 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10172 from www.uniprot.org...
The NucPred score for your sequence is 0.83 (see score help below)
1 MSYPNQMNGSMYGYGANNGVQPGFDSYGMPINQGGMNYQQQTYPYQQPYQ 50
51 PDGYAGNTMLPFQQSQPATQFNNGFGYASQPTGSVADYGQQQQQMYGYNG 100
101 MMPQTMNNTGFMQPQQTGAIPGFAPQPTGFVQPQPTGFMSQQPASFMQPQ 150
151 RTGGAGFIQPQRTGAMPAYQPQMNNFMQPQKTGGFAPQATGFMQTQPFGA 200
201 APSFAPQPTGFVQPQQTGVVMPPQPTGYLQAQPTGPFASFVQPQQTASFM 250
251 PAAQPLKPQKTGQIHNSKAMDTRLSFVSAADQAKFEQLFKSAVGREEAMS 300
301 SEIGKAILVRSKLPTVQLSKIWRLSDTTRSGRLLFPQFVLAMYLCNLGLT 350
351 GKPIPDKVPDGILNEVNAMVDAISFSLDENYAKPTQPIPQAAAQQMAAQM 400
401 FGGFQQAAGIPSQITGFQPQAMMPQRTGMQPQMTGFQQPMIPQRTGMQPQ 450
451 MTGFQQPMMPQRTGLQPQMTGFQQPMVPQRTGMQPQMTGFQQPMMPQRTG 500
501 LQPQMTGFQQPMVPQRTGMQPMMPGLQQPMAPQRTGMQPMMPQRTGMQPQ 550
551 MTGFQQPMAPQRTGMQPMMPQRTGMQPQMPGMQQPMAPQRTGMQPMMPQR 600
601 TGMQQPMAPQRTGMQPMMPQRTGMQPQMPGMQQPMAPQRTGMQPMMPQRT 650
651 GMQPQMPGMQQPMAPQRTGMQPMAPQRTGMQPMMPQRTGMQPQMPGMQQP 700
701 MAPQRTGMQPMMPQRTGMQPQMPGMQQPMAPQRTGMQPMAPQRTGMQPQM 750
751 TGGPMLPQRTGGMAPQPTGMPGQWGFINTPLSNLPGIEALGQQMMPNAPS 800
801 GGLNNTFQQKKDIPWAISKEEKRIYDQIFDAWDKERKGTLGGNAVLEIFG 850
851 QSKLTRTELEHIWNLCDHGDKGSLDRDEFAVALHLIYRKLNGNEVPAVLP 900
901 PELIPPSTRNFTESLNQVKNLIKNDTSNRKPFGAENQSKLKKNSFYDNPS 950
951 ETTEKDATLYRHNDSDASAYVSSARRRDFKEEKIESAPPIINDIDSEIAS 1000
1001 LKKRIHEKSLVVNALEDKKLAATPANDVQNDSLIYRIKSVQDEINRLSTS 1050
1051 NKSPEVASMNVRLEELSTRVSKMLSDINEVDHTIASLSLKLFQAEDTKNS 1100
1101 YDQTSPEATQERNRTISSKLAEMEKQKNESKAALEQMKNYVTNIENNIRA 1150
1151 KLLPSAANDDAWLSQNVVDESVTRVVKELPVPAPAAPQTLNPPSVSTVQQ 1200
1201 SKPIESNTHTPEVKATSESPSASSNLEDRAARIKAEAQRRMNERLAALGI 1250
1251 KPRQKGTPSPAPVNSATSTPVAAPTAQQIQPGKQASAVSSNVPAVSASIS 1300
1301 TPPAVVPTVQHPQPTKQIPTAAVKDPSTTSTSFNTAPIPQQAPLENQFSK 1350
1351 MSLEPPVRPAVPTSPKPQIPDSSNVHAPPPPVQPMNAMPSHNAVNARPSA 1400
1401 PERRDSFGSVSSGSNVSSIEDETSTMPLKASQPTNPGAPSNHAPQVVPPA 1450
1451 PMHAVAPVQPKAPGMVTNAPAPSSAPAPPAPVSQLPPAVPNVPVPSMIPS 1500
1501 VAQQPPSSVAPATAPSSTLPPSQSSFAHVPSPAPPAPQHPSAAALSSAPA 1550
1551 DNSMPHRSSPYAPQEPVQKPQAINNIAPATNLGTSQSFSPRMGPVNNSGS 1600
1601 PLAMNAAGQPSLAVPAVPSAPSNHFNPFAKMQPPAPSPLQPSGHDSDNWS 1650
1651 QHGDEEEEDSEDDIRSSKDAAALAAKLFGGMAPAHPVSTPPVRPQSAAPP 1700
1701 QMSAPTPPPPPMSVPPPPSAPPMPAGPPSAPPPPLPASSAPSVPNPGDRS 1750
1751 ALLQQIHTGTRLKKTVTTDKSKPIAGRVLDASDGNSSAWYGNLS 1794
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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