 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96JQ0 from www.uniprot.org...
The NucPred score for your sequence is 0.33 (see score help below)
1 MQKELGIVPSCPGMKSPRPHLLLPLLLLLLLLLGAGVPGAWGQAGSLDLQ 50
51 IDEEQPAGTLIGDISAGLPAGTAAPLMYFISAQEGSGVGTDLAIDEHSGV 100
101 VRTARVLDREQRDRYRFTAVTPDGATVEVTVRVADINDHAPAFPQARAAL 150
151 QVPEHTAFGTRYPLEPARDADAGRLGTQGYALSGDGAGETFRLETRPGPD 200
201 GTPVPELVVTGELDRENRSHYMLQLEAYDGGSPPRRAQALLDVTLLDIND 250
251 HAPAFNQSRYHAVVSESLAPGSPVLQVFASDADAGVNGAVTYEINRRQSE 300
301 GDGPFSIDAHTGLLQLERPLDFEQRRVHELVVQARDGGAHPELGSAFVTV 350
351 HVRDANDNQPSMTVIFLSADGSPQVSEAAPPGQLVARISVSDPDDGDFAH 400
401 VNVSLEGGEGHFALSTQDSVIYLVCVARRLDREERDAYNLRVTATDSGSP 450
451 PLRAEAAFVLHVTDVNDNAPAFDRQLYRPEPLPEVALPGSFVVRVTARDP 500
501 DQGTNGQVTYSLAPGAHTHWFSIDPTSGIITTAASLDYELEPQPQLIVVA 550
551 TDGGLPPLASSATVSVALQDVNDNEPQFQRTFYNASLPEGTQPGTCFLQV 600
601 TATDADSGPFGLLSYSLGAGLGSSGSPPFRIDAHSGDVCTTRTLDRDQGP 650
651 SSFDFTVTAVDGGGLKSMVYVKVFLSDENDNPPQFYPREYAASISAQSPP 700
701 GTAVLRLRAHDPDQGSHGRLSYHILAGNSPPLFTLDEQSGLLTVAWPLAR 750
751 RANSVVQLEIGAEDGGGLQAEPSARVDISIVPGTPTPPIFEQLQYVFSVP 800
801 EDVAPGTSVGIVQAHNPPGRLAPVTLSLSGGDPRGLFSLDAVSGLLQTLR 850
851 PLDRELLGPVLELEVRAGSGVPPAFAVARVRVLLDDVNDNSPAFPAPEDT 900
901 VLLPPNTAPGTPIYTLRALDPDSGVNSRVTFTLLAGGGGAFTVDPTTGHV 950
951 RLMRPLGPSGGPAHELELEARDGGSPPRTSHFRLRVVVQDVGTRGLAPRF 1000
1001 NSPTYRVDLPSGTTAGTQVLQVQAQAPDGGPITYHLAAEGASSPFGLEPQ 1050
1051 SGWLWVRAALDREAQELYILKVMAVSGSKAELGQQTGTATVRVSILNQNE 1100
1101 HSPRLSEDPTFLAVAENQPPGTSVGRVFATDRDSGPNGRLTYSLQQLSED 1150
1151 SKAFRIHPQTGEVTTLQTLDREQQSSYQLLVQVQDGGSPPRSTTGTVHVA 1200
1201 VLDLNDNSPTFLQASGAAGGGLPIQVPDRVPPGTLVTTLQAKDPDEGENG 1250
1251 TILYTLTGPGSELFSLHPHSGELLTAAPLIRAERPHYVLTLSAHDQGSPP 1300
1301 RSASLQLLVQVLPSARLAEPPPDLAERDPAAPVPVVLTVTAAEGLRPGSL 1350
1351 LGSVAAPEPAGVGALTYTLVGGADPEGTFALDAASGRLYLARPLDFEAGP 1400
1401 PWRALTVRAEGPGGAGARLLRVQVQVQDENEHAPAFARDPLALALPENPE 1450
1451 PGAALYTFRASDADGPGPNSDVRYRLLRQEPPVPALRLDARTGALSAPRG 1500
1501 LDRETTPALLLLVEATDRPANASRRRAARVSARVFVTDENDNAPVFASPS 1550
1551 RVRLPEDQPPGPAALHVVARDPDLGEAARVSYRLASGGDGHFRLHSSTGA 1600
1601 LSVVRPLDREQRAEHVLTVVASDHGSPPRSATQVLTVSVADVNDEAPTFQ 1650
1651 QQEYSVLLRENNPPGTSLLTLRATDPDVGANGQVTYGGVSSESFSLDPDT 1700
1701 GVLTTLRALDREEQEEINLTVYAQDRGSPPQLTHVTVRVAVEDENDHAPT 1750
1751 FGSAHLSLEVPEGQDPQTLTMLRASDPDVGANGQLQYRILDGDPSGAFVL 1800
1801 DLASGEFGTMRPLDREVEPAFQLRIEARDGGQPALSATLLLTVTVLDAND 1850
1851 HAPAFPVPAYSVEVPEDVPAGTLLLQLQAHDPDAGANGHVTYYLGAGTAG 1900
1901 AFLLEPSSGELRTAAALDREQCPSYTFSVSAVDGAAAGPLSTTVSVTITV 1950
1951 RDVNDHAPTFPTSPLRLRLPRPGPSFSTPTLALATLRAEDRDAGANASIL 2000
2001 YRLAGTPPPGTTVDSYTGEIRVARSPVALGPRDRVLFIVATDLGRPARSA 2050
2051 TGVIIVGLQGEAERGPRFPRASSEATIRENAPPGTPIVSPRAVHAGGTNG 2100
2101 PITYSILSGNEKGTFSIQPSTGAITVRSAEGLDFEVSPRLRLVLQAESGG 2150
2151 AFAFTVLTLTLQDANDNAPRFLRPHYVAFLPESRPLEGPLLQVEADDLDQ 2200
2201 GSGGQISYSLAASQPARGLFHVDPTTGTITTTAILDREIWAETRLVLMAT 2250
2251 DRGSPALVGSATLTVMVIDTNDNRPTIPQPWELRVSEDALLGSEIAQVTG 2300
2301 NDVDSGPVLWYVLSPSGPQDPFSVGRYGGRVSLTGPLDFEQCDRYQLQLL 2350
2351 AHDGPHEGRANLTVLVEDVNDNAPAFSQSLYQVMLLEHTPPGSAILSVSA 2400
2401 TDRDSGANGHISYHLASPADGFSVDPNNGTLFTIVGTVALGHDGSGAVDV 2450
2451 VLEARDHGAPGRAARATVHVQLQDQNDHAPSFTLSHYRVAVTEDLPPGST 2500
2501 LLTLEATDADGSRSHAAVDYSIISGNWGRVFQLEPRLAEAGESAGPGPRA 2550
2551 LGCLVLLEPLDFESLTQYNLTVAAADRGQPPQSSVVPVTVTVLDVNDNPP 2600
2601 VFTRASYRVTVPEDTPVGAELLHVEASDADPGPHGLVRFTVSSGDPSGLF 2650
2651 ELDESSGTLRLAHALDCETQARHQLVVQAADPAGAHFALAPVTIEVQDVN 2700
2701 DHGPAFPLNLLSTSVAENQPPGTLVTTLHAIDGDAGAFGRLRYSLLEAGP 2750
2751 GPEGREAFALNSSTGELRARVPFDYEHTESFRLLVGAADAGNLSASVTVS 2800
2801 VLVTGEDEYDPVFLAPAFHFQVPEGARRGHSLGHVQATDEDGGADGLVLY 2850
2851 SLATSSPYFGINQTTGALYLRVDSRAPGSGTATSGGGGRTRREAPRELRL 2900
2901 EVIARGPLPGSRSATVPVTVDITHTALGLAPDLNLLLVGAVAASLGVVVV 2950
2951 LALAALVLGLVRARSRKAEAAPGPMSQAAPLASDSLQKLGREPPSPPPSE 3000
3001 HLYHQTLPSYGGPGAGGPYPRGGSLDPSHSSGRGSAEAAEDDEIRMINEF 3050
3051 PRVASVASSLAARGPDSGIQQDADGLSDTSCEPPAPDTWYKGRKAGLLLP 3100
3101 GAGATLYREEGPPATATAFLGGCGLSPAPTGDYGFPADGKPCVAGALTAI 3150
3151 VAGEEELRGSYNWDYLLSWCPQFQPLASVFTEIARLKDEARPCPPAPRID 3200
3201 PPPLITAVAHPGAKSVPPKPANTAAARAIFPPASHRSPISHEGSLSSAAM 3250
3251 SPSFSPSLSPLAARSPVVSPFGVAQGPSASALSAESGLEPPDDTELHI 3298
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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