Glucocerebrosidase sequence based prediction

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Secondary structure prediction

General

The secondary structure of a protein is the three-dimensional form. In contrast to the tertiary structure it describes the local segments. Because of weak chemical forces like hydrogen bonds and the values of the φ and ψ angles they form different structures. The main types are α-helices and parallel and anti-parallel β-sheets. Some rare structures are π-helices and 3,10-helices. Another possibility are coils, which are irregular formed elements.
A protein consists of several secondary structure elements which build together the tertiary structure. <ref>http://en.wikipedia.org/wiki/Biomolecular_structure#Secondary_structure</ref>

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

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 to predict secondary structure. These are based on the position specific scoring matrices generated by PSI-BLAST, which is run before.<ref>David T. Jones, Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices, JMB, 1999</ref>
As input only the protein sequence is needed.

We run the online and the local version of PSIPRED and got different results. In the following it is compared to the secondary structure given in Uniprot<ref>http://www.uniprot.org/uniprot/P04062</ref>.

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

The results differ a lot. The online version of PSIPRED seems to have different parameters than the local version. So we got different results. Compared to the given secondary structure in Uniprot there are many regions that are predicted wrong.

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>
As input only the protein sequence is needed. Alternatively you can also use a multiple sequence alignment.

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----

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.
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:
2wkl, 3keh, 3ke0, 3gxm, 3gxi, 3gxf, 3gxd, 2wcg, 2vt0, 2v3f, 2v3e, 2v3d, 2nt1, 2nt0, 2nsx, 2j25, 2f61, 1y7v, 1ogs, 2wnw.
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.

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

Prediction of transmembrane alpha-helices and signal peptides

General

Transmembrane topology

Signal peptides

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>

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>.

Results

TMHMM posterior probabilities for glucocerebrosidase

Output of TMHMM:
# sp|P04062|GLCM_HUMAN Length: 536

  1. sp|P04062|GLCM_HUMAN Number of predicted TMHs: 0
  2. sp|P04062|GLCM_HUMAN Exp number of AAs in TMHs: 1.77867
  3. sp|P04062|GLCM_HUMAN Exp number, first 60 AAs: 1.62607
  4. 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. Furthermore it localizes the protein correctly by indicating that it is non-cytosolic.



Phobius and PolyPhobius

Phobius

PolyPhobius

OCTOPUS and SPOCTOPUS

OCTOPUS

SOCTOPUS

SignalIP

TargetP

Prediction of GO terms

General

GOPET

Pfam

ProtFun 2.2

References

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