Glucocerebrosidase sequence based prediction

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 <ref>http://www.nature.com/horizon/proteinfolding/background/images/importance_f3.gif</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 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.