??af??e?a 1

1
S?G???S? ?????????O? ??O????O? S?? G?????O????
?O? T????O? ??? ???????O? ???? GRAM
???????O? ?apa??????p?????, ?.?., ?s??????,
?.?., ?p?????, ?.G. ?a? ?aµ?d?a?a?, S.?. ??µ?a?
???????a? ??tt???? ?a? ???f?s????, ?µ?µa
???????a?, ?a?ep?st?µ?? ??????, ????a 157 01
??S?GOG? ?? µeµß?a????? p??te??e? p??sd?d??? st?
µeµß???? t?? ?a?a?t???st???? t?? ?e?t?????e?. ??
p??te??e? a?t?? ?????? e??????? t?? e?s?d?-???d?
??s??? ?a? e??a? ???st?? ?? µetaf??e??, a?t??e?,
?a????a ? p????e?. ???e? ?e?t??????? ??
?p?d??e??, ????µa ? d?µ???? p??te??e?. ??
µeµß?a????? p??te??e? d?a??????ta? se
pe??fe?e?a???, se d?aµeµß?a????? ?a? se
??p?-s??de?µe?e?. ?? d?aµeµß?a????? p??te??e?,
ap?te???? pe??p?? t? 25-30 ???? t?? p?????
p??sd????sµ???? p??te?µ?t?? ?a? ep?te???? µ?a
se??? ap? ß???????? s?µa?t???? ?e?t?????e?. ?
p???p????ste?? ?µ?da a?t??, e??a? ??
a-e????e?de?? d?aµeµß?a????? p??te??e? ?? ?p??e?
d?ape????? t? ??p?d??? d?p??st?ß?da µe t? µ??f?
?d??f?ß?? a-e?????. G?a t?? p?????s? t?? d?µ??
?a? t?? t?p?????a? t?? d?aµeµß?a????? p??te????
????? a?apt???e? µ?a se??? ap? ?p?????st????
te??????, t?? ?p???? ? ?e?t?????a ßas??eta? se
?e??????? ???t?a, se stat?st???? a?a??se?? ? se
Hidden Markov ?odels.
?????a 2 a-e????e?de?? d?aµeµß?a????? p??te??e?

• MEMSAT3 ?????aµµa p?? efa?µ??e? µ?a µ???d? ??a
  t?? p?????s? t?? de?te??ta???? d?µ?? ?a?
  t?p?????a? t?? a-e????e?d?? d?aµeµß?a?????
  p??te???? ?a? ßas??eta? st?? a?a?????s?
  t?p???????? µ??t????. ? µ???d?? a?t?
  ???s?µ?p??e? stat?st????? p??a?e? p?? p???????ta?
  ap? ded?µ??a µeµß?a????? p??te???? ?a??
  p??sd????sµ???? ?a? ??a? a??????µ? d??aµ????
  p????aµµat?sµ?? p?? a?a??????e? t?? t?p?????a t??
  µeµß?a????? p??te????.
• HMMTOP4 ???a? µ?a µ???d?? p?? ßas???µe?? st?
  Hidden Markov Model ???e? p?????s? t?? ??s?? ?a?
  t?? ?pa???? t?? d?aµeµß?a????? e????? t??
  d?aµeµß?a????? p??te????. ?ts? µa? d??e? t? ??s?
  ( in or out ), t?? a???µ? ?a? t? a???ß?? s?µe??
  t?? d?aµeµß?a????? tµ?µ?t??.

?????a 1 ??µ? ??t?p?asµat???? µeµß?????
PrediSi1 Prediction of Signal peptides. ??
p????aµµa a?t? e??a? ??a ??? e??a?e?? ??a t??
p?????s? t?? signal-peptide st?? aµ????????
a???????e? t?? ßa?t????? ?a? t?? e??a???t????
???a??sµ??. ? µ???d?? p?? ???s?µ?p??e?ta?
ßas??eta? se position specific weight matrix, o
?p???? ?aµß??e? ?p???? t?? s????t?te? t??
aµ??????? se ??e? t?? p??te??e?. PRED-CLASS2
?????aµµa p?? ?at??????p??e? t?? p??te??e? µe
ß?s? t?? p????f???a p?? pe????eta? st?? aµ???????
t??? a???????a se transmembrane, fibrous,
globular ?a? mixed.
????? ??? ??T???? ??t??e?µe?? µe??t?? µa?
ap?t??esa? ?? d?aµeµß?a????? p??te??e? t??
es?te????? (??tta?????) µeµß????? t?? a???t????
a??? ?a? t?? ?et???? ?at? Gram ßa?t?????. G?a t??
a????s? 194 p????? p??sd????sµ???? ßa?t???a???
????d??µ?t?? (129 a???t????, 65 ?et???? ?at?
Gram) ???s?µ?p??????a? ?? a??????µ?? p?????s??
PRED-CLASS, MEMSAT, HMMTOP ?a? PrediSi.
???????S????
GRAM
MEMBRANE PROTEINS (gt2TM) (29795) 18
PROTEIN NUMBER
??S?S?? ?.?.
MEMBRANE PROTEINS (lt2TM) (11927) 7
GC / AT
NO MEMBRANE PROTEINS (127710) 75
NUMBER TM
?µ??????? ?at????pa (?.?.) s??a?t?se? t??
p?s?st?? t?? ?.?.
????µ?? ?? s??a?t?se? t?? p?s?st?? t?? a???µ??
t?? p??te????
GRAM
MEMBRANE PROTEINS (gt2TM) (62247) 15
VA / FI
????? GC / AT sta ????d??µata s??a?t?se? t??
????? VA / FI sta ??
MEMBRANE PROTEINS (lt2TM) (31457) 8
NO MEMBRANE PROTEINS (315724) 77
PROTEIN NUMBER
PROTEIN NUMBER
(VA)
A.A. ?O???????
?la GCX
Phe TTC/T
Ile ATA/C/T
Val GTX
5
LENGTH TM
LENGTH TM
?st???aµµa t?? µ????? t?? d?aµeµß?a????? tµ?µ?t??
s??a?t?se? t?? p?s?st?? t?? a???µ?? t??
p??te???? ??a t? HMMTOP ?a? MEMSAT
(FI)

• S?????S?
• ?p? t?? a????s? t?? ßa?t???a??? p??te?µ?t?? ?a?
  t?? pa?at???s? t?? ??af???? pa?ast?se??, ??a
  s?µa?t??? e???µa e??a? ? e?t?p?sµ?? µ?a?
  s?µa?t???? d?af???? st?? ?ata??µ? t?? p??te????
  µe d?af??et??? a???µ? d?aµeµß?a????? tµ?µ?t??.
  S???e???µ??a, ß?????e ?t? sta ?et??? ?at? Gram
  ßa?t???a, ?? p??te??e? µe 6 d?aµeµß?a???? tµ?µata
  e??a? s?µa?t??? pe??ss?te?e? ap? ?t? sta a???t???
  ?at? Gram. ?? ?e????? a?t? p??pe? ?a d?e?e????e?
  ?a??? 6 d?aµeµß?a???? tµ?µata apa?t??ta? se
  d??f??e? ???????e?e? d?aµeµß?a????? µetaf?????,
  ?p??
• - Aquaporins,
• - ATP binding cassette ABC transporters,
• - Protein-export membrane proteins (secD,
  secF),
• ?? ?p??e? s?et????ta? µe µetaf??? ??s??? d?aµ?s?
  t?? µeµß????? ap? ta ßa?t???a.
• Ge????te?e? d?af???? se ???e? pa?aµ?t???? (µ???
  d?aµeµß?a????? tµ?µ?t??, ?ata??µ?? aµ??????? ??p)
  de? pa?at??????a?.
• ?a?at??????e t????, µ?a d?af??et??? s?s??t?s?
  t?? ????? t?? aµ??????? (VA)/(FI) sta
  d?aµeµß?a???? tµ?µata, µe t? ???? t??
  ?????e?t?d??? (GC)/(AT) se ???????? t?
  ????d??µa.
• ?? d?af???? a?t?? p??pe? ?a e?e???????
  pe?a?t???, a??? p??a??? ?a ?fe????ta? st?
  d?af??et??? s?stas? t?? µeµß?a??? se ??p?d?a ?a?
  ?at ep??tas? se d?af???? st?? ?d??f?ß???t?ta t??
  µeµß?????.

p?s?st? ßa????? (V) ?a? a?a????? (?) s??a?t?se?
t?? p?s?st?? fa????a?a????? (F) ?a?
?s??e?????? (?)
??????G??F?? 1 Hiller K, Grote A, Scheer M, Munch
R, Jahn D. PrediSi prediction of signal peptides
and their cleavage positions. Nucleic Acids
Research, 2004 Vol.32, Web Server issue
W375-W379. 2 Pasquier C, Promponas VJ, Hamodrakas
SJ. PRED-CLASS Cascading Neural Networks for
Generalized Protein Classification and
Genome-Wide Applications. Proteins 2001
44361-369. 3 Jones DT, Taylor WR, Thornton JM. A
Model Recognition Approach to the Prediction of
All-Helical Membrane Protein Structure and
Topology Biochemistry 1994 33, 3038-3049. 4
Tusnady, G.E. and Simon, I. The HMMTOP
transmembrane topology prediction server.
Bioinformatics 2001 Vol.17 no.9, 849-850. 5
Stevens T, Arkin I. The effect of nucleotide bias
upon the composition and prediction of
transmembrane helices. Protein Science 2000
9505-511.