Citrus Sinensis ID: 035871

Local Sequence Feature Prediction

Prediction and MethodResult
Residue Number Marker
Protein Sequence ?
Secondary Structure (Consensus) ?
Disordered Region (Consensus) ?
Transmembrane Helix (Consensus) ?
Signal Peptide (Consensus) ?
Coiled Coil (COILS) ?
 
--------10--------20--------30--------40--------50--------60--------70--------80--------90-------100-------110-------120-------130-------140-------150-------160-------170-------180-------190-------200-------210-------220-------230-------240-------250-------260-------270-------280-------290-------300-------310-------320-------330-------340-------350-------360-------370-------380-------390-------400-------410-------420-------430-------440-------450-------460-------470-------480-------490-------500-------510-------520-------530-------540-------550-------560-------570-------580-------590-------600-------610-------620-------630-------640-------650-------660-------670-------680-------690-------700-------710-------720-------730-------740-------750-------760-------770-------780-------790-------800-------810-------820-------830-------840-------850-------860-------870-------880-------890-------900-------910-------920-------930-------940-------950-------960-------970-------980-------990------1000------1010------1020------1030------1040------1050------1060------1070------1080------1090------1100------1110------1120------113
MATSSATTAVRSWRTAFLTLRDETSSLSGCPSVSQLLHDVLFSNLHSLVYAASDLPPHEVTSDLLFLLELVSNAPPRDGEDSTLTFTHTCHLVHGILQRVSFEFNSSSFNLILNSFQSIINFFLVKAATKSSATRFKPVMQCLETTRCLTNVYQGKFSPLEIVHLVKFVLHALECSHAEFVCLYNASATQRSTAESGKRLHRYSSLWEVQALSITMLGEAFSRAGSSLPVDIWQSTIEVLRKVIDVIASKSVLGEDSILSSRFYSSLLNCLHVVLTDPKISLSDHVSGFVTALRLFFVYGLTSSPQFTFPAVGHKEVSPNLPSEEPKKIDHTPYRPPHLRKKDRLNIKQSKPQDHRIFSDDDSFTMNFMSSDSDYSDSDGSIKDTDSVQSSKVRVAALVCLQDLCRADPKSFTTQWTILLPTNDVLRPRKFEATLMTCLLFDPCLKARMASASTLAAMLDGPSTVFLQVAEYKESIKCGSFMPLSTSYGHIIMQLHNGIIYLIQRETHDRLLASLFKILMPLISCTPYSRMPGELMPNLIISLRARIEEGFPLKTDQTGLLVAAISCLTAALSTSPAPVQVKQMFLEEISAGSVEVDKRSGVLFTLLQCSERLASPAICFESLQALRAVSHNYPNIMSSYWQQVSTIVFKILKAASPEVPAKAWKGHVGNTAGFIGEKVVTAAIKVLDESLRAISGFKGTEDLLDDKLLDNPFTSDCIRIKNISSAPLYEQESSEDIKESAKAFQSGSEQWSEMIEKHMPLILQHISSMVRTAAVTCFAGITSSVFFSLLKETQEFIISSLIDSALHDDVASVRSAACRAIGVISCFPQVSQSAEIIDKFIHAVEINTHDPLVSVRITASWALANICDSIRHCIDDFAFKPSIDSNANSHLMASLTESALNLTKDGDKIKSNAVRGLGNLSRFVKYTSSSHPASLGDSRWLERIVQALVSCVTTGNVKVQWNVCRALSNLFLNETINLEDMDWAPSVFSILLLLLRDSSNFKIRIQAAAALAVPSSVSDYGKSFSDVVQGLEHILENLGADHLSAPSSFKYRVALQKQKSSFLEEWFKVLCSSLGESTTHLENENNSVGNQKKEMISKAIRSLIEVYEGRKQFAVAKKFEMMDSGIW
ccccccccccHHHHHHHHccccccccccccccHHHHHHHHHHccccccccccccccccHHHHHHHHHHHHHHccccccccccccHHHHHHHHHHHHHHHEEEEEccccHHHHHHHHHHHHHHHHHHHHcccccccccHHHHHHHHHHHHHHHcccccccHHHHHHHHHHHHHHHcccHHHHHcccccccccccccccccccccccHHHHHHHHHHHHHHHHccccccccHHHHHHHHHHHHHHHHHHHcccccccHHHHHHHHHHHHHHHHHHHHccccccccccHHHHHHHHHHHHHccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccHHHHHHHHHHHHHHHHHHHHccccccccccccccccccccccccccHHHHHHHHccccHHHHHHHHHHHHHHHccccHHHHHHHHHHcccccccccccHHHHHHHHHHHHHHHHHHHHHHcHHHHHHHHHHHHHHHHHcccccccccccHHHHHHHHHHHHHcccccccccHHHHHHHHHHHHHHHccccccHHHHHHHHHHHccccccccccHHHHHHHHHHHHccccccHHHHHHHHHHHHHccccccHHHHHHHHHHHHHHHHHccccccHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHccccccccHHHHHcccccccHHHHHHHccccccHHccccHHHHHHHHHHHcccHHHHHHHHHHHHHHHccccHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHccccccHHHHHHHHHHHHHHHcccccccHHHHHHHHHHHHHHHcccccccHHHHHHHHHHHHHHHHHHHHccccccccccccccHHHHHHHHHHHHHcccccHHHHHHHHHHHHHHHHHHcccccccccccccHHHHHHHHHHHHHHHcccccHHHHHHHHHHHHHHccccccccccccHHHHHHHHHHHHHccccHHHHHHHHHHHccccccccccccHHHHHHHHHHHHHHccccccccccccHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHcccccHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHccc
**********RSWRTAFLTLRDETSSLSGCPSVSQLLHDVLFSNLHSLVYAASDLPPHEVTSDLLFLLELVSNAPPRDGEDSTLTFTHTCHLVHGILQRVSFEFNSSSFNLILNSFQSIINFFLVKAATKSSATRFKPVMQCLETTRCLTNVYQGKFSPLEIVHLVKFVLHALECSHAEFVCLY****************HRYSSLWEVQALSITMLGEAFSRAGSSLPVDIWQSTIEVLRKVIDVIASKSVLGEDSILSSRFYSSLLNCLHVVLTDPKISLSDHVSGFVTALRLFFVYGLTSSPQFTFP**********************************************************************************VRVAALVCLQDLCRADPKSFTTQWTILLPTNDVLRPRKFEATLMTCLLFDPCLKARMASASTLAAMLDGPSTVFLQVAEYKESIKCGSFMPLSTSYGHIIMQLHNGIIYLIQRETHDRLLASLFKILMPLISCTPYSRMPGELMPNLIISLRARIEEGFPLKTDQTGLLVAAISCLTAALSTSPAPVQVKQMFLEEISAGSVEVDKRSGVLFTLLQCSERLASPAICFESLQALRAVSHNYPNIMSSYWQQVSTIVFKILKAASPEVPAKAWKGHVGNTAGFIGEKVVTAAIKVLDESLRAISGFKGTEDLLDDKLLDNPFTSDCIRIKNISSAPLYEQESSEDIKESAKAFQSGSEQWSEMIEKHMPLILQHISSMVRTAAVTCFAGITSSVFFSLLKETQEFIISSLIDSALHDDVASVRSAACRAIGVISCFPQVSQSAEIIDKFIHAVEINTHDPLVSVRITASWALANICDSIRHCIDDFAFKPSIDSNANSHLMASLTESALNLTKDGDKIKSNAVRGLGNLSRFVKYTSSSHPASLGDSRWLERIVQALVSCVTTGNVKVQWNVCRALSNLFLNETINLEDMDWAPSVFSILLLLLRDSSNFKIRIQAAAALAVPSSVSDYGKSFSDVVQGLEHILENLGADHLSAPSSFKYRVALQKQKSSFLEEWFKVLCSSLGESTTHLEN***********MISKAIRSLIEVYEGRKQFAVAKKFEMMDS*I*
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MATSSATTAVRSWRTAFLTLRDETSSLSGCPSVSQLLHDVLFSNLHSLVYAASDLPPHEVTSDLLFLLELVSNAPPRDGEDSTLTFTHTCHLVHGILQRVSFEFNSSSFNLILNSFQSIINFFLVKAATKSSATRFKPVMQCLETTRCLTNVYQGKFSPLEIVHLVKFVLHALECSHAEFVCLYNASATQRSTAESGKRLHRYSSLWEVQALSITMLGEAFSRAGSSLPVDIWQSTIEVLRKVIDVIASKSVLGEDSILSSRFYSSLLNCLHVVLTDPKISLSDHVSGFVTALRLFFVYGLTSSPQFTFPAVGHKEVSPNLPSEEPKKIDHTPYRPPHLRKKDRLNIKQSKPQDHRIFSDDDSFTMNFMSSDSDYSDSDGSIKDTDSVQSSKVRVAALVCLQDLCRADPKSFTTQWTILLPTNDVLRPRKFEATLMTCLLFDPCLKARMASASTLAAMLDGPSTVFLQVAEYKESIKCGSFMPLSTSYGHIIMQLHNGIIYLIQRETHDRLLASLFKILMPLISCTPYSRMPGELMPNLIISLRARIEEGFPLKTDQTGLLVAAISCLTAALSTSPAPVQVKQMFLEEISAGSVEVDKRSGVLFTLLQCSERLASPAICFESLQALRAVSHNYPNIMSSYWQQVSTIVFKILKAASPEVPAKAWKGHVGNTAGFIGEKVVTAAIKVLDESLRAISGFKGTEDLLDDKLLDNPFTSDCIRIKNISSAPLYEQESSEDIKESAKAFQSGSEQWSEMIEKHMPLILQHISSMVRTAAVTCFAGITSSVFFSLLKETQEFIISSLIDSALHDDVASVRSAACRAIGVISCFPQVSQSAEIIDKFIHAVEINTHDPLVSVRITASWALANICDSIRHCIDDFAFKPSIDSNANSHLMASLTESALNLTKDGDKIKSNAVRGLGNLSRFVKYTSSSHPASLGDSRWLERIVQALVSCVTTGNVKVQWNVCRALSNLFLNETINLEDMDWAPSVFSILLLLLRDSSNFKIRIQAAAALAVPSSVSDYGKSFSDVVQGLEHILENLGADHLSAPSSFKYRVALQKQKSSFLEEWFKVLCSSLGESTTHLENENNSVGNQKKEMISKAIRSLIEVYEGRKQFAVAKKFEMMDSGIW

Function Prediction

Annotation transfered from Closely Related SWISS-PROT Entries ?

No confident close homologs for annotation transfering were detected in SWISSPROT

Prediction of Enzyme Commission Number ?

No confident prediction of EC number!

Spatial Structural Prediction

Structural Models Based on Templates

Template: 1B3U, chain A
Confidence level:very confident
Coverage over the Query: 437-583,601-659,677-701,729-730,747-873,886-927,939-1024
View the alignment between query and template
View the model in PyMOL
Template: 1B3U, chain A
Confidence level:very confident
Coverage over the Query: 392-420,434-465,490-583,601-659,677-694,730-732,749-873,886-927,939-1014
View the alignment between query and template
View the model in PyMOL
Template: 1JDH, chain A
Confidence level:very confident
Coverage over the Query: 496-586,599-659,677-702,729-1003,1015-1039
View the alignment between query and template
View the model in PyMOL
Template: 1U6G, chain C
Confidence level:very confident
Coverage over the Query: 391-421,434-475,490-575,596-659,677-726,766-873,887-1036
View the alignment between query and template
View the model in PyMOL
Template: 1U6G, chain C
Confidence level:very confident
Coverage over the Query: 392-421,434-462,481-596,614-659,671-697,732-1013
View the alignment between query and template
View the model in PyMOL
Template: 1WA5, chain C
Confidence level:probable
Coverage over the Query: 617-658,674-732,747-870,888-1049
View the alignment between query and template
View the model in PyMOL
Template: 1LSH, chain A
Confidence level:probable
Coverage over the Query: 768-921,937-1042,1056-1074,1090-1107
View the alignment between query and template
View the model in PyMOL