 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O88799 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MALPVWTLMLLVGAAWGQEQVPAWRPNSPDLGPMVHTSREDSILSKCDFE 50
51 DNSRPFCDWSQMSADDGDWIRTTGPSLTGTSGPPGGYPNGEGYYLHMDPK 100
101 TFPQGGVARLRSPDIWEQGPLCVHFAFHMFGLSWGAQLRLLLLRGRKHLR 150
151 PYVLWKHVNTQSPSWMPTTVTVPADHDIPSWLMFEGMRGNTAYLDISLDG 200
201 LSIQRGTCNQVCMSQMCTFDTLNDLCGWSWVPTATGAKWTQKKGPTGKQG 250
251 VGPAEDFSNPGNGYYMLLDSTNARPGQKAVLLSPLSHSRGCMTLSFHYIM 300
301 HGQGHEEGLFVYATFLGNIRKYTLFSGHPGPDWQAVSVNYTGQGQIQFMV 350
351 VGMFGNIPEPAIAVDAISIAPCGESFPQCDFEDRVHPFCDWNQVYGDMGH 400
401 WSWGSKSVPTLIAGSPREFPYGGEHYIFFDSVKLSQEGQSARLVSPPFCA 450
451 PGGICVEFAYHMYGLGKGTTLKLLLGSPAGSSPIPLWNRVGSQSSGWMNS 500
501 SVTIPKGYQQPMQLFIEATRGTSTAFVVALNFILISHGPCRVLLQTEIPS 550
551 SPLLPPTGPSESTVPTLPMEQPTSPTKATTVTIEIPTTPTEEATIPTETT 600
601 TVPTEVINVSPKETSIPPEVTIPTEVITVSPEEIISPTEVTPVPTDVTAA 650
651 YVEATNASPEETSVPPEVTILTEVTTVSPEETTVPTEVPIVLIEATAFPT 700
701 GETTLYTEVPTVPTEVTGVHTEVTNVSPEETSVPTEETISTEVTTVSPEE 750
751 TTVPTEVPIVLIEATASPTGEITLYTEVPTVPTEVTGVHTEVTNVSPEET 800
801 SVPTEETISTEVTTVSPEETTLPTEVPTVSTEVTNVSPEETSVPPEETIL 850
851 TTLYTEVPTVPTEVTGVHTEVTNVSPEETSVPTEETISTEVTTVSPEETT 900
901 LPTEVPTVSTEVTNVSPEETSVPPEETILTEITTVSPEETVFPIEGTTLP 950
951 TEVLTVPIEVTTFPTGETTVPTEVPTVSTEMTGVHTEVTTVFPEETSIPT 1000
1001 EVATVLPASIPPEETTTPTEVTTTPPEETTIPAEVTTVPPASIPPEETAS 1050
1051 LTEVTTTPPEETTTPTEVTTVPPEKTTIPTEVTTVPPASIFPEETTVPPE 1100
1101 ETTIASEETTVSTQETTLLTEQSAVTQTSIACRPPCPSPPLMPIGPLLSK 1150
1151 PPGVSMFSLAPTTGVSTTESCPPNAHIELCACPASCESPKPSCQPPCIPG 1200
1201 CVCNPGFLFSNNQCINESSCNCPYNNKHYKPGEEWFTPNCTERCRCLPGS 1250
1251 LMECQISQCGTHTVCQLKSDQYQCEPYGKATCLVYGDLHFVTFDERHIGF 1300
1301 TGTCTYILTQTCSNSTDHFFRITANTEERGVEGVSCLDKVVISLPETTVT 1350
1351 MISGRHTLIGDQEVTLPAILSDDTYVGLSGRFVELRTTFGLRVRWDGDQQ 1400
1401 LFVTVSSTFSGKLCGFCGNYDGDSSNDNLKSDGMMTHDEEELRLSWQVEE 1450
1451 DEDKDWVSSRCQKKKNPPSCDAALGSTMSGPKLCGQLVNPSGPFEACLLH 1500
1501 LKASSFLDNCVTDMCSFQGLQQKLCARMSAMTATCQDAGYPVKPWREPQF 1550
1551 CPLVCPKNSRYSLCAKPCPETCHPISTTQHCSDKCVEGCECDPGFILSGS 1600
1601 ECVPSSQCGCTSFQGRYFKLQEQWFNPDCKEICTCESHNHILCKPWKCKA 1650
1651 QEACSYKNGVLGCHAQGAATCMVSGDPHYLTFDGALHHFMGTCTYVLTQP 1700
1701 CWSKSQENNFVVSATNEIHDGNLEVSYVKAVHVQVFDLKISMFKGQKVVL 1750
1751 NNQRVVLPVWPSQGRVTIRLSGIFVLLYTNFGLQVRYDGRHLVEVTVPSS 1800
1801 YTGSLCGLCGNYNNNSMDDNLRADMKPAGNSLLLGAAWKILEASDPGCFL 1850
1851 AGGKPSRCADSDMDDVWTKKCAILMNPLGPFSNCHEAVPPQASFSSCVYG 1900
1901 QCETNGDNLTFCHSLQAYASLCAQAGQVTTWRNSTFCPMRCPPRSSYNPC 1950
1951 ANSCPATCLTLSTPRDCPTLPCVEGCECQSGHILSGTTCVPLRQCGCSDQ 2000
2001 DGSYHLLGESWYTEKTCTTLCTCSAHSNITCSPTACKANHVCLRQEGLLR 2050
2051 CAAEMGECRISEDSQIVSFDDHSHPIQDTCTYILVKVCHPNTNMPFFMIS 2100
2101 AKTDINTNGKNKTFGVYQLYIDIFNFHITLQKDHLVLISLINDSIVTLPT 2150
2151 TTHIPGVSVMTEDVYTIVTIKDEIQVKFESNNFLDVKIPASSNGKVCGVC 2200
2201 GNFNGEEEDELMTPSGELAEDEQEFMNSWKDKSMDPNCQKIEGQNLQVEQ 2250
2251 QEIMNGKCRPIDFEKAQANCQTALQGPAWAHCSSRVPIKPFLLKCMNSFC 2300
2301 EFRELFRALCDSLQSFEDACQNQGLKPPIWRNSSFCPLECPAHSHYTNCL 2350
2351 PSCPPSCLDPDSRCEGSGHKVPATCREGCICQPDYVLLNDKCVLRSHCGC 2400
2401 KDAQGVFIPAGKTWISEDCTQSCTCMKGSMRCWDFQCPPGTYCKNSNDGS 2450
2451 SNCVKISLQCPAHSKFTDCLPPCHPSCSDPDGHCEGISTNAHSNCKEGCV 2500
2501 CQPGYVLRNDKCVLRIECGCQHTQGGFIPAGKNWTSRGCSQSCDCMEGVI 2550
2551 RCQNFQCPSGTYCQDIEDGTSNCANITLQCPAHSSFTNCLPPCQPSCSDP 2600
2601 EGHCGGSTTKAPSACQEGCVCEPDYVVLNNKCVPRIECGCKDAQGVLIPA 2650
2651 DKIWINKGCTQTCACVTGTIHCRDFQCPSGTYCKDIKDDASNCTEIILQC 2700
2701 PDHSLYTHCLPSCLLSCSDPDGLCRGTSPEAPSTCKEGCVCDPDYVLSND 2750
2751 KCVLRIECGCKDAQGVLIPAGKTWINRGCTQSCSCMGGAIQCQNFKCPSE 2800
2801 AYCQDMEDGNSNCTSIPLQCPAHSHYTNCLPTCQPSCSDPDGHCEGSSTK 2850
2851 APSACKEGCVCEPDYVMLNNKCVPRIECGCKDTQGVLIPADKTWINRGCT 2900
2901 QSCTCRGGAIQCQKYHCSSGTYCKDMEDDSSSCATITLQCPAHSHFTNCL 2950
2951 PPCQPSCLDSEGHCEGSTTKAPSACQEGCVCEPDYVVLNNKCVPRIECGC 3000
3001 KDAQGVLIPADKTWINRGCTQSCTCKGGAIQCQKFQCPSETYCKDIEDGN 3050
3051 SNCTRISLQCPANSNFTSCLPSCQPSCSNTDVHCEGSSPNTLSSCREGCV 3100
3101 CQSGYVLHNDKCILRNQCGCKDAQGALIPEGKTWITSGCTQSCNCTGGAI 3150
3151 QCQNFQCPLKTYCKDLKDGSSNCTNIPLQCPAHSRYTNCLPSCPPLCLDP 3200
3201 EGLCEGTSPKVPSTCREGCICQPGYLMHKNKCVLRIFCGCKNTQGAFISA 3250
3251 DKTWISRGCTQSCTCPAGAIHCRNFKCPSGTYCKNGDNGSSNCTEITLQC 3300
3301 PTNSQFTDCLPSCVPSCSNRCEVTSPSVPSSCREGCLCNHGFVFSEDKCV 3350
3351 PRTQCGCKDARGAIIPAGKTWTSKGCTQSCACVEGNIQCQNFQCPPETYC 3400
3401 KDNSEGSSTCTKITLQCPAHTQYTSCLPSCLPSCLDPEGLCKDISPKVPS 3450
3451 TCKEGCVCQSGYVLNSDKCVLRAECDCKDAQGALIPAGKTWTSPGCTQSC 3500
3501 ACMGGAVQCQSSQCPPGTYCKDNEDGNSNCAKITLQCPAHSLFTNCLPPC 3550
3551 LPSCLDPDGLCKGASPKVPSTCKEGCICQSGYVLSNNKCLLRNRCGCKDA 3600
3601 HGALIPEDKTWVSRGCTQSCVCTGGSIQCLSSQCPPGAYCKDNEDGSSNC 3650
3651 ARIPPQCPANSHYTDCFPPCPPSCSDPEGHCEASGPRVLSTCREGCLCNP 3700
3701 GFVLDRDKCVPRVECGCKDAQGALIPSGKTWTSPGCTQSCACMGGVVQCQ 3750
3751 SSQCPPGTYCKDNEDGNSNCAKITLQCPTHSNYTDCLPFCLPSCLDPSAL 3800
3801 CGGTSPKGPSTCKEGCVCQPGYVLDKDKCILKIECGCRDTQGAVIPAGKT 3850
3851 WLSTGCIQSCACVEGTIQCQNFQCPPGTYCNHNNNCAKIPLQCPAHSHFT 3900
3901 SCLPSCPPSCANLDGSCEQTSPKVPSTCKEGCLCQPGYFLNNGKCVLQTH 3950
3951 CDCKDAEGGLVPAGKTWTSKDCTQSCACTGGAVQCQNFQCPLGTYCKDSG 4000
4001 DGSSNCTKIHKGAMGDGVLMAGGIRALQCPAHSHFTSCLPSCPPSCSNLD 4050
4051 GSCVESNFKAPSVCKKGCICQPGYLLNNDKCVLRIQCGCKDTQGGLIPAG 4100
4101 RTWISSDCTKSCSCMGGIIQCRDFQCPPGTYCKESNDSSRTCAKIPLQCP 4150
4151 AHSHYTNCLPACSRSCTDLDGHCEGTSPKVPSPCKEGCLCQPGYVVHNHK 4200
4201 CVLQIHCGCKDAQGGFVPAGKTWISRGCTQSCACVGGAVQCHNFTCPTGT 4250
4251 QCQNSSCSKITVQCPAHSQYTTCLPSCLPSCFDPEGLCGGASPRAPSTCR 4300
4301 EGCVCEADYVLREDKCVLRTQCGCKDAQGDLIPANKTWLTRGCAQKCTCK 4350
4351 GGNIHCWNFKCPLGTECKDSVDGGSNCTKIALQCPAHSHHTYCLPSCIPS 4400
4401 CSNVNDRCESTSLQRPSTCIEGCLCHSGFVFSKDKCVPRTQCGCKDSQGT 4450
4451 LIPAGKNWITTGCSQRCTCTGGLVQCHDFQCPSGAECQDIEDGNSNCVEI 4500
4501 TVQCPAHSHYSKCLPPCQPSCSDPDGHCEGTSPEAPSTCEEGCVCEPDYV 4550
4551 LSNDKCVPSSECGCKDAHGVLIPESKTWVSRGCTKNCTCKGGTVQCHDFS 4600
4601 CPTGSRCLDNNEGNSNCVTYALKCPAHSLYTNCLPSCLPSCSDPEGLCGG 4650
4651 TSPEVPSTCKEGCICQSGYVLHKNKCMLRIHCDCKDFQGSLIKTGQTWIS 4700
4701 SGCSKICTCKGGFFQCQSYKCPSGTQCEESEDGSSNCVSSTMKCPANSLY 4750
4751 THCLPTCLPSCSNPDGRCEGTSHKAPSTCREGCVCQPGYLLNKDTCVHKN 4800
4801 QCGCKDIRGNIIPAGNTWISSDCTQSCACTDGVIQCQNFVCPSGSHCQYN 4850
4851 EDGSSDCAANKLERCTIFGDPYYLTFDGFTYHFLGRMNYYLIKTVDKLPR 4900
4901 GIEPLIMEGRNKISPKGSSTLHEVTTIVYGYKIQLQEELVVLVNDEKVAV 4950
4951 PYNPNEHLRVMLRAQRLLLVTDFEMVLDFDGKHSAVISLPTTYRGLTRGL 5000
5001 CGNYDRDQSNELMLPSGVLTSNVHVFGNSWEVKAQHAFFRFPRALPEDEE 5050
5051 RDEEPDLLQSECSQEQTALISSTQACRVLVDPQGPFAACHQIIAPEPFEQ 5100
5101 RCMLDMCTGWKTKEEEELRCRVLSGYAIICQEAGANMTGWRDHTHCAMTC 5150
5151 PANTVYQRCMTPCPASCAKFVTPKVCEGPCVEGCASLPGYIYSDTQSLPV 5200
5201 THCGCTADGIYYKLGDSFVTNDCSQHCTCASQGILLCEPYGCRAGESCMV 5250
5251 ANFTRGCFQDSPCLQNPCHNDGRCEEQGATFICHCDFGYGGEFCTEPQDI 5300
5301 TTRKKIEASSLVAILPGVLVMVLVPVLLPRVYVYMATRTTMGRRRMKRKE 5350
5351 KKLLRQSRLRLEDADVPEPTFKATEF 5376
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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