 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9U8M0 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MWKGFLCCLLVAGVTSLQEWDPNRQYVYKVESRAFTALHQISNQYAGILM 50
51 RAKLIIQPKTREELHAQLVEVEHSQINKELPSGWDQEIEIHDWQQLSALK 100
101 DAFAIILQNGHIVNVKVKDSTPNWAVNILKGILSTLQVNTQADNLVRHRY 150
151 NILPNASTDSAVYSIRETTITGECEVEYDVSPLPALPLLQHPELAPLSNV 200
201 NDNVIDIEKTQNFSNCKRRPAVHYGLAGIPDLEPGQNQMGDFLARSSVSR 250
251 VVISGTLNKFTVQSSVTTNQVVMSPEMYNSQKGLIVSRVNVTIKDIEEAR 300
301 PIPLPGNLQDTGDLLYSYNNAHDIEPLQRDRQDSDFTLESDSSSSSSSSS 350
351 SDENSWSRDSHSRISEENKKAQIKQQRIKANRHSDDDVTNDEISFASRER 400
401 QRRSRTRRSIRNDDSSSSSSSSEEDYQPRPLRGQPPNIPLLPFFVGNRGN 450
451 AAFLLSSDDPAEIVKSLAEEIKSDMKKPAYIPERSTHAKLMMMRDIVRTM 500
501 TAKQLQKATSLIHSESKHDLGWIAYRDMVSESGTHPALEELSIWIISKKL 550
551 SSEEGAELLATLPRAVIMPTPEYFEAFNKLVMDKRVRNQPIVNSTGLLAL 600
601 ATLHRQVHDAEFSHNNYPVHAFGRMVPRNYNATNDFIDYLGKQLHAAMAD 650
651 GNRPKIQVIIRALGNTGNKRILNYLEPYLERKKNATEFERLLMVTSLDIL 700
701 AEINPELARQVLYNVYINIGENHELRCASVILLMRTQPPAAMLQRMAEFS 750
751 NIDPVKQVVSAVQSAIRSAANLKEPGNLNLARAARSAVNILNPMSMDIAY 800
801 SNDILSSNMIQDMDLGYKDNMAHVGSSDSIIPNTILRKFNRYAGGQAHSD 850
851 INFSEMVSSVKQLLKALRNPLKQREDPLLRREPVADRREFNIPPIIIDIA 900
901 PPLEGNMMLRWLGNDRFFSYDKNDIKQLFRNYNAAALPLADMHMLDDMKV 950
951 YNQKSLAIAFPNALGLPSLFTIDVPTVLRANSTFRLLLNNTSSESSSIEV 1000
1001 LPWKLGARSETSLTYSVKEMSKIAIVTPFNSMEHMAALERNILINIPIKL 1050
1051 DVDFDLEAQNIALNMKLIGKDSNPRLAQLSTNTYTTNHKITIIKPAIQDE 1100
1101 EANEIHVRPARMMRDTFGKLQTGIALNLEIETEDEFLDMEFVRQELQGRD 1150
1151 FTSALAFIWARRTINNHNVTLSIDEDQSTTNAVRIEGKYASKINDVDAGT 1200
1201 WTEWRSSRVERSAKRHHRPRPEAQEYDSSANPAIVDRADKLEEFLNRASG 1250
1251 NRVHGLAVRVAFTGSSDATFDLTAALGLSNVNGSARALVSYISQPAHPAD 1300
1301 VMPRKTEYDLFAALSMPAPPIINFNQALEFDPDSNLDAGLSIFTNDKPSG 1350
1351 NLRIKGELQQSEERRNAIRSTPAALACMREMANGNNNLLPSCRNATEMAN 1400
1401 RLDRIRLQAKFENLSDDLINNTYKAYTWIRYFTQPYVTENIAQEQNPGRL 1450
1451 NINVDVNNDGTALNASVDTALMSITWTNIRLNRWTRSLVEPSPQDTALDR 1500
1501 LAREALPLYYEPTCVLDVSQAATFDNTTYPLTLSSAWHMMLQYQPKRSQD 1550
1551 DENLDDVPDIIRPPVAILARETSNRRKEILMNLDHTIVAFKPTSEVNVEV 1600
1601 NGRILIIEQSRTTDIMAEGNLVLQIHRLPSRAVYMVAPQHKLLMIHDGKR 1650
1651 VLLQASNGYRDEVRGLCGTFDGEPTTDFTTPSNCILNDPKAFINSYRLGG 1700
1701 DREDNAWMLNYRAQPCVSRNFTSTDIIGKHMPRNPASRGFELPHPVNDTS 1750
1751 SSSSSSSESSSSSSSSSSESASNSDLHNNSTSSSSSSSSSSSESAEGAPE 1800
1801 KSSRKTSPPQTSTLHRTMVVEEVTEICFSMRPLPECAPRFKPADTLKKKI 1850
1851 KFHCLTKGPTASHWLKMVKKGVNPDFSKKREHKQLEVDIPAKCVRH 1896
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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