 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q07954 from www.uniprot.org...
The NucPred score for your sequence is 0.60 (see score help below)
1 MLTPPLLLLLPLLSALVAAAIDAPKTCSPKQFACRDQITCISKGWRCDGE 50
51 RDCPDGSDEAPEICPQSKAQRCQPNEHNCLGTELCVPMSRLCNGVQDCMD 100
101 GSDEGPHCRELQGNCSRLGCQHHCVPTLDGPTCYCNSSFQLQADGKTCKD 150
151 FDECSVYGTCSQLCTNTDGSFICGCVEGYLLQPDNRSCKAKNEPVDRPPV 200
201 LLIANSQNILATYLSGAQVSTITPTSTRQTTAMDFSYANETVCWVHVGDS 250
251 AAQTQLKCARMPGLKGFVDEHTINISLSLHHVEQMAIDWLTGNFYFVDDI 300
301 DDRIFVCNRNGDTCVTLLDLELYNPKGIALDPAMGKVFFTDYGQIPKVER 350
351 CDMDGQNRTKLVDSKIVFPHGITLDLVSRLVYWADAYLDYIEVVDYEGKG 400
401 RQTIIQGILIEHLYGLTVFENYLYATNSDNANAQQKTSVIRVNRFNSTEY 450
451 QVVTRVDKGGALHIYHQRRQPRVRSHACENDQYGKPGGCSDICLLANSHK 500
501 ARTCRCRSGFSLGSDGKSCKKPEHELFLVYGKGRPGIIRGMDMGAKVPDE 550
551 HMIPIENLMNPRALDFHAETGFIYFADTTSYLIGRQKIDGTERETILKDG 600
601 IHNVEGVAVDWMGDNLYWTDDGPKKTISVARLEKAAQTRKTLIEGKMTHP 650
651 RAIVVDPLNGWMYWTDWEEDPKDSRRGRLERAWMDGSHRDIFVTSKTVLW 700
701 PNGLSLDIPAGRLYWVDAFYDRIETILLNGTDRKIVYEGPELNHAFGLCH 750
751 HGNYLFWTEYRSGSVYRLERGVGGAPPTVTLLRSERPPIFEIRMYDAQQQ 800
801 QVGTNKCRVNNGGCSSLCLATPGSRQCACAEDQVLDADGVTCLANPSYVP 850
851 PPQCQPGEFACANSRCIQERWKCDGDNDCLDNSDEAPALCHQHTCPSDRF 900
901 KCENNRCIPNRWLCDGDNDCGNSEDESNATCSARTCPPNQFSCASGRCIP 950
951 ISWTCDLDDDCGDRSDESASCAYPTCFPLTQFTCNNGRCININWRCDNDN 1000
1001 DCGDNSDEAGCSHSCSSTQFKCNSGRCIPEHWTCDGDNDCGDYSDETHAN 1050
1051 CTNQATRPPGGCHTDEFQCRLDGLCIPLRWRCDGDTDCMDSSDEKSCEGV 1100
1101 THVCDPSVKFGCKDSARCISKAWVCDGDNDCEDNSDEENCESLACRPPSH 1150
1151 PCANNTSVCLPPDKLCDGNDDCGDGSDEGELCDQCSLNNGGCSHNCSVAP 1200
1201 GEGIVCSCPLGMELGPDNHTCQIQSYCAKHLKCSQKCDQNKFSVKCSCYE 1250
1251 GWVLEPDGESCRSLDPFKPFIIFSNRHEIRRIDLHKGDYSVLVPGLRNTI 1300
1301 ALDFHLSQSALYWTDVVEDKIYRGKLLDNGALTSFEVVIQYGLATPEGLA 1350
1351 VDWIAGNIYWVESNLDQIEVAKLDGTLRTTLLAGDIEHPRAIALDPRDGI 1400
1401 LFWTDWDASLPRIEAASMSGAGRRTVHRETGSGGWPNGLTVDYLEKRILW 1450
1451 IDARSDAIYSARYDGSGHMEVLRGHEFLSHPFAVTLYGGEVYWTDWRTNT 1500
1501 LAKANKWTGHNVTVVQRTNTQPFDLQVYHPSRQPMAPNPCEANGGQGPCS 1550
1551 HLCLINYNRTVSCACPHLMKLHKDNTTCYEFKKFLLYARQMEIRGVDLDA 1600
1601 PYYNYIISFTVPDIDNVTVLDYDAREQRVYWSDVRTQAIKRAFINGTGVE 1650
1651 TVVSADLPNAHGLAVDWVSRNLFWTSYDTNKKQINVARLDGSFKNAVVQG 1700
1701 LEQPHGLVVHPLRGKLYWTDGDNISMANMDGSNRTLLFSGQKGPVGLAID 1750
1751 FPESKLYWISSGNHTINRCNLDGSGLEVIDAMRSQLGKATALAIMGDKLW 1800
1801 WADQVSEKMGTCSKADGSGSVVLRNSTTLVMHMKVYDESIQLDHKGTNPC 1850
1851 SVNNGDCSQLCLPTSETTRSCMCTAGYSLRSGQQACEGVGSFLLYSVHEG 1900
1901 IRGIPLDPNDKSDALVPVSGTSLAVGIDFHAENDTIYWVDMGLSTISRAK 1950
1951 RDQTWREDVVTNGIGRVEGIAVDWIAGNIYWTDQGFDVIEVARLNGSFRY 2000
2001 VVISQGLDKPRAITVHPEKGYLFWTEWGQYPRIERSRLDGTERVVLVNVS 2050
2051 ISWPNGISVDYQDGKLYWCDARTDKIERIDLETGENREVVLSSNNMDMFS 2100
2101 VSVFEDFIYWSDRTHANGSIKRGSKDNATDSVPLRTGIGVQLKDIKVFNR 2150
2151 DRQKGTNVCAVANGGCQQLCLYRGRGQRACACAHGMLAEDGASCREYAGY 2200
2201 LLYSERTILKSIHLSDERNLNAPVQPFEDPEHMKNVIALAFDYRAGTSPG 2250
2251 TPNRIFFSDIHFGNIQQINDDGSRRITIVENVGSVEGLAYHRGWDTLYWT 2300
2301 SYTTSTITRHTVDQTRPGAFERETVITMSGDDHPRAFVLDECQNLMFWTN 2350
2351 WNEQHPSIMRAALSGANVLTLIEKDIRTPNGLAIDHRAEKLYFSDATLDK 2400
2401 IERCEYDGSHRYVILKSEPVHPFGLAVYGEHIFWTDWVRRAVQRANKHVG 2450
2451 SNMKLLRVDIPQQPMGIIAVANDTNSCELSPCRINNGGCQDLCLLTHQGH 2500
2501 VNCSCRGGRILQDDLTCRAVNSSCRAQDEFECANGECINFSLTCDGVPHC 2550
2551 KDKSDEKPSYCNSRRCKKTFRQCSNGRCVSNMLWCNGADDCGDGSDEIPC 2600
2601 NKTACGVGEFRCRDGTCIGNSSRCNQFVDCEDASDEMNCSATDCSSYFRL 2650
2651 GVKGVLFQPCERTSLCYAPSWVCDGANDCGDYSDERDCPGVKRPRCPLNY 2700
2701 FACPSGRCIPMSWTCDKEDDCEHGEDETHCNKFCSEAQFECQNHRCISKQ 2750
2751 WLCDGSDDCGDGSDEAAHCEGKTCGPSSFSCPGTHVCVPERWLCDGDKDC 2800
2801 ADGADESIAAGCLYNSTCDDREFMCQNRQCIPKHFVCDHDRDCADGSDES 2850
2851 PECEYPTCGPSEFRCANGRCLSSRQWECDGENDCHDQSDEAPKNPHCTSQ 2900
2901 EHKCNASSQFLCSSGRCVAEALLCNGQDDCGDSSDERGCHINECLSRKLS 2950
2951 GCSQDCEDLKIGFKCRCRPGFRLKDDGRTCADVDECSTTFPCSQRCINTH 3000
3001 GSYKCLCVEGYAPRGGDPHSCKAVTDEEPFLIFANRYYLRKLNLDGSNYT 3050
3051 LLKQGLNNAVALDFDYREQMIYWTDVTTQGSMIRRMHLNGSNVQVLHRTG 3100
3101 LSNPDGLAVDWVGGNLYWCDKGRDTIEVSKLNGAYRTVLVSSGLREPRAL 3150
3151 VVDVQNGYLYWTDWGDHSLIGRIGMDGSSRSVIVDTKITWPNGLTLDYVT 3200
3201 ERIYWADAREDYIEFASLDGSNRHVVLSQDIPHIFALTLFEDYVYWTDWE 3250
3251 TKSINRAHKTTGTNKTLLISTLHRPMDLHVFHALRQPDVPNHPCKVNNGG 3300
3301 CSNLCLLSPGGGHKCACPTNFYLGSDGRTCVSNCTASQFVCKNDKCIPFW 3350
3351 WKCDTEDDCGDHSDEPPDCPEFKCRPGQFQCSTGICTNPAFICDGDNDCQ 3400
3401 DNSDEANCDIHVCLPSQFKCTNTNRCIPGIFRCNGQDNCGDGEDERDCPE 3450
3451 VTCAPNQFQCSITKRCIPRVWVCDRDNDCVDGSDEPANCTQMTCGVDEFR 3500
3501 CKDSGRCIPARWKCDGEDDCGDGSDEPKEECDERTCEPYQFRCKNNRCVP 3550
3551 GRWQCDYDNDCGDNSDEESCTPRPCSESEFSCANGRCIAGRWKCDGDHDC 3600
3601 ADGSDEKDCTPRCDMDQFQCKSGHCIPLRWRCDADADCMDGSDEEACGTG 3650
3651 VRTCPLDEFQCNNTLCKPLAWKCDGEDDCGDNSDENPEECARFVCPPNRP 3700
3701 FRCKNDRVCLWIGRQCDGTDNCGDGTDEEDCEPPTAHTTHCKDKKEFLCR 3750
3751 NQRCLSSSLRCNMFDDCGDGSDEEDCSIDPKLTSCATNASICGDEARCVR 3800
3801 TEKAAYCACRSGFHTVPGQPGCQDINECLRFGTCSQLCNNTKGGHLCSCA 3850
3851 RNFMKTHNTCKAEGSEYQVLYIADDNEIRSLFPGHPHSAYEQAFQGDESV 3900
3901 RIDAMDVHVKAGRVYWTNWHTGTISYRSLPPAAPPTTSNRHRRQIDRGVT 3950
3951 HLNISGLKMPRGIAIDWVAGNVYWTDSGRDVIEVAQMKGENRKTLISGMI 4000
4001 DEPHAIVVDPLRGTMYWSDWGNHPKIETAAMDGTLRETLVQDNIQWPTGL 4050
4051 AVDYHNERLYWADAKLSVIGSIRLNGTDPIVAADSKRGLSHPFSIDVFED 4100
4101 YIYGVTYINNRVFKIHKFGHSPLVNLTGGLSHASDVVLYHQHKQPEVTNP 4150
4151 CDRKKCEWLCLLSPSGPVCTCPNGKRLDNGTCVPVPSPTPPPDAPRPGTC 4200
4201 NLQCFNGGSCFLNARRQPKCRCQPRYTGDKCELDQCWEHCRNGGTCAASP 4250
4251 SGMPTCRCPTGFTGPKCTQQVCAGYCANNSTCTVNQGNQPQCRCLPGFLG 4300
4301 DRCQYRQCSGYCENFGTCQMAADGSRQCRCTAYFEGSRCEVNKCSRCLEG 4350
4351 ACVVNKQSGDVTCNCTDGRVAPSCLTCVGHCSNGGSCTMNSKMMPECQCP 4400
4401 PHMTGPRCEEHVFSQQQPGHIASILIPLLLLLLLVLVAGVVFWYKRRVQG 4450
4451 AKGFQHQRMTNGAMNVEIGNPTYKMYEGGEPDDVGGLLDADFALDPDKPT 4500
4501 NFTNPVYATLYMGGHGSRHSLASTDEKRELLGRGPEDEIGDPLA 4544
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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