 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P52948 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MFNKSFGTPFGGGTGGFGTTSTFGQNTGFGTTSGGAFGTSAFGSSNNTGG 50
51 LFGNSQTKPGGLFGTSSFSQPATSTSTGFGFGTSTGTANTLFGTASTGTS 100
101 LFSSQNNAFAQNKPTGFGNFGTSTSSGGLFGTTNTTSNPFGSTSGSLFGP 150
151 SSFTAAPTGTTIKFNPPTGTDTMVKAGVSTNISTKHQCITAMKEYESKSL 200
201 EELRLEDYQANRKGPQNQVGAGTTTGLFGSSPATSSATGLFSSSTTNSGF 250
251 AYGQNKTAFGTSTTGFGTNPGGLFGQQNQQTTSLFSKPFGQATTTQNTGF 300
301 SFGNTSTIGQPSTNTMGLFGVTQASQPGGLFGTATNTSTGTAFGTGTGLF 350
351 GQTNTGFGAVGSTLFGNNKLTTFGSSTTSAPSFGTTSGGLFGNKPTLTLG 400
401 TNTNTSNFGFGTNTSGNSIFGSKPAPGTLGTGLGAGFGTALGAGQASLFG 450
451 NNQPKIGGPLGTGAFGAPGFNTTTATLGFGAPQAPVALTDPNASAAQQAV 500
501 LQQHINSLTYSPFGDSPLFRNPMSDPKKKEERLKPTNPAAQKALTTPTHY 550
551 KLTPRPATRVRPKALQTTGTAKSHLFDGLDDDEPSLANGAFMPKKSIKKL 600
601 VLKNLNNSNLFSPVNRDSENLASPSEYPENGERFSFLSKPVDENHQQDGD 650
651 EDSLVSHFYTNPIAKPIPQTPESAGNKHSNSNSVDDTIVALNMRAALRNG 700
701 LEGSSEETSFHDESLQDDREEIENNSYHMHPAGIILTKVGYYTIPSMDDL 750
751 AKITNEKGECIVSDFTIGRKGYGSIYFEGDVNLTNLNLDDIVHIRRKEVV 800
801 VYLDDNQKPPVGEGLNRKAEVTLDGVWPTDKTSRCLIKSPDRLADINYEG 850
851 RLEAVSRKQGAQFKEYRPETGSWVFKVSHFSKYGLQDSDEEEEEHPSKTS 900
901 TKKLKTAPLPPASQTTPLQMALNGKPAPPPQSQSPEVEQLGRVVELDSDM 950
951 VDITQEPVLDTMLEESMPEDQEPVSASTHIASSLGINPHVLQIMKASLLT 1000
1001 DEEDVDMALDQRFSRLPSKADTSQEICSPRLPISASHSSKTRSLVGGLLQ 1050
1051 SKFTSGAFLSPSVSVQECRTPRAASLMNIPSTSSWSVPPPLTSVFTMPSP 1100
1101 APEVPLKTVGTRRQLGLVPREKSVTYGKGKLLMDMALFMGRSFRVGWGPN 1150
1151 WTLANSGEQLNGSHELENHQIADSMEFGFLPNPVAVKPLTESPFKVHLEK 1200
1201 LSLRQRKPDEDMKLYQTPLELKLKHSTVHVDELCPLIVPNLGVAVIHDYA 1250
1251 DWVKEASGDLPEAQIVKHWSLTWTLCEALWGHLKELDSQLNEPREYIQIL 1300
1301 ERRRAFSRWLSCTATPQIEEEVSLTQKNSPVEAVFSYLTGKRISEACSLA 1350
1351 QQSGDHRLALLLSQFVGSQSVRELLTMQLVDWHQLQADSFIQDERLRIFA 1400
1401 LLAGKPVWQLSEKKQINVCSQLDWKRSLAIHLWYLLPPTASISRALSMYE 1450
1451 EAFQNTSDSDRYACSPLPSYLEGSGCVIAEEQNSQTPLRDVCFHLLKLYS 1500
1501 DRHYDLNQLLEPRSITADPLDYRLSWHLWEVLRALNYTHLSAQCEGVLQA 1550
1551 SYAGQLESEGLWEWAIFVLLHIDNSGIREKAVRELLTRHCQLLETPESWA 1600
1601 KETFLTQKLRVPAKWIHEAKAVRAHMESDKHLEALCLFKAEHWNRCHKLI 1650
1651 IRHLASDAIINENYDYLKGFLEDLAPPERSSLIQDWETSGLVYLDYIRVI 1700
1701 EMLRHIQQVDCSGNDLEQLHIKVTSLCSRIEQIQCYSAKDRLAQSDMAKR 1750
1751 VANLLRVVLSLHHPPDRTSDSTPDPQRVPLRLLAPHIGRLPMPEDYAMDE 1800
1801 LRSLTQSYLRELAVGSL 1817
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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