 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q99758 from www.uniprot.org...
The NucPred score for your sequence is 0.45 (see score help below)
1 MAVLRQLALLLWKNYTLQKRKVLVTVLELFLPLLFSGILIWLRLKIQSEN 50
51 VPNATIYPGQSIQELPLFFTFPPPGDTWELAYIPSHSDAAKTVTETVRRA 100
101 LVINMRVRGFPSEKDFEDYIRYDNCSSSVLAAVVFEHPFNHSKEPLPLAV 150
151 KYHLRFSYTRRNYMWTQTGSFFLKETEGWHTTSLFPLFPNPGPREPTSPD 200
201 GGEPGYIREGFLAVQHAVDRAIMEYHADAATRQLFQRLTVTIKRFPYPPF 250
251 IADPFLVAIQYQLPLLLLLSFTYTALTIARAVVQEKERRLKEYMRMMGLS 300
301 SWLHWSAWFLLFFLFLLIAASFMTLLFCVKVKPNVAVLSRSDPSLVLAFL 350
351 LCFAISTISFSFMVSTFFSKANMAAAFGGFLYFFTYIPYFFVAPRYNWMT 400
401 LSQKLCSCLLSNVAMAMGAQLIGKFEAKGMGIQWRDLLSPVNVDDDFCFG 450
451 QVLGMLLLDSVLYGLVTWYMEAVFPGQFGVPQPWYFFIMPSYWCGKPRAV 500
501 AGKEEEDSDPEKALRNEYFEAEPEDLVAGIKIKHLSKVFRVGNKDRAAVR 550
551 DLNLNLYEGQITVLLGHNGAGKTTTLSMLTGLFPPTSGRAYISGYEISQD 600
601 MVQIRKSLGLCPQHDILFDNLTVAEHLYFYAQLKGLSRQKCPEEVKQMLH 650
651 IIGLEDKWNSRSRFLSGGMRRKLSIGIALIAGSKVLILDEPTSGMDAISR 700
701 RAIWDLLQRQKSDRTIVLTTHFMDEADLLGDRIAIMAKGELQCCGSSLFL 750
751 KQKYGAGYHMTLVKEPHCNPEDISQLVHHHVPNATLESSAGAELSFILPR 800
801 ESTHRFEGLFAKLEKKQKELGIASFGASITTMEEVFLRVGKLVDSSMDIQ 850
851 AIQLPALQYQHERRASDWAVDSNLCGAMDPSDGIGALIEEERTAVKLNTG 900
901 LALHCQQFWAMFLKKAAYSWREWKMVAAQVLVPLTCVTLALLAINYSSEL 950
951 FDDPMLRLTLGEYGRTVVPFSVPGTSQLGQQLSEHLKDALQAEGQEPREV 1000
1001 LGDLEEFLIFRASVEGGGFNERCLVAASFRDVGERTVVNALFNNQAYHSP 1050
1051 ATALAVVDNLLFKLLCGPHASIVVSNFPQPRSALQAAKDQFNEGRKGFDI 1100
1101 ALNLLFAMAFLASTFSILAVSERAVQAKHVQFVSGVHVASFWLSALLWDL 1150
1151 ISFLIPSLLLLVVFKAFDVRAFTRDGHMADTLLLLLLYGWAIIPLMYLMN 1200
1201 FFFLGAATAYTRLTIFNILSGIATFLMVTIMRIPAVKLEELSKTLDHVFL 1250
1251 VLPNHCLGMAVSSFYENYETRRYCTSSEVAAHYCKKYNIQYQENFYAWSA 1300
1301 PGVGRFVASMAASGCAYLILLFLIETNLLQRLRGILCALRRRRTLTELYT 1350
1351 RMPVLPEDQDVADERTRILAPSPDSLLHTPLIIKELSKVYEQRVPLLAVD 1400
1401 RLSLAVQKGECFGLLGFNGAGKTTTFKMLTGEESLTSGDAFVGGHRISSD 1450
1451 VGKVRQRIGYCPQFDALLDHMTGREMLVMYARLRGIPERHIGACVENTLR 1500
1501 GLLLEPHANKLVRTYSGGNKRKLSTGIALIGEPAVIFLDEPSTGMDPVAR 1550
1551 RLLWDTVARARESGKAIIITSHSMEECEALCTRLAIMVQGQFKCLGSPQH 1600
1601 LKSKFGSGYSLRAKVQSEGQQEALEEFKAFVDLTFPGSVLEDEHQGMVHY 1650
1651 HLPGRDLSWAKVFGILEKAKEKYGVDDYSVSQISLEQVFLSFAHLQPPTA 1700
1701 EEGR 1704
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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