The document provides a list of regular expression patterns that could be used to scan protein sequences for prosite patterns. It begins by showing example consensus patterns for protein domains and motifs. It then lists 20 regular expression patterns translated from prosite consensus patterns that could be used to scan protein sequences and look for matches. The document concludes by providing an example Python code snippet to scan sequences for the given prosite patterns using regular expressions.

2. FBW
07-11-2017
Wim Van Criekinge

3. Google Calendar

4. BPC 2017

5. Recap
if condition:
statements
[elif condition:
statements] ...
else:
statements
while condition:
statements
for var in sequence:
statements
break
continue
Strings
REGULAR EXPRESSIONS

6. Devhints.io

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())

9. >SEQ1
MGNLFENCTHRYSFEYIYENCTNTTNQCGLIRNVASSIDVFHWLDVYISTTIFVISGILNFYCLFIALYT
YYFLDNETRKHYVFVLSRFLSSILVIISLLVLESTLFSESLSPTFAYYAVAFSIYDFSMDTLFFSYIMIS
LITYFGVVHYNFYRRHVSLRSLYIILISMWTFSLAIAIPLGLYEAASNSQGPIKCDLSYCGKVVEWITCS
LQGCDSFYNANELLVQSIISSVETLVGSLVFLTDPLINIFFDKNISKMVKLQLTLGKWFIALYRFLFQMT
NIFENCSTHYSFEKNLQKCVNASNPCQLLQKMNTAHSLMIWMGFYIPSAMCFLAVLVDTYCLLVTISILK
SLKKQSRKQYIFGRANIIGEHNDYVVVRLSAAILIALCIIIIQSTYFIDIPFRDTFAFFAVLFIIYDFSILSLLGSFTGVA
M MTYFGVMRPLVYRDKFTLKTIYIIAFAIVLFSVCVAIPFGLFQAADEIDGPIKCDSESCELIVKWLLFCI
ACLILMGCTGTLLFVTVSLHWHSYKSKKMGNVSSSAFNHGKSRLTWTTTILVILCCVELIPTGLLAAFGK
SESISDDCYDFYNANSLIFPAIVSSLETFLGSITFLLDPIINFSFDKRISKVFSSQVSMFSIFFCGKR
>SEQ2
MLDDRARMEA AKKEKVEQIL AEFQLQEEDL KKVMRRMQKE MDRGLRLETH EEASVKMLPT YVRSTPEGSE
VGDFLSLDLG GTNFRVMLVK VGEGEEGQWS VKTKHQMYSI PEDAMTGTAE MLFDYISECI SDFLDKHQMK
HKKLPLGFTF SFPVRHEDID KGILLNWTKG FKASGAEGNN VVGLLRDAIK RRGDFEMDVV AMVNDTVATM
ISCYYEDHQC EVGMIVGTGC NACYMEEMQN VELVEGDEGR MCVNTEWGAF GDSGELDEFL LEYDRLVDES
SANPGQQLYE KLIGGKYMGE LVRLVLLRLV DENLLFHGEA SEQLRTRGAF ETRFVSQVES DTGDRKQIYN
ILSTLGLRPS TTDCDIVRRA CESVSTRAAH MCSAGLAGVI NRMRESRSED VMRITVGVDG SVYKLHPSFK
ERFHASVRRL TPSCEITFIE SEEGSGRGAA LVSAVACKKA CMLGQ
>SEQ3
MESDSFEDFLKGEDFSNYSYSSDLPPFLLDAAPCEPESLEINKYFVVIIYVLVFLLSLLGNSLVMLVILY
SRVGRSGRDNVIGDHVDYVTDVYLLNLALADLLFALTLPIWAASKVTGWIFGTFLCKVVSLLKEVNFYSGILLLA
CISVDRY
LAIVHATRTLTQKRYLVKFICLSIWGLSLLLALPVLIFRKTIYPPYVSPVCYEDMGNNTANWRMLLRILP
QSFGFIVPLLIMLFCYGFTLRTLFKAHMGQKHRAMRVIFAVVLIFLLCWLPYNLVLLADTLMRTWVIQET
CERRNDIDRALEATEILGILGRVNLIGEHWDYHSCLNPLIYAFIGQKFRHGLLKILAIHGLISKDSLPKDSRPSFVGS
SSGH TSTTL
>SEQ4
MEANFQQAVK KLVNDFEYPT ESLREAVKEF DELRQKGLQK NGEVLAMAPA FISTLPTGAE TGDFLALDFG
GTNLRVCWIQ LLGDGKYEMK HSKSVLPREC VRNESVKPII DFMSDHVELF IKEHFPSKFG CPEEEYLPMG
FTFSYPANQV SITESYLLRW TKGLNIPEAI NKDFAQFLTE GFKARNLPIR IEAVINDTVG TLVTRAYTSK
ESDTFMGIIF GTGTNGAYVE QMNQIPKLAG KCTGDHMLIN MEWGATDFSC LHSTRYDLLL DHDTPNAGRQ
IFEKRVGGMY LGELFRRALF HLIKVYNFNE GIFPPSITDA WSLETSVLSR MMVERSAENV RNVLSTFKFR
FRSDEEALYL WDAAHAIGRR AARMSAVPIA SLYLSTGRAG KKSDVGVDGS LVEHYPHFVD MLREALRELI
GDNEKLISIG IAKDGSGIGA ALCALQAVKE KKGLA MEANFQQAVK KLVNDFEYPT ESLREAVKEF
DELRQKGLQK NGEVLAMAPA FISTLPTGAE TGDFLALDFG GTNLRVCWIQ LLGDGKYEMK HSKSVLPREC
VRNESVKPII DFMSDHVELF IKEHFPSKFG CPEEEYLPMG FTFSYPANQV SITESYLLRW TKGLNIPEAI
NKDFAQFLTE GFKARNLPIR IEAVINDTVG TLVTRAYTSK ESDTFMGIIF GTGTNGAYVE QMNQIPKLAG
KCTGDHMLIN MEWGATDFSC LHSTRYDLLL DHDTPNAGRQ IFEKRVGGMY LGELFRRALF HLIKVYNFNE
GIFPPSITDA WSLETSVLSR MMVERSAENV RNVLSTFKFR FRSDEEALYL WDAAHAIGRR AARMSAVPIA
SLYLSTGRAG KKSDVGVDGS LVEHYPHFVD MLREALRELI GDNEKLISIG IAKDGSGIGA ALCALQAVKE
KKGLA
sequences

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?'

13. https://prosite.expasy.org

14. • Where to put the files ?

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

17. Getting the database
FASTA: Uniprot_sprot.fasta – 268Mb
TEXT: Uniprot_sprot.dat – zipped (560
Mb) unzipped (3Gb)
http://www.ebi.ac.uk/uniprot/download-center