 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02817 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MGLPLARLAAVCLALSLAGGSELQTEGRTRNHGHNVCSTWGNFHYKTFDG 50
51 DVFRFPGLCDYNFASDCRGSYKEFAVHLKRGPGQAEAPAGVESILLTIKD 100
101 DTIYLTRHLAVLNGAVVSTPHYSPGLLIEKSDAYTKVYSRAGLTLMWNRE 150
151 DALMLELDTKFRNHTCGLCGDYNGLQSYSEFLSDGVLFSPLEFGNMQKIN 200
201 QPDVVCEDPEEEVAPASCSEHRAECERLLTAEAFADCQDLVPLEPYLRAC 250
251 QQDRCRCPGGDTCVCSTVAEFSRQCSHAGGRPGNWRTATLCPKTCPGNLV 300
301 YLESGSPCMDTCSHLEVSSLCEEHRMDGCFCPEGTVYDDIGDSGCVPVSQ 350
351 CHCRLHGHLYTPGQEITNDCEQCVCNAGRWVCKDLPCPGTCALEGGSHIT 400
401 TFDGKTYTFHGDCYYVLAKGDHNDSYALLGELAPCGSTDKQTCLKTVVLL 450
451 ADKKKNVVVFKSDGSVLLNELQVNLPHVTASFSVFRPSSYHIMVSMAIGV 500
501 RLQVQLAPVMQLFVTLDQASQGQVQGLCGNFNGLEGDDFKTASGLVEATG 550
551 AGFANTWKAQSSCHDKLDWLDDPCSLNIESANYAEHWCSLLKKTETPFGR 600
601 CHSAVDPAEYYKRCKYDTCNCQNNEDCLCAALSSYARACTAKGVMLWGWR 650
651 EHVCNKDVGSCPNSQVFLYNLTTCQQTCRSLSEADSHCLEGFAPVDGCGC 700
701 PDHTFLDEKGRCVPLAKCSCYHRGLYLEAGDVVVRQEERCVCRDGRLHCR 750
751 QIRLIGQSCTAPKIHMDCSNLTALATSKPRALSCQTLAAGYYHTECVSGC 800
801 VCPDGLMDDGRGGCVVEKECPCVHNNDLYSSGAKIKVDCNTCTCKRGRWV 850
851 CTQAVCHGTCSIYGSGHYITFDGKYYDFDGHCSYVAVQDYCGQNSSLGSF 900
901 SIITENVPCGTTGVTCSKAIKIFMGRTELKLEDKHRVVIQRDEGHHVAYT 950
951 TREVGQYLVVESSTGIIVIWDKRTTVFIKLAPSYKGTVCGLCGNFDHRSN 1000
1001 NDFTTRDHMVVSSELDFGNSWKEAPTCPDVSTNPEPCSLNPHRRSWAEKQ 1050
1051 CSILKSSVFSICHSKVDPKPFYEACVHDSCSCDTGGDCECFCSAVASYAQ 1100
1101 ECTKEGACVFWRTPDLCPIFCDYYNPPHECEWHYEPCGNRSFETCRTING 1150
1151 IHSNISVSYLEGCYPRCPKDRPIYEEDLKKCVTADKCGCYVEDTHYPPGA 1200
1201 SVPTEETCKSCVCTNSSQVVCRPEEGKILNQTQDGAFCYWEICGPNGTVE 1250
1251 KHFNICSITTRPSTLTTFTTITLPTTPTTFTTTTTTTTPTSSTVLSTTPK 1300
1301 LCCLWSDWINEDHPSSGSDDGDRETFDGVCGAPEDIECRSVKDPHLSLEQ 1350
1351 LGQKVQCDVSVGFICKNEDQFGNGPFGLCYDYKIRVNCCWPMDKCITTPS 1400
1401 PPTTTPSPPPTSTTTLPPTTTPSPPTTTTTTPPPTTTPSPPITTTTTPPP 1450
1451 TTTPSPPISTTTTPPPTTTPSPPTTTPSPPTTTPSPPTTTTTTPPPTTTP 1500
1501 SPPTTTPITPPASTTTLPPTTTPSPPTTTTTTPPPTTTPSPPTTTPITPP 1550
1551 TSTTTLPPTTTPSPPPTTTTTPPPTTTPSPPTTTTPSPPTITTTTPPPTT 1600
1601 TPSPPTTTTTTPPPTTTPSPPTTTPITPPTSTTTLPPTTTPSPPPTTTTT 1650
1651 PPPTTTPSPPTTTTPSPPITTTTTPPPTTTPSSPITTTPSPPTTTMTTPS 1700
1701 PTTTPSSPITTTTTPSSTTTPSPPPTTMTTPSPTTTPSPPTTTMTTLPPT 1750
1751 TTSSPLTTTPLPPSITPPTFSPFSTTTPTTPCVPLCNWTGWLDSGKPNFH 1800
1801 KPGGDTELIGDVCGPGWAANISCRATMYPDVPIGQLGQTVVCDVSVGLIC 1850
1851 KNEDQKPGGVIPMAFCLNYEINVQCCECVTQPTTMTTTTTENPTPTPITT 1900
1901 TTTVTPTPTPTSTQSTTPTPITTTNTVTPTPTPTGTQTPTPTPITTTTTM 1950
1951 VTPTPTITSTQTPTPTPITTTTVTPTPTPTSTQRTTPTSITTTTTVTPTP 2000
2001 TPTGTQTPTTTPITTTTTVTPTPTPTGTQTPTTTPISTTTMVTPTPTPTG 2050
2051 TQTLTPTPITTTTTVTPTPTPTGTQTPTSTPISTTTTVTPTPTPTGTQTP 2100
2101 TLTPITTTTTVTPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTKSTTPTS 2150
2151 ITTTTMVTPTPPPTGTQTPTTTPITTTTTVTPTPTPTGTQTPTPTPITTT 2200
2201 TTVTPTPTPTGTQTPTSTPITTNTTVTPTPTPTGTPSTTLTPITTTTMVT 2250
2251 PTPTPTGTQTPTSTPISTTTTVTPTPTPTGTQTPTPTPISTTTTVTPTPT 2300
2301 PTSTQTPTTTPITTTTTVTPNPTPTGTQTPTTTPITTTTTVTPTPTPTGT 2350
2351 QTPTTTPISTTTTVTPTPTPTGTQTPTTTAITTTTTVTPTPTPTGTQTPT 2400
2401 STPITTTTTVTPTPTPTGTQTPTSTPISNTTTVTPTPTPTGTQTPTVTPI 2450
2451 TTTTTVTPTRTPTGTKSTTPTSITTTTMVTPTPTPTGTHTPTTTPITTTT 2500
2501 TVTPTPTPTGTQTPTPTPITTTTTVTPTPTPTGTQTPTSTPITTTTTVTP 2550
2551 TPTPTGTQTPTTTPITTNTTVTPTPTPTGTQTPTTVLITTTTTMTPTPTP 2600
2601 TSTKSTTVTPITTTTTVTPTPTPTGTQSTTLTPITTTTTVTPTPTPTGTQ 2650
2651 TPTTTPISTTTTVIPTPTPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTS 2700
2701 TPISTTTTVTPTATPTGTQTPTLTPITTTTTVTSTPTPTGTQTPTPTPIT 2750
2751 TTTTVTPTPTPTSTQTPTSTPITTTTTVTPTPTPTGTQTPTTTHITTTTT 2800
2801 VTPTPTPTGTQAPTPTAITTTTTVTPTPTPTGTQTPTTTPITTTTTVTPT 2850
2851 PTPTGTQSPTPTAITTTTTVTPTPTPTGTQTPTTTPITTTTTVTPTPTPT 2900
2901 GTQSTTLTPITTTTTVTPIPTPTGTQTPTSTPITTTITVTPTPTPTGTQT 2950
2951 PTPTPISTTTTVTPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQTPTTT 3000
3001 PISTTTTVTPTPTPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTPTPITT 3050
3051 TTTVTPTPTPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTPTPITTTTTV 3100
3101 TPTPTPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTTTPITTTTTVTPTP 3150
3151 TPTGTQSTTLTPITTTTTVTPTPTPTGTQTPTSTPITTTTTVTPTPTGTQ 3200
3201 TPTPTPISTTTTVTPTPTPTGTQTPTMTPITTTTTVTPTPTPTGTQTPTT 3250
3251 TPISTTTTVTPTPTPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTTTPIT 3300
3301 TTTTVTPTPTPTGTQSTTLTPITTTTTVTPTPTPTGTQTPTPTPISTTTT 3350
3351 VTPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQTPTTTPISTTTTVTPT 3400
3401 PTPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTTTPITTTTTVTPTPTPT 3450
3451 GTQAPTPTAITTTTTVTPTPTPTGTQTPTTTPITTTTMVTPTPTPTGTQT 3500
3501 PTSTPITTTTTVTPTPTPTGTQTPTPTPISTTTTVTPTPTPTGTQTPTTT 3550
3551 PITTTTTVTPTPTPTGTQTPTTTPISTTTTVTPTPTPTGTQTPTSTPITT 3600
3601 TTTVTPTPTPTGTQTPTPTPITTTTTVTPTPTPTGTQTPTSTPITTTTTV 3650
3651 TPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQSTTLTPITTTTTVTPTP 3700
3701 TPTGTQTPTSTPITTTTTVTPTPTPTGTQTPTPTPISTTSTVTPTPTPTG 3750
3751 TQTPTMTPITTTTTVTPTPTPTGTQTPTSTPITTTTTVTPTPTPTGTQTP 3800
3801 TMTPITTTTTVTPTPTPTGTQAPTPTAITTTTTVTPTPTPTGTQTPTTTP 3850
3851 ITTTTTVTPTPTPTGTQSTTLTPITTTTTVTPTPTPTGTQTPTPTPISTT 3900
3901 TTVTPTPTPTGTQTPTMTPITTTTTVTPTPTPTGTQTPTTTPISTTTTVT 3950
3951 PTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQTPTTTPISTTTTVTPTPT 4000
4001 PTGTQTPTTTPITTTTTVTPTPTPTGTQTPTTTPISTTTTVTPTPTPTGT 4050
4051 QTPTSTPITTTTTVTPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQAPT 4100
4101 PTAITTTSTVTPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQSPTPTAI 4150
4151 TTTTTVTPTPTPTGTQTPTLTPITTTTTVTPTPTPTGTQTPTPTPISTTT 4200
4201 TVTPTPTPTGTQTPTTTPITTTTTVTPTPTPTGTQTPTTVLITTTTTMTP 4250
4251 TPTPTSTKSTTVTPITTTTTVTATPTPTGTQTPTMIPISTTTTVTPTPTP 4300
4301 TTGSTGPPTHTSTAPIAELTTSNPPPESSTPQTSRSTSSPLTESTTLLST 4350
4351 LPPAIEMTSTAPPSTPTAPTTTSGGHTLSPPPSTTTSPPGTPTRGTTTGS 4400
4401 SSAPTPSTVQTTTTSAWTPTPTPLSTPSIIRTTGLRPYPSSVLICCVLND 4450
4451 TYYAPGEEVYNGTYGDTCYFVNCSLSCTLEFYNWSCPSTPSPTPTPSKST 4500
4501 PTPSKPSSTPSKPTPGTKPPECPDFDPPRQENETWWLCDCFMATCKYNNT 4550
4551 VEIVKVECEPPPMPTCSNGLQPVRVEDPDGCCWHWECDCYCTGWGDPHYV 4600
4601 TFDGLYYSYQGNCTYVLVEEISPSVDNFGVYIDNYHCDPNDKVSCPRTLI 4650
4651 VRHETQEVLIKTVHMMPMQVQVQVNRQAVALPYKKYGLEVYQSGINYVVD 4700
4701 IPELGVLVSYNGLSFSVRLPYHRFGNNTKGQCGTCTNTTSDDCILPSGEI 4750
4751 VSNCEAAADQWLVNDPSKPHCPHSSSTTKRPAVTVPGGGKTTPHKDCTPS 4800
4801 PLCQLIKDSLFAQCHALVPPQHYYDACVFDSCFMPGSSLECASLQAYAAL 4850
4851 CAQQNICLDWRNHTHGACLVECPSHREYQACGPAEEPTCKSSSSQQNNTV 4900
4901 LVEGCFCPEGTMNYAPGFDVCVKTCGCVGPDNVPREFGEHFEFDCKNCVC 4950
4951 LEGGSGIICQPKRCSQKPVTHCVEDGTYLATEVNPADTCCNITVCKCNTS 5000
5001 LCKEKPSVCPLGFEVKSKMVPGRCCPFYWCESKGVCVHGNAEYQPGSPVY 5050
5051 SSKCQDCVCTDKVDNNTLLNVIACTHVPCNTSCSPGFELMEAPGECCKKC 5100
5101 EQTHCIIKRPDNQHVILKPGDFKSDPKNNCTFFSCVKIHNQLISSVSNIT 5150
5151 CPNFDASICIPGSITFMPNGCCKTCTPRNETRVPCSTVPVTTEVSYAGCT 5200
5201 KTVLMNHCSGSCGTFVMYSAKAQALDHSCSCCKEEKTSQREVVLSCPNGG 5250
5251 SLTHTYTHIESCQCQDTVCGLPTGTSRRARRSPRHLGSG 5289
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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