 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P58871 from www.uniprot.org...
The NucPred score for your sequence is 0.78 (see score help below)
1 MKGSTLREGTAMASPLPQDMEEELAPVGSEPGDPRAKPPVKPKPRGLPSK 50
51 PALPAKPSLLVPVGPRPPRGPLAELPSARKMNMLAGPQPYGVSKRPLPFA 100
101 PRPSAEATAGGDVTQESGKEDAGKEDLPPLTPPARCAALGGVRKAPAPFR 150
151 PSSERFAACTVEEILAKMEQPRKEILASPDRLWGSRLTFNHDGSSRYGPR 200
201 TYGAPCPREEDSKSPAKGRSQEGTAEIPAECQEEHSKTPEERNLTSSPAM 250
251 NGDLAKLACSEAPTDVSKTWVTSSADPVSEHGGSTSAVRLANISVPASES 300
301 PRLSSRPSSPCHSQLSETQSPAASEASSICLPVTPASPSAVLPAEPPGHS 350
351 PSSELPAEAAPETLSPNSSPVETVSGHHSPEQPPVLLPQLLTEGAELPDI 400
401 TRTFPCGEEAAARGHTESRPSSLAQRRFSEGVLQPPSQDQEKLGGSLATL 450
451 PQGQGSQSALDRPFGSGTESNWSLSQSFEWTFPTRPSGLGVWRLDSPPPS 500
501 PITEASEAAEAAEADSWAVSGRGEGVSQVGPGTPPAPESPRKPISGVQGN 550
551 DPGISLPQRDDGESQPRSPALLPSTVEGPPGAPLLQAKENYEDQEPLVGH 600
601 ESPITLAAREAALPVLEPALGQQQPTPSDQPCILFVDVPDPEQALSTEED 650
651 VVTLGWAETTLPMTEAQEPCSVSPEPTGPESSSRWLDDLLASPPPNSGSA 700
701 RRAAGAELKDRQSPSTCSEGLLGWAQKDLQSEFGVATDSHHSSFGSSSWS 750
751 QDTSQNYSLGGRSPVGDTGLGKRDWSSKCGQGSGEGSTREWASRHSLGQE 800
801 VIGIGGSQDESEVPVRERAVGRPAQLGAQGLEADAQQWEFGKRESQDPHS 850
851 IHDKELQDQEFGKRDSLGSFSTRDASLQDWEFGKRASVSTNQDTDENDQE 900
901 LGMKNLSRGYSSQDAEEQDREFEKRDSVLDIHGSRATAQQNQEFGKSAWF 950
951 QDYSSGGGGSRVLGSQERGFGIRSLSSGFSPEEAQQQDEEFEKKTPVGED 1000
1001 RFCEASRDVGHLEEGASGGLLSPSTPHSRDGAARPKDEGSWQDGDSSQEI 1050
1051 TRLQGRMQAESQSPTNVDLEDKEREQRGWAGEFSLGVAAQSEAAFSPGRQ 1100
1101 DWSRDVCVEASESSYQFGIIGNDRVSGAGLSPSRKSGGGHFVPPGETKAG 1150
1151 AVDWTDQLGLRNLEVSSCVSSEGPSEARENVVGQMGWSDSLGLNNGDLAR 1200
1201 RLGTGESEEPRSLGVGEKDWTSSVEARNRDLPGQAEVGRHSQARESGVGE 1250
1251 PDWSGAEAGEFLKSRERGVGQADWTPDLGLRNMAPGAGCSPGEPRELGVG 1300
1301 QVDWGDDLGLRNLEVSCDLESGGSRGCGVGQMDWAQDLGLRNLRLCGAPS 1350
1351 EVRECGVGRVGPDLELDPKSSGSLSPGLETEDPLEARELGVGEISGPETQ 1400
1401 GEDSSSPSFETPSEDTGMDTGEAPSLGASPSSCLTRSPPSGSQSLLEGIM 1450
1451 TASSSKGAPQRESAASGSRVLLEEEGLAAGAGQGEPQEPSRAPLPSSRPQ 1500
1501 PDGEASQVEEVDGTWSLTGAARQNEQASAPPPRRPPRGLLPSCPSEDFSF 1550
1551 IEDTEILDSAMYRSRANLGRKRGHRAPAIRPGGTLGLSETADSDTRLFQD 1600
1601 STEPRASRVPSSDEEVVEEPQSRRTRMSLGTKGLKVNLFPGLSPSALKAK 1650
1651 LRSRNRSAEEGEVTESKSSQKESSVQRSKSCKVPGLGKPLTLPPKPEKSS 1700
1701 GSEGSSPNWLQALKLKKKKI 1720
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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