Difference between revisions of "Glucocerebrosidase sequence based prediction"

Revision as of 12:01, 4 June 2011

In this section several different sequence based predictions are applied to the sequence of glucocerebrosidase.

Secondary structure prediction

General

beta sheets and a α-helix as examples for secondary structure
Source: http://www.nature.com/horizon/proteinfolding/background/images/importance_f3.gif

The secondary structure of a protein describes the local conformation of its polypeptide chain which is limitated by the peptide bond and hydrogen bonding considerations. Two types of secondary structures are dominating the local conformations of a polypeptide chain: alpha (α) helix and beta (β) sheets. Some helix structures occuring less frequent are e.g. π-helices and 3,10-helices. Helices and sheets are stablized by hydrogen bond interactions between the backbone atoms of the corresponding residues and are the only regular secondary structural elements present in proteins. Loops or coils are examples for irregular structural elements. <ref>http://en.wikipedia.org/wiki/Biomolecular_structure#Secondary_structure</ref>

PSIPRED

PSIPRED is a method by David T. Jones, published 1999 in JMB with "Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices". PSIPRED works with a two-stage neural network that analyses the output of PSI-BLAST to predict the secondary structure of a protein. <ref>David T. Jones, Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices, JMB, 1999</ref>
As input, only the protein sequence in fasta format is needed.

Results

The online, as well as the local version of PSIPRED were applied to the sequence of glucocerebrosidase. Both runs resulted in different results which are compared to the secondary structure given in Uniprot. <ref>http://www.uniprot.org/uniprot/P04062</ref>. As one can see in the table below, the results differ a lot which may be traced back to the fact that different parameters were used in both versions. The comparison to the structure listed in Uniprot shows however, that the results of both versions differ in many regions considerably from the reference structure.

Conf:	`988898954488887622315999999999998641038968865325999649995388`
online:	`CCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHHCCCCCCCCCCCCCCCEEEEECCC`
Conf:	`987898955489988742200466888998986410038977877777863169974474`
local:	`CCCCCCCCCCCCCCCCCEEEEHHHHHHHHHHHHHHHCCCCCCCCCCCCCCCEEEEEECCC`
uniprot:	`------------------------------------------------EEEE-EEEEEE-`
AA:	`MEFSSPSREECPKPLSRVSIMAGSLTGLLLLQAVSWASGARPCIPKSFGYSSVVCVCNAT`

Conf:	`558889998889992599996377885421237645688875108995378301079247`
online:	`CCCCCCCCCCCCCCCEEEEEECCCCCCCCCCCCCCCCCCCCCCCEEEECCCCCEEEEEEE`
Conf:	`148899998788875431100345640022100111177897107840966454557422`
local:	`CCCCCCCCCCCCCCCCCCCCCCCCCCCEEEEEEEEECCCCCCCCEEEECCCCCCCEEEEE`
uniprot:	`-------------EEEEEEEE-----EEEEEEE-EEE----EEEEEEEEEEEEEE--EEE`
AA:	`YCDSFDPPTFPALGTFSRYESTRSGRRMELSMGPIQANHTGTGLLLTLQPEQKFQKVKGF`

Conf:	`300233899997249999999999860597882105999750588999986666899999`
online:	`EECCCHHHHHHHHCCCHHHHHHHHHHCCCCCCCEEEEEEEEECCCCCCCCCCCCCCCCCC`
Conf:	`011335889987508927898998851396893001358621344677653324799999`
local:	`CCCCCHHHHHHHHHCCHHHHHHHHHHHCCCCCCEEEEEEEECCCCCCCCCCCCCCCCCCC`
uniprot:	`EE--HHHHHHH----HHHHHHHHHHHH-CCCC---EEEEEEE--EEEEE------EEE--`
AA:	`GGAMTDAAALNILALSPPAQNLLLKSYFSEEGIGYNIIRVPMASCDFSIRTYTYADTPDD`

Conf:	`689999994100245289999999971999389971377785612147247999889999`
online:	`CCCCCCCCCHHCCCCCHHHHHHHHHHCCCCCEEEECCCCCCCCCEECCCCCCCCCCCCCC`
Conf:	`721111368543220024799998733999689957899974220056347854325899`
local:	`CCCCCCCCCCCCCCCHHHHHHHHHHHCCCCCEEEECCCCCCCCCCCCCCCCCCCCCCCCC`
uniprot:	`--------HHHH--HHHHHHHHHHH-----EEEEEEE---HHH----EEEEE-EEEE---`
AA:	`FQLHNFSLPEEDTKLKIPLIHRALQLAQRPVSLLASPWTSPTWLKTNGAVNGKGSLKGQP`

Conf:	`922699999999999999975490786872012579899999999986349999999999`
online:	`CCHHHHHHHHHHHHHHHHHHHCCEEEEEEECCCCCCCCCCCCCCCCCCCCCHHHHHHHHH`
Conf:	`971468799999999967663395143898112789787678873222114422121122`
local:	`CCHHHHHHHHHHHHHHHHHHHCCCCEEEEEEECCCCCCCCCCCCCCCCCCCCCCCCHHHH`
uniprot:	`-HHHHHHHHHHHHHHHHHHH-----EEEEE-----HHH------------HHHHHHHHHH`
AA:	`GDIYHQTWARYFVKFLDAYAEHKLQFWAVTAENEPSAGLLSGYPFQCLGFTPEHQRDFIA`

Conf:	`955799851689972999944888873334664149955640224689831699998033`
online:	`HHHHHHHHCCCCCCEEEEEECCCCCCHHHHHHHHCCCHHHHCCCCEEEEEECCCCCCHHH`
Conf:	`111332310577410134212544556520222238976651151878702212236320`
local:	`HHHHHHHHCCCCCCCEEEEECCCCCCCCCCHHHHCCCHHHHHCCEEEEEECCCCCCCCCC`
uniprot:	`-HHHHHH--CCCCEEEEEEEEEHHH--HHHHHHH--HHHH----EEEEEEE------HHH`
AA:	`RDLGPTLANSTHHNVRLLMLDDQRLLLPHWAKVVLTDPEAAKYVHGIAVHWYLDFLAPAK`

Conf:	`412688750999509994343699998866567831444255999999996402335772`
online:	`HHHHHHHHCCCCCEEEEECCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHCCEEEEEE`
Conf:	`011111126898001101210389653344579861210143212566552001100000`
local:	`CCCCCCCCCCCCCCEEHHHHCCCCCCCCCCCCCCCHHHHCCCCHHHHHHHHHHHHHHEEE`
uniprot:	`HHHHHHHH---EEEEEEEEE--------------HHHHHHHHHHHHHHHH--EEEEEEEE`
AA:	`ATLGETHRLFPNTMLFASEACVGSKFWEQSVRLGSWDRGMQYSHSIITNLLYHVVGWTDW`

Conf:	`000169999986689878535895679769986202333102244469939999542389`
online:	`EECCCCCCCCCCCCCCCCCCEEEECCCCEEEECCHHHHHHHHCCCCCCCCEEEEEEECCC`
Conf:	`023699999860001325228998208703226821232123444679927984435078`
local:	`CCCCCCCCCCCCEECCCCCCEEEEECCCCEEECCCEEEECCCCCCCCCCCEEEEEEEECC`
uniprot:	`E-----------------EEEEEHHH-EEEE-HHHHHHHHHH-------EEEEEEEEE--`
AA:	`NLALNPEGGPNWVRNFVDSPIIVDITKDTFYKQPMFYHLGHFSKFIPEGSQRVGLVASQK`

Conf:	`99028999928998999999099993779999099304998518951899999309`
online:	`CCCEEEEEECCCCCEEEEEECCCCCCEEEEEEECCCCEEEEECCCCEEEEEEEEEC`
Conf:	`99538995649997799999146898214741998642000389842568774139`
local:	`CCCCEEEEECCCCCEEEEEEECCCCCEEEEECCCCCCCCCCCCCCCEEEEEEEECC`
uniprot:	`EEEEEEEE-----EEEEEEE-EEE-EEEEEEECCCEEEEEEE---EEEEEEE----`
AA:	`NDLDAVALMHPDGSAVVVVLNRSSKDVPLTIKDPAVGFLETISPGYSIHTYLWRRQ`

Jpred3

Jpred3 was published in 2008 by Christian Cole, Jonathan D. Barber and Geoffrey J. Barton as "The Jpred 3 secondary structure prediction server" in Nucl. Acids Res. The Jnet algorithm predicts the secondary structure and the solvent accessibility of a protein with the help of alignment profiles. Therefore it uses the position-specific scoring matrix (PSSM) created by PSI-BLAST and a hidden Markov model. The final prediction is made with a neural network.<ref>http://nar.oxfordjournals.org/content/36/suppl_2/W197.full</ref>
As input, either the protein sequence or a multiple sequence alignment is taken.

Results

Jpred3 predicts the majority of the secondary structure elements correctly. Only rarely sheets and helices are mixed up, secondary structures overlooked and regions assigned to a certain structure by mistake. For the prediction a lot of BLAST-Hits with an E-value of 0 and one Hit with an E-Value of 2e-52 were used: 2wkl, 3keh, 3ke0, 3gxm, 3gxi, 3gxf, 3gxd, 2wcg, 2vt0, 2v3f, 2v3e, 2v3d, 2nt1, 2nt0, 2nsx, 2j25, 2f61, 1y7v, 1ogs, 2wnw. Of these proteins, all but 2wnw are glucocerebrosidase proteins of Homo Sapiens. The latter is a hydrolyse activated by a transcription factor from salmonella typhimurium, which seems to be a glucocerebrosidase as well. Some examples of the used proteins are shown in the pictures bellow.

OrigSeq	`MEFSSPSREECPKPLSRVSIMAGSLTGLLLLQAVSWASGARPCIPKSFGYSSVVCVCNATYCDSFDPPTFPALGTFSRYESTRSGRRMELSMGPIQANH`
Jnet	`-----------------HHHHHHHHHHHHHHHHHHHH---------------EEEEE-----------------EEEEEEE------------------`
jhmm	`----------------HHHHHHHHHHHHHHHHHHHHH---------------EEEEE-----------------EEEEEEE------------------`
jpssm	`------------------HHHHHHHHHHHHHHHHHH---------------EEEEEE-----------------EEEEEEE------------------`
uniprot	`------------------------------------------------EEEE-EEEEEE--------------EEEEEEEE-----EEEEEEE-EEE--`

OrigSeq	`TGTGLLLTLQPEQKFQKVKGFGGAMTDAAALNILALSPPAQNLLLKSYFSEEGIGYNIIRVPMASCDFSIRTYTYADTPDDFQLHNFSLPEEDTKLKIP`
Jnet	`----EEEEEE----EEEEEEEEEEHHHHHHHHH----HHHHHHHHHHH-------EEEEEE----------------------------------HHHH`
jhmm	`----EEEEE-----EEEEEEEEEEEHHHHHHHH----HHHHHHHHHHH-------EEEEEE----------------------------------HHHH`
jpssm	`----EEEEEE----EEEEEEEE-HHHHHHHHHH----HHHHHHHHHHH------EEEEEEEE--------------------------------HHHHH`
uniprot	`--EEEEEEEEEEEEEE--EEEEE--HHHHHHH----HHHHHHHHHHHH-CCCC---EEEEEEE--EEEEE------EEE----------HHHH--HHHH`

OrigSeq	`LIHRALQLAQRPVSLLASPWTSPTWLKTNGAVNGKGSLKGQPGDIYHQTWARYFVKFLDAYAEHKLQFWAVTAENEPSAGLLSGYPFQCLGFTPEHQRD`
Jnet	`HHHHHHHH----EEEEE--------EE-------------------HHHHHHHHHHHHHHHHH----EEEEE---------------------HHHHHH`
jhmm	`HHHHHHHH----EEEEE-----------------------------HHHHHHHHHHHHHHHHH----EEEEE---------------------HHHHHH`
jpssm	`HHHHHHHHH---EEEEE--------EEE-----------------HHHHHHHHHHHHHHHHHH----EEEEEE--------------------HHHHHH`
uniprot	`HHHHHHH-----EEEEEEE---HHH----EEEEE-EEEE----HHHHHHHHHHHHHHHHHHH-----EEEEE-----HHH------------HHHHHHH`

OrigSeq	`FIARDLGPTLANSTHHNVRLLMLDDQRLLLPHWAKVVLTDPEAAKYVHGIAVHWYLDFLAPAKATLGETHRLFPNTMLFASEACVGSKFWEQSVRLGSW`
Jnet	`HHHHHHHHHHHH-----EEEEEE--------HHHHHHH--HHHHHHHHEEEE----------HHHHHHHHHH-----EEEEEEEE--------------`
jhmm	`HHHHHHHHHHHH------EEEEE-------HHHHHHHH--H-HHHHHHEEEE----------HHHHHHHHHH-----EEEEEEEE--------------`
jpssm	`HHHHHHHHHHHH-----EEEEEEE-------HHHHHH----HHHHHH--EE-----------HHHHHHHHHH-----EEEEEEE--------------H`
uniprot	`HHH-HHHHHH--CCCCEEEEEEEEEHHH--HHHHHHH--HHHH----EEEEEEE------HHHHHHHHHHH---EEEEEEEEE--------------HH`

OrigSeq	`DRGMQYSHSIITNLLYHVVGWTDWNLALNPEGGPNWVRNFVDSPIIVDITKDTFYKQPMFYHLGHFSKFIPEGSQRVGLVASQKNDLDAVALMHPDGSA`
Jnet	`HHHHHHHHHHHHHHHHHHHHHHHHHHH----------------EEEEE----EEEE---HHHHHHHH-------EEEEE-------EEEEEEE-----E`
jhmm	`-HHHHHHHHHHHHHHHHHHHHHHHHHH----------------EEEEE----EEEE---HHHHHHHH-------EEEE--------EEEEEEE-----E`
jpssm	`HHHHHHHHHHHHHHHHHHHHHHHHHHE----------------EEEEE----EEEE--HHHHHHHH--------EEEEEE------EEEEEEEE----E`
uniprot	`HHHHHHHHHHHHHH--EEEEEEEEE-----------------EEEEEHHH-EEEE-HHHHHHHHHH-------EEEEEEEEE--EEEEEEEE-----EE`

OrigSeq	`VVVVLNRSSKDVPLTIKDPAVGFLETISPGYSIHTYLWRRQ`
Jnet	`EEEEEE-----EEEEEEE---EEEEEEE----EEEEEEE--`
jhmm	`EEEEEE-----EEEEEEE---EEEEEEE----EEEEEEE--`
jpssm	`EEEEEE----EEEEEEEE---EEEEEEE---EEEEEEE---`
uniprot	`EEEEE-EEE-EEEEEEECCCEEEEEEE---EEEEEEE----`

Structure of 2wkl
Source:http://www.ebi.ac.uk/pdbsum/2wkl

Structure of 3keh
Source: http://www.ebi.ac.uk/pdbsum/3keh

Structure of 1ogs
Source: http://www.ebi.ac.uk/pdbsum/1ogs

Structure of 2wnw
Source: http://www.ebi.ac.uk/pdbsum/2wnw

Comparison with DSSP

DSSP, which stands for Define Secondary Structure of Proteins, is by Wolfgang Kabsch and Chris Sander, who published it 1983 in Biopolymers with the title "Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features".<ref>http://swift.cmbi.ru.nl/gv/dssp/</ref>
The DSSP algorithm recognises the secondary structure. Therefore it defines hydrogen bonds with an electrostatic definition. The different patterns of hydrogen bonds constitute one of eight possible secondary structure types. So it is no prediction tool.<ref>http://en.wikipedia.org/wiki/DSSP_%28protein%29</ref>

                    10        20        30        40        50        60
                     |         |         |         |         |         |
   1 -   60 ARPCIPKSFGYSSVVCVCNATYCDSFDPPTFPALGTFSRYESTRSGRRMELSMGPIQANH
   1 -   60      SSS TTTTSSSSSSTT            TTSSSSSSSSTTT  TSSSSSS  TT
   1 -   60                     *                                      *
   1 -   60 AAA AAAAAAAA        A     AA  A  A    A    AA A  AA   A AAAA
                    70        80        90       100       110       120
                     |         |         |         |         |         |
  61 -  120 TGTGLLLTLQPEQKFQKVKGFGGAMTDAAALNILALSPPAQNLLLKSYFSEEGIGYNIIR
  61 -  120    TSSSSSSSSSSSSS  SSSSS  HHHHHHHTTT HHHHHHHHHHHHTTTTT   SSS
  61 -  120   *    * *
  61 -  120 A A  A   A AAAA A             A  A  AAA  A   A    AA
                   130       140       150       160       170       180
                     |         |         |         |         |         |
 121 -  180 VPMASCDFSIRTYTYADTPDDFQLHNFSLPEEDTKLKIPLIHRALQLAQRPVSLLASPWT
 121 -  180 SSST  TTTTT   T  TTT TT TT    HHHHTTHHHHHHHHHHH TT  SSSSSST
 121 -  180          **       *           **
 121 -  180          AAA    AAAA AA AA    A  AA   A  AA AAA A A
                   190       200       210       220       230       240
                     |         |         |         |         |         |
 181 -  240 SPTWLKTNGAVNGKGSLKGQPGDIYHQTWARYFVKFLDAYAEHKLQFWAVTAENEPSAGL
 181 -  240   333 TT TTTTT   TT TTTHHHHHHHHHHHHHHHHHHHTT   TSSST TTTT333
 181 -  240        ***** *
 181 -  240       AAAAAA A   AAA  AAA A   A   A  A   AAA A             A
                   250       260       270       280       290       300
                     |         |         |         |         |         |
 241 -  300 LSGYPFQCLGFTPEHQRDFIARDLGPTLANSTHHNVRLLMLDDQRLLLPHWAKVVLTDPE
 241 -  300 TTT  T      HHHHHHHHHHTHHHHHHTTTTTTTSSSSSSSS333TTHHHHHHHTTHH
 241 -  300  ** *                                        *
 241 -  300 AAAAA      A AA  A   A   A  AA A AA A      A A   A  A   A AA
                   310       320       330       340       350       360
                     |         |         |         |         |         |
 301 -  360 AAKYVHGIAVHWYLDFLAPAKATLGETHRLFPNTMLFASEACVGSKFWEQSVRLGSWDRG
 301 -  360 HHTT  SSSSSSSTTT   HHHHHHHHHHH TTTSSSSSSSS    TTT T  TT HHHH
 301 -  360                **           *   *             **
 301 -  360   AA        A  AA A AA   A AA  AA            AAA A  A    A
                   370       380       390       400       410       420
                     |         |         |         |         |         |
 361 -  420 MQYSHSIITNLLYHVVGWTDWNLALNPEGGPNWVRNFVDSPIIVDITKDTFYKQPMFYHL
 361 -  420 HHHHHHHHHHHHTTSSSSSSSST   TTT   TT      TSSSS333TSSSS HHHHHH
 361 -  420                               *** ***        **
 361 -  420     A      AA            AAA    A A A        AAA
                   430       440       450       460       470       480
                     |         |         |         |         |         |
 421 -  480 GHFSKFIPEGSQRVGLVASQKNDLDAVALMHPDGSAVVVVLNRSSKDVPLTIKDPAVGFL
 421 -  480 HHHHTT  TT SSSSSSSTT  TSSSSSSS TTT SSSSSSS TTT SSSSSSSTTTSSS
 421 -  480                 *****                               *     *
 421 -  480         A       AAAAAAA       AAA         A AAA A   A AA  A
                   490
                     |
 481 -  497 ETISPGYSIHTYLWHRQ
 481 -  497 SSSS TTSSSSSSS
 481 -  497                 *
 481 -  497 A A A          AA

            500       510       520       530       540       550
              |         |         |         |         |         |
 498 -  557 ARPCIPKSFGYSSVVCVCNATYCDSFDPPTFPALGTFSRYESTRSGRRMELSMGPIQANH
 498 -  557      SS  TTTT SSSS TT      T     TTSSSSSSSSTTT  TSSSSSS  T
 498 -  557   * *                           *  *             ****** *
 498 -  557 AAA AAAAAAAA        A    AAAAAA AA    A    AA A   A A A AAAA
            560       570       580       590       600       610
              |         |         |         |         |         |
 558 -  617 TGTGLLLTLQPEQKFQKVKGFGGAMTDAAALNILALSPPAQNLLLKSYFSEEGIGYNIIR
 558 -  617   TTSSSSSSSSSSSSS  SSSSS  HHHHHHHHTT HHHHHHHHHHHHTTTTT   SSS
 558 -  617                               *     *
 558 -  617 AAAA A   A AAAA A             A  AA AAA  A   A    A
            620       630       640       650       660       670
              |         |         |         |         |         |
 618 -  677 VPMASCDFSIRTYTYADTPDDFQLHNFSLPEEDTKLKIPLIHRALQLAQRPVSLLASPWT
 618 -  677 SSST  TTTTT   T  TTT TT TT    HHHHTTHHHHHHHHHHH TT  SSSSSST
 618 -  677          ***    *             *
 618 -  677          AAA    AAAA  A AA A  A  AAA     AA AAA AAA
            680       690       700       710       720       730
              |         |         |         |         |         |
 678 -  737 SPTWLKTNGAVNGKGSLKGQPGDIYHQTWARYFVKFLDAYAEHKLQFWAVTAENEPSAGL
 678 -  737   333 TT TTTTT   TT TTTHHHHHHHHHHHHHHHHHHHTT   TSSST TT33333
 678 -  737   *
 678 -  737   A   AAA  A A   AAA      A   A   A  A   AAA A             A
            740       750       760       770       780       790
              |         |         |         |         |         |
 738 -  797 LSGYPFQCLGFTPEHQRDFIARDLGPTLANSTHHNVRLLMLDDQRLLLPHWAKVVLTDPE
 738 -  797 TTT  T      HHHHHHHHHHTHHHHHHTTTTTTTSSSSSSSS333TTHHHHHHHTTHH
 738 -  797   ***                                        **
 738 -  797 AAA A      A A  AA   A   A  AA A AA A      A A   A  A   A AA
            800       810       820       830       840       850
              |         |         |         |         |         |
 798 -  857 AAKYVHGIAVHWYLDFLAPAKATLGETHRLFPNTMLFASEACVGSKFWEQSVRLGSWDRG
 798 -  857 HHTT  SSSSSSSTTT   HHHHHHHHHHH TTTSSSSSSSST  TTTT T  TT HHHH
 798 -  857                **   *       ** *              **
 798 -  857   A         A  AA A AA   A AAA AA            AAA A  A    A
            860       870       880       890       900       910
              |         |         |         |         |         |
 858 -  917 MQYSHSIITNLLYHVVGWTDWNLALNPEGGPNWVRNFVDSPIIVDITKDTFYKQPMFYHL
 858 -  917 HHHHHHHHHHHHTTSSSSSSSST   TTT   TT      TSSSS333TSSSS HHHHHH
 858 -  917                               *   **         **
 858 -  917     A      AA             AA    AAAA         AAA
            920       930       940       950       960       970
              |         |         |         |         |         |
 918 -  977 GHFSKFIPEGSQRVGLVASQKNDLDAVALMHPDGSAVVVVLNRSSKDVPLTIKDPAVGFL
 918 -  977 HHHHTT  TT SSSSSSSTT  TSSSSSSS TTT SSSSSSS TTT SSSSSSSTTTSSS
 918 -  977
 918 -  977         A       AAAAAAA        AA         A AAA A   A A   A
            980       990
              |         |
 978 -  994 ETISPGYSIHTYLWHRQ
 978 -  994 SSSS TTSSSSSSS
 978 -  994
 978 -  994 A A            AA

The result of DSSP has four lines: The first line describes the amino acids, the second the secondary structure, the third residues involved in symmetry contacts, which are marked with an asterix (*) and the fourth solvent accessible residues, which are marked with an A.
The secondary structure has the elements H, T, 3 and S, where H indicates an alpha helix, T a hydrogen bonded turn, 3 a residue in an isolated beta-bridge and S a bend, which is a region of high curvature.
The structure differs from our GBA sequence, because it used the PDB-file, which contains the sequence without the signaling peptide at the beginning and both domains of the protein. In the following we compare the secondary structure of domain A by DSSP with the secondary structure in Uniprot.

uniprot	`----------EEEE-EEEEEE--------------EEEEEEEE-----EEEEEEE-EEE-`
DSSP	`-----SSS-TTTTSSSSSSTT------------TTSSSSSSSSTTT--TSSSSSS--TT-`

uniprot	`---EEEEEEEEEEEEEE--EEEEE--HHHHHHH----HHHHHHHHHHHH-CCCC---EEE`
DSSP	`TSSSSSSSSSSSSS--SSSSS--HHHHHHHTTT-HHHHHHHHHHHHTTTTT---SSS`

uniprot	`EEEE--EEEEE------EEE----------HHHH--HHHHHHHHHHH-----EEEEEEE-`
DSSP	`SSST--TTTTT---T--TTT-TT-TT----HHHHTTHHHHHHHHHHH-TT--SSSSSST-`

uniprot	`--HHH----EEEEE-EEEE----HHHHHHHHHHHHHHHHHHH-----EEEEE-----HHH`
DSSP	`--333-TT-TTTTT---TT-TTTHHHHHHHHHHHHHHHHHHHTT---TSSST-TTTT333`

uniprot	`------------HHHHHHHHHH-HHHHHH--CCCCEEEEEEEEEHHH--HHHHHHH--HH`
DSSP	`TTT--T------HHHHHHHHHHTHHHHHHTTTTTTTSSSSSSSS333TTHHHHHHHTTHH`

uniprot	`HH----EEEEEEE------HHHHHHHHHHH---EEEEEEEEE--------------HHHH`
DSSP	`HHTT--SSSSSSSTTT---HHHHHHHHHHH-TTTSSSSSSSS----TTT-T--TT-HHHH`

uniprot	`HHHHHHHHHHHH--EEEEEEEEE-----------------EEEEEHHH-EEEE-HHHHHH`
DSSP	`HHHHHHHHHHHHTTSSSSSSSST---TTT---TT------TSSSS333TSSSS-HHHHHH`

uniprot	`HHHH-------EEEEEEEEE--EEEEEEEE-----EEEEEEE-EEE-EEEEEEECCCEEE`
DSSP	`HHHHTT--TT-SSSSSSSTT--TSSSSSSS-TTT-SSSSSSS-TTT-SSSSSSSTTTSSS`

uniprot	`EEEE---EEEEEEE---`
DSSP	`SSSS-TTSSSSSSS---`

The result with DSSP is good. Most helices are right and the beta sheets are determined mostly as bends. Sometimes the helices or beta sheets are mixed up with turns, but overall it fits quite well.

Prediction of disordered regions

General

Disordered regions are regions with no fixed secondary and therefore tertiary structure. The Ramachandran angles of these regions are very flexible, which means, they can have more than one secondary structure which often depends on the binding to a substrate.
There are two types of disordered regions, the extended or random coil like ones and the collapsed or molten gloguble like ones. They have a lot of different functions, for example they are responsible for the DNA/RNA/protein recognition or the specificity and affinity of the binding. They are often involved in regulatory functions.<ref>http://www.pondr.com/pondr-tut1.html</ref>

DISOPRED

DISOPRED was published by Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF and Jones DT in 2004 in the Journal of Molecular Biology with the title "Prediction and functional analysis of native disorder in proteins from the three kingdoms of life".<ref>http://bioinf.cs.ucl.ac.uk/disopred/</ref>
The method is based on a neural network which was trained with the SVMlight support vector machine package. DISOPRED first uses PSI-BLAST with a filtered sequence database and uses the position-specific scoring matrix at the final iteration to generate inputs for DISOPRED.<ref>http://cms.cs.ucl.ac.uk/typo3/fileadmin/bioinf/Disopred/disopred_help.html</ref>

DISOPRED predictions for a false positive rate threshold of: 5%

conf	`999999999988777630000000000000000000000000000000000000000000`
pred	`****************............................................`
AA	`MEFSSPSREECPKPLSRVSIMAGSLTGLLLLQAVSWASGARPCIPKSFGYSSVVCVCNAT`
conf	`000000000000000000000001456676677776543210000000000000000000`
pred	`.........................************.......................`
AA	`YCDSFDPPTFPALGTFSRYESTRSGRRMELSMGPIQANHTGTGLLLTLQPEQKFQKVKGF`
conf	`000000000000000000000000000000000000000000000000000000000000`
pred	`............................................................`
AA	`GGAMTDAAALNILALSPPAQNLLLKSYFSEEGIGYNIIRVPMASCDFSIRTYTYADTPDD`
conf	`000000000000000000000000000000000000000000000000000021000000`
pred	`............................................................`
AA	`FQLHNFSLPEEDTKLKIPLIHRALQLAQRPVSLLASPWTSPTWLKTNGAVNGKGSLKGQP`
conf	`000000000000000000000000000000000000000000000000000000000000`
pred	`............................................................`
AA	`GDIYHQTWARYFVKFLDAYAEHKLQFWAVTAENEPSAGLLSGYPFQCLGFTPEHQRDFIA`
conf	`000000000000000000000000000000000000000000000000000000000000`
pred	`............................................................`
AA	`RDLGPTLANSTHHNVRLLMLDDQRLLLPHWAKVVLTDPEAAKYVHGIAVHWYLDFLAPAK`
conf	`000000000000000000000000000000000000000000000000000000000000`
pred	`............................................................`
AA	`ATLGETHRLFPNTMLFASEACVGSKFWEQSVRLGSWDRGMQYSHSIITNLLYHVVGWTDW`
conf	`000000000000000000000000000000000000000000000000000000022331`
pred	`............................................................`
AA	`NLALNPEGGPNWVRNFVDSPIIVDITKDTFYKQPMFYHLGHFSKFIPEGSQRVGLVASQK`
conf	`00000000000000000000000000000000000000000000000000000004`
pred	`........................................................`
AA	`NDLDAVALMHPDGSAVVVVLNRSSKDVPLTIKDPAVGFLETISPGYSIHTYLWRRQ`

Asterisks (*) represent disorder predictions and dots (.) prediction of order. The confidence estimates give a rough indication of the probability that each residue is disordered.

The interesting thing is, that the signale peptide is marked as a disordered region and has also a very high confidence. The second as disordered predicted region is part of a beta sheet. So there the prediction may be wrong.

POODLE

POODLE stands for Prediction Of Order and Disorder by machine LEarning. It is by S. Hirose, K. Shimizu, N. Inoue, S. Kanai and T. Noguchi and was published in 2008 in CASP8 Proceedings as "Disordered region prediction by integrating POODLE series".

It consists of three predictions:

short disorder regions prediction (POODLE-S: short disorder regions, missing region in X ray structure or high B-factor region)
long disorder regions prediction (POODLE-L: mainly longer than 40 consecutive amino acids)
unfolded protein prediction

POODLE-I is based on a work-flow approach. POODLE uses machine learning and only needs the amino acid sequence for prediction.<ref>http://mbs.cbrc.jp/poodle/help.html</ref>

POODLE-I

result of POODLE-I (POODLE series only)

Predicted as disordered:

no.	AA	ORD/DIS	Prob.
1	M	D	0.851
2	E	D	0.813
3	F	D	0.781
4	S	D	0.753
5	S	D	0.757
6	P	D	0.813
7	S	D	0.777
8	R	D	0.743
9	E	D	0.699
10	E	D	0.707
11	C	D	0.711
12	P	D	0.656
13	K	D	0.633
14	P	D	0.609
15	L	D	0.581
16	S	D	0.553
17	R	D	0.535
18	V	D	0.51
...
95	I	D	0.58
96	Q	D	0.812
97	A	D	0.892
98	N	D	0.744
99	H	D	0.551

The first as disordered predicted regions are part of the signal peptide. The second as disordered predicted regions are part of a beta sheet.

POODLE-S

result of POODLE-S (missing residues)

result of POODLE-S ()

Predicted as disordered:

no.	AA	ORD/DIS/xray	Prob./xray	ORD/DIS/B-factor	Prob./B-factor
1	M	D	0.764	D	1
2	E	D	0.707	D	0.901
3	F	D	0.731	D	0.882
4	S	D	0.759	D	0.875
5	S	D	0.764	D	0.847
6	P	D	0.75	D	0.825
7	S	D	0.782	D	0.811
8	R	D	0.751	D	0.791
9	E	D	0.707	D	0.711
10	E	D	0.692	D	0.661
11	C	D	0.679	D	0.568
12	P	D	0.66	D	0.596
13	K	D	0.633	D	0.557
14	P	D	0.607	D	0.517
15	L	D	0.588	O	0.493
16	S	D	0.55	O	0.489
17	R	D	0.523	O	0.424
18	V	D	0.534	O	0.391
...
100	T	O	0.368	D	0.571
101	G	O	0.29	D	0.54
102	T	O	0.243	D	0.555
...
360	K	D	0.527	0	0.133
361	A	D	0.534	0	0.138
362	T	D	0.577	0	0.162
363	L	D	0.594	0	0.162
364	G	D	0.513	0	0.163

POODLE-S also predicts a part of the signal peptide as disordered. It is based on looking for regions that are missing in the X-ray structure. Based on high B-factor region there are later also some disordered regions. The first one is not defined in Uniprot, the second one is part of a helix.

POODLE-L

result of POODLE-L

POODLE-L predicted no disordered regions. So there are no disordered regions that are longer than 40 amino acids.

IUPred

IUPred is a method by Zsuzsanna Dosztányi, Veronika Csizmók, Péter Tompa and István Simon which was published 2005 in Bioinformatics with the title "IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content".

The idea of IUPred is to estimate the ability of polypeptides to form stabilizing contacts. It depends on the surrounding sequence and its chemical properties. Intrinsically unstructured regions (IUs) cannot form such contacts. With a 20 by 20 energy predictor matrix the energies can be calculated and with that the probability of IUs.<ref>http://iupred.enzim.hu/Theory.html</ref>

For IUPred you can choose one of these three options<ref>http://iupred.enzim.hu/Help.html</ref>:

long disorder: predicts context-independent global disorder with at least 30 consecutive residues of predicted disorder (neighbourhood of 100 residues is considered)
short disorder: predicts short, probably context-dependent, disordered regions, e.g. missing residues in the X-ray structure (neighbourhood of 25 residues is considered)
structured domains: predicts putative structured domains

IUPred: short disordered regions

Diagram of IUPred for short disorders

Disordered regions:

Position	Residue	Disorder Tendency
1	M	0.9753
2	E	0.9369
3	F	0.9280
4	S	0.9009
5	S	0.8857
6	P	0.7869
7	S	0.7418
8	R	0.7034
9	E	0.5992
10	E	0.5549
...
85	G	0.5126
86	R	0.4458
87	R	0.5412
88	M	0.5173
89	E	0.5173
90	L	0.5992
91	S	0.5846
92	M	0.5900
93	G	0.5900
94	P	0.5900
95	I	0.5374
...
103	G	0.5173
104	L	0.5084
...
533	W	0.5514
534	R	0.5992
535	R	0.6124
536	Q	0.6474

IUPred for short disordered regions predicts for the beginning residues disordered regions with a high probability. The reason for that may be the signal peptide, which is there. It is hard to say, if the other regions are also disordered, because the probabilities are not really high. A disordered region of a length two is really short and therefore not very probable. At the end there is no secondary structure given at Uniprot, so maybe there is a disordered region.

IUPred: long disordered regions

Diagram of IUPred for long disorders

Disordered regions:

Position	Residue	Disorder Tendency
1	M	0.4864
2	E	0.5017
3	F	0.5707
...
87	R	0.4979
88	M	0.4979
89	E	0.4979
90	L	0.6136
91	S	0.5901
92	M	0.5992
93	G	0.5017
...
229	A	0.5055
230	V	0.5055
231	N	0.5211
...
235	S	0.5055
236	L	0.5139

IUPred for long disordered regions predicts some disordered regions but with a very low probability. They are also very short. So it is not very probable, that there are really disordered regions.

IUPred: structured regions

Diagram of IUPred for structured regions

IUPred for structured regions predicts globular domains for position 4 to 536. So all except the first four residues are predicted as structured.

META-Disorder

META-Disorder was published 2009 by Avner Schlessinger, Marco Punta, Guy Yachdav, Laszlo Kajan and Burkhard Rost with "Improved Disorder Prediction by Combination of Orthogonal Approaches" in PLoS ONE.<ref>http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0004433</ref>
It is a method which combines NORSnet<ref>https://www.rostlab.org/owiki/index.php/Norsnet</ref>, PROFbval<ref>https://rostlab.org/owiki/index.php/Profbval</ref> and Ucon<ref>https://www.rostlab.org/owiki/index.php/UCON</ref> to predict disordered regions. As input only the amino acid sequence is needed.<ref>https://www.rostlab.org/owiki/index.php/Metadisorder</ref>

Result of PredictProtein for disordered regions.

META-Disorder predicts for the first six residues disordered regions:

position	residue	score
1	M	0.636
2	E	0.596
3	F	0.596
4	S	0.586
5	S	0.561
6	P	0.551

The region, which is predicted to be disordered, is part of the signal peptide. If it is really a disordered region is hard to say, because the begin of a protein is often mispredicted as disordered.

Prediction of transmembrane alpha-helices and signal peptides

General

Transmembrane topology

The topology of a membrane protein is characterized by the number of membrane spanning segments in the protein. The transmembrane regions of the protein are hydrophobic and have a length of aproximately 15-30 residues which is enough to cross the lipid bilayer of the membrane once. The different transmembrane regions are connected by hydrophilic loops which are located outside the membrane. These attributes can be used to predict the transmembrane topology of a protein.
Predictors: TMHMM, OCTOPUS

Signal peptides

Signal Peptides are located at the N-terminus of a protein sequence and direct the transport of a protein to its correct location. Signal Sequences have a typical size of 20-30 residues and have a tripartite structure: a hydrophobic core region (h-region) which is flanked by a basic n-region and a slightly polar c-region. The sequence variation among signal sequences affects ER targeting, translocation and signal peptidase cleavage. <ref>Hegde R.S. and Bernstein H.D. (2006) The surprising complexity of signal sequences. Trends Biochem Sci 31(10), 563-71</ref>
Predictors: SignalP, TargetP

Combined transmembrane and signal peptide prediction

The high similarity between the hydrophobic region of a transmembrane helix and the one of a signal peptide leads to cross-predictions when conventional transmembrane topology and signal peptide predictors as TMHMM and SignalP are used. Predictors which are based on submodels for both make less errors coming from cross-predictions and help to discriminate against false positives. Furthermore, a predicted signal peptide indicates that the N-terminus of the protein is non-cytoplasmic and is therefore helpful to assign the orientation of the protein. <ref>Käll L. Krogh A, & Sonnhammer, E. L. (2007) Advantages of combined tranasmembrane topology and signal peptide prediction - the Phobius web server. Nucleic Acids Res., Vol. 35, Web server issue, S.429-32</ref>
Predictors: Phobius, Polyphobius, SPOCTOPUS.

TMHMM

TMHMM is a method to predict transmembrane topology of membrane-spanning proteins. It is based on a hidden Markoc model with an architecture of 7 types of states (helix core, helic caps on both sides, one loop on the cytoplasmic side, two loops on the non-cytoplasmic side and a globular domain in the middle of each loop) which correspond to the biological system. The method was established by Sonnhammer et al. in 1998 <ref>Sonnhammer EL, von Heijne G, Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol. 1998;6:175–182.</ref>.

Usage

Results

TMHMM posterior probabilities for glucocerebrosidase

sp|P04062|GLCM_HUMAN Length: 536 sp|P04062|GLCM_HUMAN Number of predicted TMHs: 0 sp|P04062|GLCM_HUMAN Exp number of AAs in TMHs: 1.77867 sp|P04062|GLCM_HUMAN Exp number, first 60 AAs: 1.62607 sp|P04062|GLCM_HUMAN Total prob of N-in: 0.06840 sp|P04062|GLCM_HUMAN TMHMM2.0 outside 1 536

TMHMM predicts no transmembrane segments for the sequence of glucocerebrosidase which is correct.

Phobius and PolyPhobius

Phobius is based on a hidden Markoc model which contains submodels for both transmembrane helices and signal peptides and therefore obtains a better discrimination between the two segments than predictors for only one of them. This method was presented in 2004 by Käll et al. <ref>Käll L., et al. A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 2004;338:1027–1036.</ref> Polyphobius is a pradiction server that additionally uses an algorithm to include homology information. The performance of transmembrane topology and signal peptide prediction is increased by incorporating extra support from homologs. <ref>Käll L., et al. An HMM posterior decoder for sequence feature prediction that includes homology information Bioinformatics, 21 (Suppl 1):i251-i257, June 2005.</ref>

Usage - Phobius

Webserver: http://phobius.sbc.su.se/
Input: Protein sequence in fasta format

Results - Phobius

Phobius posterior probabilities for glucocerebrosidase

ID sp|P04062|GLCM_HUMAN FT SIGNAL 1 39 FT REGION 1 19 N-REGION. FT REGION 20 31 H-REGION. FT REGION 32 39 C-REGION. FT TOPO_DOM 40 536 NON CYTOPLASMIC.

Phobius predics a signal peptide ranging from amino acid 1 to amino acid 39 of the sequence of glucocerebrosidase. This goes along with the information given on Uniprot <ref>http://www.uniprot.org/uniprot/P04062</ref> that the protein has a 39 residue signal sequence. The presence of a signal peptide explains the differences between the sequences of sp|P04062|GLCM_HUMAN and its corresponding PDB structure 1OGS: the sequence of 1OGS has 39 amino acids less than the sequence of sp|P04062|GLCM_HUMAN as the signal peptide is missing in the mature structure. The prediction, that the protein is non-cytoplasmic is true, as glucocerebrosidase is located in the lysosome.

Usage - PolyPhobius

Webserver: http://phobius.sbc.su.se/poly.html
Input: Protein sequence in fasta format

Results - PolyPhobius

Phobius posterior probabilities for glucocerebrosidase

ID sp|P04062|GLCM_HUMAN FT SIGNAL 1 39 FT REGION 1 23 N-REGION. FT REGION 24 34 H-REGION. FT REGION 35 39 C-REGION. FT TOPO_DOM 40 536 NON CYTOPLASMIC.

Polyphobius returns the same predictions as Phobius: a 39 residue signal sequence with slightly different region ranges.

OCTOPUS and SPOCTOPUS

OCTOPUS (obtainer of correct topologies for uncharacterized sequences) was developed in 2007 by Viklund et al. The method combines hidden markov models and artificial neural networks. Furthermore, OCTOPUS is the first method that integrates the modelation of reentrant-, membrane dip-, and TM hairpin regions. <ref>Viklund, H., and A. Elofsson. 2008. OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24:1662-1668.</ref> SPOCTOPUS is an extention of the OCTOPUS algorithm which additionally predictes signal peptides for reducing predictions of transmembrane regions as signal peptides and the other way round. The method was first mentiond by Viklund et al. in 2008. <ref>Viklund, H., et al. 2008. SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology Bioinformatics 24:2928-2929.</ref>

Usage

Webserver: http://octopus.cbr.su.se/index.php
Input: Protein sequence in fasta format

OCTOPUS - Results

Results of the topology prediction with Octopus

OCTOPUS predicts a transmembrane segment ranging from amino acid 16 to amino acid 36. The region from amino acid 1 to 15 is predicted to be cytoplasmic and the remaining amino acids (from amino acid 37) are indicated to be non cytoplasmic. This is an example for a misclassification between transmembrane segments and signal peptides when a prediction tool for only transmembrane segments is used.

SPOCTOPUS - Results

Results of the topology prediction with Spoctopus

SPOCTOPUS correctly identifies the regions from amino acid 1 to amino acid 39 as a signal peptide. The combination of a signal peptide and transmembrane segment prediction helps to eliminate the misclassification made by a transmembrane segment predictor (cf. results of OCTOPUS).

SignalP

SignalP uses a combination of several artificial neural networks and hidden Markov models to predict the presence and location of signal peptide cleavage sites of three different organism groups: eukaryotes, Gram-negative and Gram-positive bacteria. The current version 3.0 was published in 2004 by Bendtsen et al. <ref>Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol. 2004;340:783–795.</ref>

Results

Prediction of signal peptide with SignalP 3.0 (image taken from the web server<ref>http://www.cbs.dtu.dk/services/SignalP/</ref>)

>sp|P04062|GLCM_HUMAN Prediction: Signal peptide Signal peptide probability: 0.516 Signal anchor probability: 0.001 Max cleavage site probability: 0.423 between pos. 39 and 40

SignalP predicts the signal peptide of glucocerebrosidase correctly. Even the cleavage site is located correctly between amino acid 39 and amino acid 40. The location of the different regions of the signal peptide can be seen in the illustration to the right.

TargetP

TargetP, a neural network-based tool, predicts the subcellular location of eukaryotic proteins based on the predicted presence of N-terminal presequences like mitochodraial targeting peptides, chloroplast trasit peptides and pathway signal peptides. For the latter two potential cleavage sites can be predicted as TargetP uses ChloroP and SignalP. This method, which only uses N-terminal sequence information, was published in 2000 by Emanuelsson et al. <ref>Emanuelsson O., et al. Predicting Subcellular Localization of Proteins Based on their N-terminal Amino Acid Sequence. J.Mol.Biol. (2000) 300, 1005-1016.</ref>

Usage

Webserver: http://www.cbs.dtu.dk/services/TargetP/
Input: protein sequence in fasta format, cutoff, organism group

Results

Name Len mTP SP other Loc RC TPlen sp_P04062_GLCM_HUMAN 536 0.091 0.364 0.612 _ 4 - cutoff 0.000 0.000 0.000

TargetP does not predict the signal peptide of glucocerebrosidase. Although the score for a signal peptide is much higher than the one for a mitochondrial targeting peptide, it locates the protein with a very low reliability class rather at any other location than in chloroplast, mitochondrion or the secretory pathway.

Further Examples of Use

As the protein glucocerebrosidase does not contain any transmembrane segments, the application of the different tools mentioned above will further be demonstrated with some other proteins: BACR_HALSA<ref>http://www.uniprot.org/uniprot/P02945</ref>, RET4_HUMAN<ref>http://www.uniprot.org/uniprot/P02753</ref>, INSL5_HUMAN<ref>http://www.uniprot.org/uniprot/Q9Y5Q6</ref>, LAMP1_HUMAN<ref>http://www.uniprot.org/uniprot/P11279</ref> and A4_HUMAN<ref>http://www.uniprot.org/uniprot/P05067</ref>.

Topology

The table below indicates the correct number of transmembrane segments and presence or absence of a signal peptide in the five proteins mentioned above. A more detailed description with the exact positions of the membrane segments and the cleavage sites can be seen in the corresponding Uniprot entries.

Protein	# Transmembrane Segments	Signal Peptide
BACR_HALSA	7	0
RET4_HUMAN	0	1
INSL5_HUMAN	0	1
LAMP1_HUMAN	1	1
A4_HUMAN	1	1

TMHMM

The topology predictions of TMHMM for the different proteins are illustrated in the graphic below. TMHMM predicts 6 different transmembrane segments for BACR_HASLA, but indicates, that a signal peptide is possible. According to Uniprot, BACR_HASLA consists of 7 different transmembrane segments and no signal peptide. The last transmembrane helix, ranging from amino acid 217 to 236 was not predicted by TMHMM. The prediction that RET4_HUMAN and INSL5_Human do not contain any transmembrane segments goes along with the corresponding Uniprot entries. TMHMM finds 2 transmembrane segments in LAMP1_HUMAN. The first transmembrane segment is in reality a signal paptide, as indicated in the Uniprot entry and is therefore another example for the confusion of transmembrane segments and signal peptides. The single transmembrane helix of A4_HUMAN was predicted correctly.

Phobius and PolyPhobius

The results of Phobius and PolyPhobius are identical. There are only minor differences in the length of the different regions of a signal peptide. The topology predictions of both methods for the different proteins are illustrated in the graphic below. The predictions for all of the proteins were made correctly by Phobius and PolyPhobius. Sometimes the transmembrane segments are shifted some amino acid positions to the right or to the left. The ortientation of the proteins, the presence of signal peptides and the overall topology of the proteins go along with the corresponding Uniprot entries.

OCTOPUS

OCTOPUS makes a lot of false predictions resulting from a confusion between signal peptides and transmembrane regions. Each signal peptide was predicted as either transmembrane segment or reentrant/dip region. BACR_HASLA, which does not have a signal peptide, was predicted correctly.

SPOCTOPUS

SPOCTOPUS, which expends the OCTOPUS algorithm with a signal peptide prediction, predicts the overall topology, orientation of the protein and presenence of signal peptides in each case correctly. This is a very good example, that a combined signal peptide and transmembrane prediction is more reliable and makes less errors than a single transmembrane prediction.

SignalP

If a signal peptide is present in the protein, SignalP predicts it with a very high confidence, both with the neural networks and the hidden Markov models. The presence of a signal peptide is predicted correctly for the proteins RET4_HUMAN, INSL5_HUMAN, LAMP1_HUMAN and A4_HUMAN. The results of the neural network and the hidden Markov models differed for the protein BACT_HALSA which does not have a signal peptide. The S-Score of the neural networks indicates that there is a signal peptide and that the corresponding cleavage site is between position 38 and 39. In contrast, the hidden Marcov models predict a signal anchor.

Protein	Signal Anchor Prob.	Signal Peptide Prob.	Cleavage Site Prob.	Cleavage Site
BACR_HALSA	0.86	0.02	0.00	15-16
RET4_HUMAN	0.00	1.00	0.98	18-19
INSL5_HUMAN	0.00	1.00	0.91	22-23
LAMP1_HUMAN	0.00	1.00	0.85	28-29
A4_HUMAN	0.00	1.00	0.99	17-18

TargetP

TargetP predicts present signal peptides (for proteins RET4_HUMAN, INSL5_HUMAN, LAMP1_HUMAN and A4_HUMAN) correctly and with a very high (1-2) reliability class in each case. The method predicts a signal peptide for BACR_HALSA as well, but the reliablility class is in this case very low (4), which indicates that the prediction is not very safe.

Name Len mTP SP other Loc RC TPlen sp_P02945_BACR_HALSA 262 0.019 0.897 0.562 S 4 116 sp_P02753_RET4_HUMAN 201 0.242 0.928 0.020 S 2 18 sp_Q9Y5Q6_INSL5_HUMA 135 0.074 0.899 0.037 S 1 22 sp_P11279_LAMP1_HUMA 417 0.043 0.953 0.017 S 1 28 sp_P05067_A4_HUMAN 770 0.035 0.937 0.084 S 1 17 cutoff 0.000 0.000 0.000

Discussion

The application of the different tools for transmembrane region and signal peptide prediction to a variety of proteins shows that predictors which combine both elements are more reliable and make less errors coming from misclassifications than single predictors. Therefore, if possible, a predictor for both, transmembrane segment and signal peptide like SPOCTOPUS or Phobius, should be used.

Prediction of GO terms

General

The Gene Ontology Consortium tries to unify the terminology of gene and gene product attributes across all species to decrease non-consistent descriptions in different databases. It therefore has developed three different ontologies: cellular component, molecular function and biological process. <ref>http://www.geneontology.org/GO.doc.shtml</ref> For each prediction, precision and recall are calculated by comparing the paths to the root in the GO-tree of the predicted and the reference terms. For the GO-tree, the transitive closure<ref>http://www.geneontology.org/scratch/transitive_closure/go_transitive_closure.links</ref> of june 2008 is used. This file is not up to date and therefore does not contain each GO-term, but still helps to give an impression of how good the predictions are.

Annotation of Glucocerebrosidase

The following GO-term annotations are taken from Uniprot<ref>http://www.ebi.ac.uk/QuickGO/GProtein?ac=P04062</ref> and are used as reference for a comparison with the different prediction tools.

Accession	Term	Ontology
GO:0005975	carbohydrate metabolic process	biological process
GO:0008219	cell death	biological process
GO:0006629	lipid metabolic process	biological process
GO:0007040	lysosome organization	biological process
GO:0006665	sphingolipid metabolic process	biological process
GO:0008152	metabolic process	biological process
GO:0005765	lysosomal membrane	cellular component
GO:0016020	membrane	cellular component
GO:0005764	lysosome	cellular component
GO:0043169	cation binding	molecular function
GO:0003824	catalytic activity	molecular function
GO:0004348	glucosylceramidase activity	molecular function
GO:0016798	hydrolase activity, acting on glycosyl bonds	molecular function
GO:0016787	hydrolase activity	molecular function
GO:0005515	protein binding	molecular function

GOPET

GOPET, a Gene Ontology term Prediction and Evaluation Tool, uses homology searches and Support Vector Machines to predict the molecular function GO-terms for sequences of any organism. It was made public in 2006 by Vinayagam et al. <ref>Vinayagam A., et al. GOPET: A tool for automated predictions of Gene Ontology terms. BMC Bioinformatics. 2006; 7: 161.</ref>

Usage

Webserver: http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar
Input: protein sequence in fasta format

Results

GOPET predicts 3 different GO-terms for glucocerebrosidase. The annotations made by GOPET are correct: each predicted GO-term is listed in the corresponding Uniprot entry of glucocerebrosidase. GOPET does not predict two molecular function GO-terms: protein binding and cation binding. In total, GOPET achieves a recall of 0.67 and a precision of 1.00 if only the GO-terms corresponding to molecular function are taken into account.

GOid	Aspect	Confidence	GO term
GO-ID:0016787	F	98%	hydrolase activity
GO-ID:0004348	F	97%	glucosylceramidase activity
GO-ID:0016798	F	97%	hydrolase activity acting on glycosyl bonds

Pfam

The database Pfam contains protein families and domains and is based on hidden Markov models. It consists of two parts: Pfam-A is curated and therefore contains high quality data whereas Pfam-B is generated automatically. Pfam was presented by Sonnhammer et al. in 1997. <ref>Sonnhammer E., et al., Pfam: A Comprehensive Database of Protein Domain Families Based on Seed Alignments. PROTEINS: Structure, Function, and Genetics 28:405-420(1997)</ref>

Usage

Database search: http://pfam.sanger.ac.uk/search
Input: protein sequence in fasta format
use families of significant Pfam-A matches to search in thepfam2go<ref>http://www.geneontology.org/external2go/pfam2go</ref> file for corresponding GO-terms

Results
Pfam assigns glucocerebrosidase to the "O-Glycosyl hydrolase family 30". To retrieve the GO-annotations for this family, the pfam2go file <ref>http://www.geneontology.org/external2go/pfam2go</ref> of the Gene Ontology website had to be used. Each GO-term listed in Pfam is listed in the corresponding Uniprot site as well. This results in a precision of 1.00 and a recall of 0.65.

Accession	Term	Ontology
GO:0004348	glucosylceramidase activity	molecular function
GO:0006665	sphingolipid metabolic process	biological process
GO:0007040	lysosome organization	biological process
GO:0005764	lysosome	cellular component

ProtFun 2.2

ProtFun is an ab initio prediction server for protein function which is based on sequence derived protein features as predicted post translational modifications, protein sorting signals and phisical/chemical properties that have been calculated from the amino acid composition of the input sequence. <ref>Jehnsen L., et al. Ab initio prediction of human orphan protein function from post-translational modifications and localization features. J. Mol. Biol., 319:1257-1265, 2002</ref>

Usage

Webserver: http://www.cbs.dtu.dk/services/ProtFun/
Input: protein sequence in fasta format

Results
ProtFun assigns glucocerebrosidase to immune response (GO:0006955) which is not tue. Therefore the results of ProtFun have a recall and a precission of 0.00.

 Functional category                  Prob     Odds
 Amino_acid_biosynthesis              0.035    1.593
 Biosynthesis_of_cofactors            0.182    2.528
 Cell_envelope                     => 0.504    8.262
 Cellular_processes                   0.032    0.438
 Central_intermediary_metabolism      0.382    6.063
 Energy_metabolism                    0.067    0.740
 Fatty_acid_metabolism                0.027    2.088
 Purines_and_pyrimidines              0.538    2.213
 Regulatory_functions                 0.031    0.191
 Replication_and_transcription        0.126    0.471
 Translation                          0.082    1.863
 Transport_and_binding                0.560    1.365

 Enzyme/nonenzyme                     Prob     Odds
 Enzyme                            => 0.773    2.698
 Nonenzyme                            0.227    0.318

 Enzyme class                         Prob     Odds
 Oxidoreductase (EC 1.-.-.-)          0.083    0.399
 Transferase    (EC 2.-.-.-)          0.228    0.660
 Hydrolase      (EC 3.-.-.-)          0.272    0.859
 Lyase          (EC 4.-.-.-)          0.045    0.961
 Isomerase      (EC 5.-.-.-)          0.011    0.345
 Ligase         (EC 6.-.-.-)          0.017    0.332

 Gene Ontology category               Prob     Odds
 Signal_transducer                    0.054    0.251
 Receptor                             0.027    0.158
 Hormone                              0.001    0.206
 Structural_protein                   0.002    0.087
 Transporter                          0.024    0.222
 Ion_channel                          0.018    0.307
 Voltage-gated_ion_channel            0.004    0.195
 Cation_channel                       0.012    0.268
 Transcription                        0.070    0.550
 Transcription_regulation             0.030    0.237
 Stress_response                      0.085    0.962
 Immune_response                   => 0.153    1.804
 Growth_factor                        0.005    0.376
 Metal_ion_transport                  0.009    0.020

Further Examples of Use

The different prediction tools have also been applied to the additional proteins already used in the transmembrane and signal peptide prediction section. The reference annotations and the predictions are listed here, and the resulting precision and recall values are listed in the table below. This time, recall and precision of GOPet have been calculated by a comparison to all annotated GO-terms of the corresponding proteins, not only to the ones being a molecular function. This may be an explanation for the low values.

TODO: ProtFun, wenn Server wieder geht + File einfügen

	Bacteriorhodopsin	Retinol-binding protein 4	Insulin-like peptide INSL5	Lysosome-associated membrane glycoprotein 1	Amyloid beta A4 protein
GOPet
Precision	0.62	0.62	1.00	0.00	0.70
Recall	0.21	0.04	0.57	0.00	0.05
Pfam
Precision	1.00	1.00	1.00	1.00	1.00
Recall	0.26	0.01	0.71	0.07	0.03
ProtFun 2.2
Precision
Recall

References

@@ Line 149: / Line 149: @@
 === Jpred3 ===
-Jpred3 was published 2008 by Christian Cole, Jonathan D. Barber and Geoffrey J. Barton as "The Jpred 3 secondary structure prediction server" in Nucl. Acids Res. The Jnet algorithm predicts the secondary structure and solvent accessibility with the help of alignment profiles. Therefore it uses the position-specific scoring matrix (PSSM) from PSI-BLAST and a hidden Markov model. The prediction is made with a neural network.<ref>http://nar.oxfordjournals.org/content/36/suppl_2/W197.full</ref><br/>
+Jpred3 was published in 2008 by Christian Cole, Jonathan D. Barber and Geoffrey J. Barton as "The Jpred 3 secondary structure prediction server" in Nucl. Acids Res. The Jnet algorithm predicts the secondary structure and the solvent accessibility of a protein with the help of alignment profiles. Therefore it uses the position-specific scoring matrix (PSSM) created by PSI-BLAST and a hidden Markov model. The final prediction is made with a neural network.<ref>http://nar.oxfordjournals.org/content/36/suppl_2/W197.full</ref><br/>
-As input only the protein sequence is needed. Alternatively you can also use a multiple sequence alignment.
+As input, either the protein sequence or a multiple sequence alignment is taken.
+''' Results '''
+Jpred3 predicts the majority of the secondary structure elements correctly. Only rarely sheets and helices are mixed up, secondary structures overlooked and regions assigned to a certain structure by mistake.
+For the prediction a lot of BLAST-Hits with an E-value of 0 and one Hit with an E-Value of 2e-52 were used: 2wkl, 3keh, 3ke0, 3gxm, 3gxi, 3gxf, 3gxd, 2wcg, 2vt0, 2v3f, 2v3e, 2v3d, 2nt1, 2nt0, 2nsx, 2j25, 2f61, 1y7v, 1ogs, 2wnw. Of these proteins, all but 2wnw are glucocerebrosidase proteins of Homo Sapiens. The latter is a hydrolyse activated by a transcription factor from salmonella typhimurium, which seems to be a glucocerebrosidase as well. Some examples of the used proteins are shown in the pictures bellow.
 {| border="0" style="text-align:center; border-spacing:0;text-align:left;"
@@ Line 224: / Line 230: @@
 |}
-Jpred3 predicts the bigger part of the protein correctly. Some parts are not predicted, where are helices or beta sheets and only one time it predicted sheets instead of helices.<br/>
-For the prediction it used a lot of hits of Blast with an E-value of 0, and also one with an E-value of 2e-52:<br/>
-wkl, 3keh, 3ke0, 3gxm, 3gxi, 3gxf, 3gxd, 2wcg, 2vt0, 2v3f, 2v3e, 2v3d, 2nt1, 2nt0, 2nsx, 2j25, 2f61, 1y7v, 1ogs, 2wnw.<br/>
-All but 2wnw are glucoceramidase proteins of Homo Sapiens, 1ogs is exactly that we use. 2wnw is a hydrolyse activated by transcription factor from salmonella typhimurium. It seems to be also glucoceramidase. Some examples of the used proteins are shown in the following pictures.<br/>
 {| class="centered"

Difference between revisions of "Glucocerebrosidase sequence based prediction"

Revision as of 12:01, 4 June 2011

Contents

Secondary structure prediction

General

PSIPRED

Jpred3

Comparison with DSSP

Prediction of disordered regions

General

DISOPRED

POODLE

POODLE-I

POODLE-S

POODLE-L

IUPred

IUPred: short disordered regions

IUPred: long disordered regions

IUPred: structured regions

META-Disorder

Prediction of transmembrane alpha-helices and signal peptides

General

Transmembrane topology

Signal peptides

Combined transmembrane and signal peptide prediction

TMHMM

Phobius and PolyPhobius

OCTOPUS and SPOCTOPUS

SignalP

TargetP

Further Examples of Use

Topology

TMHMM

Phobius and PolyPhobius

OCTOPUS

SPOCTOPUS

SignalP

TargetP

Discussion

Prediction of GO terms

General

Annotation of Glucocerebrosidase

GOPET

Pfam

ProtFun 2.2

Further Examples of Use

References

Navigation menu

Search