 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P35500 from www.uniprot.org...
The NucPred score for your sequence is 0.33 (see score help below)
1 MTEDSDSISEEERSLFRPFTRESLVQIEQRIAAEHEKQKELERKRAEGEV 50
51 PQYGRKKKQKEIRYDDEDEDEGPQPDPTLEQGVPIPVRLQGSFPPELAST 100
101 PLEDIDPYYSNVLTFVVVSKGKDIFRFSASKAMWMLDPFNPIRRVAIYIL 150
151 VHPLFSLFIITTILVNCILMIMPTTPTVESTEVIFTGIYTFESAVKVMAR 200
201 GFILCPFTYLRDAWNWLDFVVIALAYVTMGIDLGNLAALRTFRVLRALKT 250
251 VAIVPGLKTIVGAVIESVKNLRDVIILTMFSLSVFALMGLQIYMGVLTQK 300
301 CIKKFPLDGSWGNLTDENWDYHNRNSSNWYSEDEGISFPLCGNISGAGQC 350
351 DDDYVCLQGFGPNPNYGYTSFDSFGWAFLSAFRLMTQDFWEDLYQLVLRA 400
401 AGPWHMLFFIVIIFLGSFYLVNLILAIVAMSYDELQKKAEEEEAAEEEAI 450
451 REAEEAAAAKAAKLEERANAQAQAAADAAAAEEAALHPEMAKSPTYSCIS 500
501 YELFVGGEKGNDDNNKEKMSIRSVEVESESVSVIQRQPAPTTAHQATKVR 550
551 KVSTTSLSLPGSPFNIRRGSRSSHKYTIRNGRGRFGIPGSDRKPLVLSTY 600
601 QDAQQHLPYADDSNAVTPMSEENGAIIVPVYYGNLGSRHSSYTSHQSRIS 650
651 YTSHGDLLGGMAVMGVSTMTKESKLRNRNTRNQSVGATNGGTTCLDTNHK 700
701 LDHRDYEIGLECTDEAGKIKHHDNPFIEPVQTQTVVDMKDVMVLNDIIEQ 750
751 AAGRHSRASDRGVSVYYFPTEDDDEDGPTFKDKALEVILKGIDVFCVWDC 800
801 CWVWLKFQEWVSLIVFDPFVELFITLCIVVNTMFMAMDHHDMNKEMERVL 850
851 KSGNYFFTATFAIEATMKLMAMSPKYYFQEGWNIFDFIIVALSLLELGLE 900
901 GVQGLSVLRSFRLLRVFKLAKSWPTLNLLISIMGRTMGALGNLTFVLCII 950
951 IFIFAVMGMQLFGKNYHDHKDRFPDGDLPRWNFTDFMHSFMIVFRVLCGE 1000
1001 WIESMWDCMYVGDVSCIPFFLATVVIGNLVVLNLFLALLLSNFGSSSLSA 1050
1051 PTADNDTNKIAEAFNRIGRFKSWVKRNIADCFKLIRNKLTNQISDQPSGE 1100
1101 RTNQISWIWSEGKGVCRCISAEHGDNELELGHDEILADGLIKKGIKEQTQ 1150
1151 LEVAIGDGMEFTIHGDMKNNKPKKSKYLNNATDDDTASINSYGSHKNRPF 1200
1201 KDESHKGSAETMEGEEKRDASKEDLGLDEELDEEGECEEGPLDGDIIIHA 1250
1251 HDEDILDEYPADCCPDSYYKKFPILAGDDDSPFWQGWGNLRLKTFQLIEN 1300
1301 KYFETAVITMILMSSLALALEDVHLPQRPILQDILYYMDRIFTVIFFLEM 1350
1351 LIKWLALGFKVYFTNAWCWLDFVIVMVSLINFVASLVGAGGIQAFKTMRT 1400
1401 LRALRPLRAMSRMQGMRVVVNALVQAIPSIFNVLLVCLIFWLIFAIMGVQ 1450
1451 LFAGKYFKCEDMNGTKLSHEIIPNRNACESENYTWVNSAMNFDHVGNAYL 1500
1501 CLFQVATFKGWIQIMNDAIDSREVDKQPIRETNIYMYLYFVFFIIFGSFF 1550
1551 TLNLFIGVIIDNFNEQKKKAGGSLEMFMTEDQKKYYNAMKKMGSKKPLKA 1600
1601 IPRPRWRPQAIVFEIVTDKKFDIIIMLFIGLNMFTMTLDRYDASDTYNAV 1650
1651 LDYLNAIFVVIFSSECLLKIFALRYHYFIEPWNLFDVVVVILSILGLVLS 1700
1701 DIIEKYFVSPTLLRVVRVAKVGRVLRLVKGAKGIRTLLFALAMSLPALFN 1750
1751 ICLLLFLVMFIFAIFGMSFFMHVKEKSGINDVYNFKTFGQSMILLFQMST 1800
1801 SAGWDGVLDAIINEEACDPPDNDKGYPGNCGSATVGITFLLSYLVISFLI 1850
1851 VINMYIAVILENYSQATEDVQEGLTDDDYDMYYEIWQQFDPEGTQYIRYD 1900
1901 QLSEFLDVLEPPLQIHKPNKYKIISMDIPICRGDLMYCVDILDALTKDFF 1950
1951 ARKGNPIEETGEIGEIAARPDTEGYEPVSSTLWRQREEYCARLIQHAWRK 2000
2001 HKARGEGGGSFEPDTDHGDGGDPDAGDPAPDEATDGDAPAGGDGSVNGTA 2050
2051 EGAADADESNVNSPGEDAAAAAAAAAAAAAAGTTTAGSPGAGSAGRQTAV 2100
2101 LVESDGFVTKNGHKVVIHSRSPSITSRTADV 2131
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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