 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8VHE6 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MFRIGRRQLWKQSVTRVLTQRLKEEKEAKRARLDGRHDYLFAIVASCLDL 50
51 NKPEVEDALLEGNQIERMDQLFAVGGLRHLMFYYQDVEGAEAGHCGSSGG 100
101 VNPASGKMKKPKVFVTEGKDVALMGACVFFIRSDPSKAITPENIHREVSF 150
151 NTLDTADGGLLNSVRRLLSDIFIPALRASSHGWGELEGLQDASSIRQEFL 200
201 SSLEGFVGILSGAQNSLKEKVNLQKCDIIELKSLKEPTDYLALASNPETV 250
251 EKVECCMRVWIKQMEQILAENSQLRKEADDVGPRAELEHWKQRLSRFNYL 300
301 LDQLKSPDVKAALALLAAAKSKLLKVWRDTDIRITDAANEAKDNVKYLYT 350
351 LEKCCDPLYSSDPVTMIDAIPTLINAIKMVYSISHYYNTSEKITSLFVKV 400
401 TNQMISACKAHITNNGTATIWSQPQEIVMQKIAAVIKLKQGYQSCFQETK 450
451 QKLKQNPSEKQFDFSEMYIFGKFETFHRRLAKIMDIFTTFKTYSVLQDSK 500
501 IEGLEDMATKYQDIVAAIKKKEYNFLDQREMDFDQDYEEFCKRINELHND 550
551 LQRFMDITFEKIPSTRQALSTLKKFERLNIPNLGIEEKYQIIFQNFATDI 600
601 DTISKLYTKQKYDPPLARNQPPIAGKILWARQLFHRLEQPMQLFQQHPFV 650
651 LRTAEAKPVIRSYNRIAKVLLEFEVLYHRAWLQQIEEIHAGLEASLLVKA 700
701 PGTGELFVNFDPQILVLFRETQCMSQLGLPVSPFAAALFQKRDMFKKNFS 750
751 DMKMMLSEYERVKLKMPPAIEQLMFPHLARVDEALQPGLAVLTWTSLNIG 800
801 GYLENAFAKIKDLELLLDRVNDLIEFRIHAILEEMSSMALCQLPQDDPLT 850
851 CEEFLQMTKDLCVSGAQLLHFKSSLVEEAVNELINMLLDVDVPPEEAAEN 900
901 VCHENASPSGNTSGRREGHSEALASSFNAGASSLPLTATARKKKETEVLE 950
951 EARELLSHFNHQNTDALLKVTRNTLEAIRRRIHFSHMINFRDSNDASKAK 1000
1001 QNHLPIFRASVTLAIPNIAMTPALEDIQQTLNKAVECIISVPKGVRQWSS 1050
1051 ELLSKRKMHERKMAAVKNNEDSDSDAEVEENELQETLEIASINLPIPVQT 1100
1101 QNYYKNISDNKEIVKLVSVLSTVISSTKKEVITSMDRFKRYNHIWQKEKE 1150
1151 DTIMTFIAQNPLLPEFESRILYFQSLEQEINAEPEYIRVGSIALYTADLK 1200
1201 LSLTAETKAWMAVIGRHCNRKYRAEMENILTVVEESQKKLSRPIKDLDDI 1250
1251 RIAMAALKEIREQQISTDFQVGPIEESYALLNKYGLLVAKEEMDKVDTLR 1300
1301 YAWEKLLARASDVQNELGALQPSFRKELISTVEVFLQDCQQFYLDYDLNG 1350
1351 PMASGLKPQEASGRLIIFQNQFDNIYRKYITYTGGEELFGLPVTPYPQLL 1400
1401 EIKRQLNLLQKIYSLYNNVIETVNSYQDTLWSDVNIEKINNELLEFQNRC 1450
1451 RKLPRALKDWQAFLDLKKTIDDFSECCPLLEYMASNAMVERHWQRITALT 1500
1501 GHSLDVGNETFKLRNIMEAPLLKYKEEIEDICISAVKERDIEQKLRQVIN 1550
1551 EWDNKTLTFSGFKTRGELLLRGDSTSEVIASMEDSLMLLGSLLSNRYNMP 1600
1601 FKAQIQKWVQCLSNSTDIIENWMTVQNLWIYLEAVFVGGDIAKQLPKEAK 1650
1651 RFSNIDKSWVKIMTRAHEIPNVVQCCVGDETMGQLLPHLLDQLEICQKSL 1700
1701 TGYLEKKRLCFPRFFFVSDPALLEILGQASDSHTIQAHLLNVFDNIKTVK 1750
1751 FHDKIYDRILSISSREGETIELDKPVMAEGNVEVWLNSLLEESQSSLHLV 1800
1801 IRQAAANIQEPGFQLIEFLSSFPAQVGLLGIQMLWTRDSEEALRNAKFDK 1850
1851 KIMQKTNQAFLELLNMLIEITTKDLSSMERVKYETLITIHVHQRDIFDDL 1900
1901 CHMHVKSPTDFEWLKQCRFYFKEDSDKTMIHITDVAFIYQNEFLGCTDRL 1950
1951 VITPLTDRCYITLAQALGMSMGGAPAGPAGTGKTETTKDMGRCLGKYVVV 2000
2001 FNCSDQMDFRGLGRIFKGLAQSGSWGCFDEFNRIDLPVLSVAAQQISIIL 2050
2051 TCKKEHKKSFIFTDGDNVTMNPEFGLFLTMNPGYAGRQELPENLKINFRS 2100
2101 VAMMVPDRQIIIRVKLASCGFIDNVVLARKFFTLYQLCEEQLSKQVHYDF 2150
2151 GLRNILSVLRTLGAAKRASPTDTESTIVMRVLRDMNLSKLIDEDEPLFLS 2200
2201 LIEDLFPNILLDKAGYPELETAISKQVEEAGLINHPPWKLKVIQLFETQR 2250
2251 VRHGMMTLGPSGSGKTTCIHTLMKAMTDCGKPHREMRMNPKAITAPQMFG 2300
2301 RLDVATNDWTDGIFSTLWRKTLKAKKGEHIWIVLDGPVDAIWIENLNSVL 2350
2351 DDNKTLTLANGDRIPMAPNCKIVFEPHNIDNASPATVSRNGMVFMSSSVL 2400
2401 DWSPILEGFLKRRSPQEAEILRQLYAETFPDLYRFSIQNLEFKMEVLEAF 2450
2451 VITQSTHMLQGLIPPKEQAGEVDPEHLGRLFVFAMMWSVGAVLELEGRRR 2500
2501 MELWLRSREGPTLHLPQLTDAGDTMFDYYVAPNGTWRHWSLCTPEYVYPP 2550
2551 DTTPEYGSILVPNVDNVRTDFLIKTIAKQGKAVLLIGEQGTAKTVIIKGF 2600
2601 MSKFDPESHMVKNLNFSSATTPVMFQRTIESYVDKRMGTTYGPPAGKKMA 2650
2651 VFIDDLNMPVINEWGDQVTNEIVRQLMEQSGFYNLEKPGEFTSIVDIQFL 2700
2701 AAMIHPGGGRNDIPQRLKRQFSIFNCTLPSDASMDKIFGVIGAGYYCAQR 2750
2751 GFSEEVQDALIKLVPLTRRLWQMTKLKMLPTPAKFHYVFNLRDLSRIWQG 2800
2801 MLNITSEVIKDTDELLRLWKHECKRVIADRFSMSSDVTWFDKAVVSLVEE 2850
2851 EFGEEKAPVVDCGVDAYFVDFLRDAPEATGETPEEADAEMPKLYEPIASL 2900
2901 NHLRERLSVFLQLYNESIRGTGMDMVFFIDAMVHLVKISRVIRTPRGNAL 2950
2951 LVGVGGSGKQSLTRLASFIAGYTSFQITLTRSYNTSNLMEDLKVLYRTAG 3000
3001 QQGKGITFIFTDNEIKEESFLEYMNNVLSSGEVSNLFARDEIDEINSDLT 3050
3051 PIMKKEHPRRPPTNDNLYEYFMSRVRGNLHIVLCFSPVGEKFRNRALKFP 3100
3101 ALISGCTIDWFSRWPKDALVAVSEHFLSSYTIDCTAEIKKELVQCMGSFQ 3150
3151 DGVAEKCADYFQRFRRSTHVTPKSYLSFIQGYKFIYEEKHMEVQSLANRM 3200
3201 NTGLEKLKEASESVAALSKELAGKEKELQVANEKADTVLKEVTMKAQAAE 3250
3251 KVKAEVQKVKDKAQAIVDSISKDKAIAEEKLEAAKPALEEAEAALQTIKP 3300
3301 SDIATVRTLGRPPHLIMRIMDCVLLLFQRRVNAVKIDVDKGCTMPSWQES 3350
3351 LKLMTAGNFLQNLQQFPKDTINEEVIEFLNPYFEMSDYNIETAKRVCGNV 3400
3401 AGLCSWTKAMASFFSINKEVLPLKANLIVQENRHILAMQDLQKAQAELDA 3450
3451 KQAELDVVQAEYEQAMAEKQTLLEDADRCRHKMQTASTLISGLAGEKERW 3500
3501 TEQSKEFAAQTKRLVGDVLLATAFLSYSGPFNQEFRDLLLHDWKKEMKAR 3550
3551 KIPFGNGLNLNEMLIDAPTISEWNLQGLPNDDLSIQNGIIVTKASRYPLL 3600
3601 IDPQTQGKIWIKNKESQNELQITSLNHKYFRNHLEDSLSLGRPLLIEDVG 3650
3651 EELDPALDNVLEKNFIKTGSTFKVKVGDKEVDVMDGFKLYITTKLPNPAY 3700
3701 TPEISARTSIIDFTVTVKGLEDQLLGRVILTEKQELEKERTHLLEDVTAN 3750
3751 KRRMKELEDNLLYRLTSTQGSLVEDESLIIVLSNTKKTAEEVTQKLEISG 3800
3801 ETEIQINSAREEYRPVATRGSILYFLITEMRLVNEMYQTSLRQFLGLFDL 3850
3851 SLARSVKSPITSKRIANIIEHMTYEVFKYAARGLYEEHKFLFTLLLTLKI 3900
3901 DIQRNLVKHEEFLTLIKGGASLDLKACPPKPSKWILDMTWLNLVELSKLK 3950
3951 QFSDILDQISRNEKMWRVWFDKENPEEEPLPNAYDKSLDCFRRLLLIRSW 4000
4001 CPDRTIAQARKYIMDSMGENYAEGVILDLEKTWEESDPRTPLICLLSMGS 4050
4051 DPTDSIIALGKRLKIETRYVSMGQGQEVHARKLLHQTMANGGWVLLQNCH 4100
4101 LGLDFLDELMDVVTETETVHDTFRLWITTEVHKQFPITLLQMSIKFANEP 4150
4151 PQGLRAGLRRTYGGVSQDLLDVSVGAQWKPMLYAVAFLHSTVQERRKFGP 4200
4201 LGWNIPYEFNQADFNATVQFIQNHLDDMDVKKGVSWTTVRYMIGEIQYGG 4250
4251 RVTDDYDKRLLNTFAKVWFSENMFGPDFTFYQGYNIPKCSTVDGYLQYIQ 4300
4301 SLPAYDSPEVFGLHPNADITYQSKLAKDVLDTILGIQPKDSSGGGDETRE 4350
4351 AVVARLADDMLEKLPEDYSPFEVKERLQKMGPFQPMNIFLRQEIDRMQRV 4400
4401 LSLVRSTLTELKLAVDGTIIMSENLRDALDCMFDARIPARWKKASWVSST 4450
4451 LGFWFTELLERNCQFTSWVSNGRPHCFWMTGFFNPQGFLTAMRQEITRAN 4500
4501 KGWALDNMVLCNEVTKFMKDDISAPPTEGVYVYGLYLEGAGWDKRNMKLI 4550
4551 ESKPKVLFELMPVIRIFAENNTARDPRLYCCPIYKKPVRTDLNYIAAVDL 4600
4601 KTAQAPEHWVLRGVALLCDVK 4621
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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