 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9BXP8 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 MMCLKILRISLAILAGWALCSANSELGWTRKKSLVEREHLNQVLLEGERC 50
51 WLGAKVRRPRASPQHHLFGVYPSRAGNYLRPYPVGEQEIHHTGRSKPDTE 100
101 GNAVSLVPPDLTENPAGLRGAVEEPAAPWVGDSPIGQSELLGDDDAYLGN 150
151 QRSKESLGEAGIQKGSAMAATTTTAIFTTLNEPKPETQRRGWAKSRQRRQ 200
201 VWKRRAEDGQGDSGISSHFQPWPKHSLKHRVKKSPPEESNQNGGEGSYRE 250
251 AETFNSQVGLPILYFSGRRERLLLRPEVLAEIPREAFTVEAWVKPEGGQN 300
301 NPAIIAGVFDNCSHTVSDKGWALGIRSGKDKGKRDARFFFSLCTDRVKKA 350
351 TILISHSRYQPGTWTHVAATYDGRHMALYVDGTQVASSLDQSGPLNSPFM 400
401 ASCRSLLLGGDSSEDGHYFRGHLGTLVFWSTALPQSHFQHSSQHSSGEEE 450
451 ATDLVLTASFEPVNTEWVPFRDEKYPRLEVLQGFEPEPEILSPLQPPLCG 500
501 QTVCDNVELISQYNGYWPLRGEKVIRYQVVNICDDEGLNPIVSEEQIRLQ 550
551 HEALNEAFSRYNISWQLSVHQVHNSTLRHRVVLVNCEPSKIGNDHCDPEC 600
601 EHPLTGYDGGDCRLQGRCYSWNRRDGLCHVECNNMLNDFDDGDCCDPQVA 650
651 DVRKTCFDPDSPKRAYMSVKELKEALQLNSTHFLNIYFASSVREDLAGAA 700
701 TWPWDKDAVTHLGGIVLSPAYYGMPGHTDTMIHEVGHVLGLYHVFKGVSE 750
751 RESCNDPCKETVPSMETGDLCADTAPTPKSELCREPEPTSDTCGFTRFPG 800
801 APFTNYMSYTDDNCTDNFTPNQVARMHCYLDLVYQQWTESRKPTPIPIPP 850
851 MVIGQTNKSLTIHWLPPISGVVYDRASGSLCGACTEDGTFRQYVHTASSR 900
901 RVCDSSGYWTPEEAVGPPDVDQPCEPSLQAWSPEVHLYHMNMTVPCPTEG 950
951 CSLELLFQHPVQADTLTLWVTSFFMESSQVLFDTEILLENKESVHLGPLD 1000
1001 TFCDIPLTIKLHVDGKVSGVKVYTFDERIEIDAALLTSQPHSPLCSGCRP 1050
1051 VRYQVLRDPPFASGLPVVVTHSHRKFTDVEVTPGQMYQYQVLAEAGGELG 1100
1101 EASPPLNHIHGAPYCGDGKVSERLGEECDDGDLVSGDGCSKVCELEEGFN 1150
1151 CVGEPSLCYMYEGDGICEPFERKTSIVDCGIYTPKGYLDQWATRAYSSHE 1200
1201 DKKKCPVSLVTGEPHSLICTSYHPDLPNHRPLTGWFPCVASENETQDDRS 1250
1251 EQPEGSLKKEDEVWLKVCFNRPGEARAIFIFLTTDGLVPGEHQQPTVTLY 1300
1301 LTDVRGSNHSLGTYGLSCQHNPLIINVTHHQNVLFHHTTSVLLNFSSPRV 1350
1351 GISAVALRTSSRIGLSAPSNCISEDEGQNHQGQSCIHRPCGKQDSCPSLL 1400
1401 LDHADVVNCTSIGPGLMKCAITCQRGFALQASSGQYIRPMQKEILLTCSS 1450
1451 GHWDQNVSCLPVDCGVPDPSLVNYANFSCSEGTKFLKRCSISCVPPAKLQ 1500
1501 GLSPWLTCLEDGLWSLPEVYCKLECDAPPIILNANLLLPHCLQDNHDVGT 1550
1551 ICKYECKPGYYVAESAEGKVRNKLLKIQCLEGGIWEQGSCIPVVCEPPPP 1600
1601 VFEGMYECTNGFSLDSQCVLNCNQEREKLPILCTKEGLWTQEFKLCENLQ 1650
1651 GECPPPPSELNSVEYKCEQGYGIGAVCSPLCVIPPSDPVMLPENITADTL 1700
1701 EHWMEPVKVQSIVCTGRRQWHPDPVLVHCIQSCEPFQADGWCDTINNRAY 1750
1751 CHYDGGDCCSSTLSSKKVIPFAADCDLDECTCRDPKAEENQ 1791
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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