LIVMFYW].
[EQ]-{LNYH}-x-[ATV]-[FY]-{LDAM}-{T}-W-{PG}-N.
[LIVM]-x-[SGNL]-[LIVMN]-[DAGHENRS]-[SAGPNVT]-x-[DNEAG]-[LIVM]-x-[DEAGQ]-x(4)-[LIVM]-x-[LM]-[SAG]-[LIVM]-[LIVMT]-[WS]-x(0,1)-[LIVM](2).
[FY]-C-[RH]-[NS]-x(7,8)-[WY]-C.
C-x-C-x(2)-{V}-x(2)-G-{C}-x-C.
C-x(2)-P-F-x-[FYWIV]-x(7)-C-x(8,10)-W-C-x(4)-[DNSR]-[FYW]-x(3,5)-[FYW]-x-[FYWI]-C.
[LIFAT]-{IL}-x(2)-W-x(2,3)-[PE]-x-{VF}-[LIVMFY]-[DENQS]-[STA]-[AV]-[LIVMFY].
[KRH]-x(2)-C-x-[FYPSTV]-x(3,4)-[ST]-x(3)-C-x(4)-C-C-[FYWH].
C-x(4,5)-C-C-S-x(2)-G-x-C-G-x(3,4)-[FYW]-C.
[LIVMFYG]-[ASLVR]-x(2)-[LIVMSTACN]-x-[LIVM]-{Y}-x(2)-{L}-[LIV]-[RKNQESTAIY]-[LIVFSTNKH]-W-[FYVC]-x-[NDQTAH]-x(5)-[RKNAIMW].
C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-H.
L-x(6)-L-x(6)-L-x(6)-L.
C-x(2)-C-x(1,2)-[DENAVSPHKQT]-x(5,6)-[HNY]-[FY]-x(4)-C-x(2)-C-x(2)-F(2)-x-R.
[LIVMFE]-[FY]-P-W-M-[KRQTA].
L-M-A-[EQ]-G-L-Y-N.
IRED_1R-P-C-x(11)-C-V-S.
[RKQ]-R-[LIM]-x-[LF]-G-[LIVMFY]-x-Q-x-[DNQ]-V-G.
[KR]-x(1,3)-[RKSAQ]-N-{VL}-x-[SAQ](2)-{L}-[RKTAENQ]-x-R-{S}-[RK].
[LIVMF](2)-D-E-A-D-[RKEN]-x-[LIVMFYGSTN].
[KRQ]-[LIVMA]-x(2)-[GSTALIV]-{FYWPGDN}-x(2)-[LIVMSA]-x(4,9)-[LIVMF]-x-{PLH}-[LIVMSTA]-[GSTACIL]-{GPK}-{F}-x-[GANQRF]-[LIVMFY]-x(4,5)-[LFY]-x(3)-
[FYIVA]-{FYWHCM}-{PGVI}-x(2)-[GSADENQKR]-x-[NSTAPKL]-[PARL].
Scan for the following prosite patterns in your 4 sequences
Hint: translate the patters to regexes and then scan
8. reuse galacto.py in github
Consensus_pattern="G-R-x-N-[LIV]-I-G-[DE]-H-x-D-Y"
pattern=Consensus_pattern.replace("-","")
pattern=pattern.replace("x","[A-Z]")
#print(pattern)
count=0
for s in sequences:
count=count+1
print ("searching seq",count)
s=s.replace(" ","")
matches = re.finditer(pattern,s)
for match in matches:
print (match.group(0),"from: ",match.start(),"to: ",match.end())
10. 10
Reading Files
name = open("filename")
– opens the given file for reading, and returns a file object
name.read() - file's entire contents as a string
name.readline() - next line from file as a string
name.readlines() - file's contents as a list of lines
– the lines from a file object can also be read using a for loop
>>> f = open("hours.txt")
>>> f.read()
'123 Susan 12.5 8.1 7.6 3.2n
456 Brad 4.0 11.6 6.5 2.7 12n
789 Jenn 8.0 8.0 8.0 8.0 7.5n'
11. 11
File Input Template
• A template for reading files in Python:
name = open("filename")
for line in name:
statements
>>> input = open("hours.txt")
>>> for line in input:
... print(line.strip()) # strip() removes n
123 Susan 12.5 8.1 7.6 3.2
456 Brad 4.0 11.6 6.5 2.7 12
789 Jenn 8.0 8.0 8.0 8.0 7.5
12. 12
Writing Files
name = open("filename", "w")
name = open("filename", "a")
– opens file for write (deletes previous contents), or
– opens file for append (new data goes after previous data)
name.write(str) - writes the given string to the file
name.close() - saves file once writing is done
>>> out = open("output.txt", "w")
>>> out.write("Hello, world!n")
>>> out.write("How are you?")
>>> out.close()
>>> open("output.txt").read()
'Hello, world!nHow are you?'
15. Swiss-Knife.py
• Using a database as input ! Parse
the entire Swiss Prot collection
– How many entries are there ?
– Average Protein Length (in aa and
MW)
– Relative frequency of amino acids
• Compare to the ones used to construct
the PAM scoring matrixes from 1978 –
1991
16. Amino acid frequencies
1978 1991
L 0.085 0.091
A 0.087 0.077
G 0.089 0.074
S 0.070 0.069
V 0.065 0.066
E 0.050 0.062
T 0.058 0.059
K 0.081 0.059
I 0.037 0.053
D 0.047 0.052
R 0.041 0.051
P 0.051 0.051
N 0.040 0.043
Q 0.038 0.041
F 0.040 0.040
Y 0.030 0.032
M 0.015 0.024
H 0.034 0.023
C 0.033 0.020
W 0.010 0.014
Second step: Frequencies of Occurence