 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q7TPH6 from www.uniprot.org...
The NucPred score for your sequence is 0.83 (see score help below)
1 MMMCAATASPAAASSGPGGDGFFAAATISSSPAPGALFMPVPDGSVAAAG 50
51 LGLGLPTTDSRGHYQLLLSGRALADRYRRIYTTALSDRDQAGSSTGHPAS 100
101 RNKKILNKKKLKRKQKSKSKVKTRSKSENVENTVIIPDIKLHSNPSAFNI 150
151 YCNVRHCVLEWQKKETSLAAASKNSVQSGESDSDEEEESREPPIKLPKII 200
201 EVGLCEVFELIKETRFSHPSLCLRSLQALLNVLQGQQPEGLQSEPPEVLE 250
251 SLFQLLLEITVRSTGMNDSTGQSLTALSCACLFSLVASWGETGRTLQAIS 300
301 AILTNNGSHACQTIQVPTILNSLQRSVQAVLVGKIQVQDWFSNGIKKAAL 350
351 MHKWPLKEVSVDEDDQCLLQNDGFFLYLLCKDGLYKIGSGYSGTVRGHIY 400
401 NSTSRIRNRKEKKSWLGYAQGYLLYRDLNNHSMTAIRISPETLEQDGTVL 450
451 LPDCHTEGQNILFTDGEYINQIAASRDDGFVVRIFATSTEPVLQQELQLK 500
501 LARKCLHACGISLFDLEKDLHIISTGFDEESAILGAGREFALMKTANGKI 550
551 YYTGKYQSLGIKQGGPSAGKWVELPITKSPKIVHFSVGHDGSHALLVAED 600
601 GSVFFTGSASKGEDGESTKSRRQSKPYKPKKIIKMEGKIVVYTACNNGSS 650
651 SVISKDGELYMFGKDAIYSDSSSLVSDLKGHFVTQVAMGKAHTCVLMKNG 700
701 EVWTFGVNNKGQCGRDTGAMNQGGKGFGVENMATAMDEDLEEELDEKDEK 750
751 SMMCPPGMHKWKLEQCMVCTVCGDCTGYGASCVSSGRPDRVPGGICGCGS 800
801 GESGCAVCGCCKACARELDGQEARQRGILDAVKEMIPLDLLLAVPVPGVN 850
851 IEEHLQLRQEEKRQRVIRRHRLEDGRGPLVFAGPIFMNHREQALARLRSH 900
901 PAQLKHKRDKHKDGSGDRGEKDASKITTYPPGSVRFDCELRAVQVSCGFH 950
951 HSVVLMENGDVYTFGYGQHGQLGHGDVNSRGCPTLVQALPGPSTQVTAGS 1000
1001 NHTAVLLMDGQVFTFGSFSKGQLGRPILDIPYWNAKPAPMPNIGSKYGRK 1050
1051 ATWIGASGDQTFLRIDEALINSHVLATSEIFASKHIIGLVPASISEPPPF 1100
1101 KCLLINKVDGSCKTFNDSEQEDLQGFGVCLDPVYDVLWRFRPSTRELWCY 1150
1151 NAVVADARLPSATDMQSRCSILSPELALPTGSRALTTRSHAALHILGCLD 1200
1201 TLAAMQDLKMGIASTEEETQAVMKVYSKEDYSVVNRFESHGGGWGYSAHS 1250
1251 VEAIRFSADTDILLGGLGLFGGRGEYTAKIKLFELGPDGGDHETDGDLLA 1300
1301 ETDVLAYDCAAREKYAMMFDEPVLLQAGWWYVAWARVSGPSSDCGSHGQA 1350
1351 SITTDDGVIFQFKSSKKSNNGTDVNAGQIPQLLYRLPTSDGSTSKGKQQT 1400
1401 SEPVHILKRSFARTVSVECFESLLSILHWSWTTLVLGVEELRGLKGFQFT 1450
1451 ATLLDLERLRFVGTCCLRLLRVYTCEIYPVSATGKAVVEETSKLAECIGK 1500
1501 TRTLLRKILSEGVDHCMVKLDNDPQGYLSQPLRLLEAVLQECHNTFTACF 1550
1551 HSFYPTPALQWACLCDLLNCLDQEANFKTSSSRLLAAVMSALCHTSVKLT 1600
1601 SLFPIAYDGEVLLRSIVKQVSTENDSTLVHRFPLLVGHMEKLSQSEENIS 1650
1651 GMTSFREVLEKMLVIVVLPVRNSLRRESELFSSHLVSNTCGLLASIVSEL 1700
1701 TASALGSEVDGLNSLHSVKASANRFTKTSQGRSWNTGNGSPDAICFAVDK 1750
1751 PGIVVVGFAVYGGGGIHEYELEVLVDDSEHAGDSTHSHRWTSLELVKGTY 1800
1801 TTDDSPSDIAEIRLDKVVPLKENVKYAVRLRNYGSRTANGDGGMTTVQCP 1850
1851 DGVTFTFSTCSLSSNGTNQTRGQIPQILYYRSEFDGDLQSQLLSKANEED 1900
1901 KNCSRALSVVSTVVRAAKDLLHRALAVDADDIPELLSSSSLFSMLLPLII 1950
1951 AYIGPVAAAIPKVAVEVFGLVQQLLPSVAILNQKYAPPAFNPNQSTDSTT 2000
2001 GNQPEQGLSACTTSNHYAVIESEHPYKPACVMHYKVTFPECVRWMTIEFD 2050
2051 PQCGTAQSEDVIRLLIPVRTIQNSGYGAKLTSVHENLNSWVELKKYSGSS 2100
2101 GWPTMVLVLPGNEALFSLETASDYVKDDKASFYGFKCFAIGYEFSPGPDE 2150
2151 GVIQLEKELANLGGVCAAALMKKDLALPVGNELEEDLEILEEAALQVCKT 2200
2201 HSGILGKGLALSHSPTILEALEGNLPLQIQSNEQSFLDDFIACVPGSSGG 2250
2251 RLARWLQPDSYADPQKTSLILNKDDIRCGWPTTITVQTKDQYGDVVHVPN 2300
2301 MKVEVKAVPVSQKKTSLQQDQGKKCQRIPGSPSAAASSADMTFGGLASPK 2350
2351 LDVSYEPMIVKEARYIAITMMKVYENYSFEELRFASPTPKRPSENMLIRV 2400
2401 NNDGTYCANWTPGAIGLYTVHVTIDGIEIDAGLEVKVKDPPKGMIPPGTQ 2450
2451 LVKPKADPQPNKIRKFVAKDSAGLRIRSHPSLQSEQIGIVRVNGTITFID 2500
2501 EIHNDDGVWLRLNEETIKKYVPNMNGYTEAWCLSFNQHLGKSLLVPVDNI 2550
2551 FNASQGVRDLDVFSWTSKAFFPQEPKTNTDDFFKDMNSCGPQEATMQERD 2600
2601 HPFLRGGPGMYKVVKTGPSGHNIRSCPNLRGIPIGMLVLGNKVKAVGEVT 2650
2651 NSEGAWVQLDKNSMVEFCESDEGEAWSLARDRGGNQYLRHEDEQVLLDQN 2700
2701 SQPPPPSPFSVQAFNKGASCSAQGFDYGLGNNKGDQLSAILNSIQSRPNL 2750
2751 PAPSIFDQAAKPPSSLVHSPFVFGQPLSFQQRQLQSDRGTISTSSRPVST 2800
2801 SGKSELPSKHSRSVKPDGHVSRTPADQKKPRGTEGLSASESLMLKSDAAK 2850
2851 LRSDSHSRSLSPNHNTLQTLKSDGRTSSGFRAESPGPGSRSSSPKPKPLP 2900
2901 TPRSSPSGASSPRSSSPQDKNLPQKSTAPAKTKLDPPRERSKSDSYTLDP 2950
2951 DTLRKKKMPLTEPLRGRSTSPKPKPVPKDPKDSPGSENRAPSPHVVQENL 3000
3001 HSEVVEVCTSSTLKTNGVTDSTCDDSGDLKSVDEGSNKVHFSIGKAPLKD 3050
3051 EQEMRASPKISRKCANRHTRPKKEKSNFLFKGDGTKSLEPAKQAMSPSVA 3100
3101 ECARAVFASFLWHEGIVHDAMACSSFLKFNPDLSKEHAPIRSSLNSQPPT 3150
3151 EEKEIKLKNRHSLEISSALNMFNIAPHGPDISKMGSINKNKVLSMLKEPP 3200
3201 LHEKCEDGKSEATFEMSMHHTMKSKSPLPLTLQHLVAFWEDISLATIKAA 3250
3251 SQNMIFPSPGSCAVLKKKECEKENKKTKKEKKKKEKTEIRPRGNLFGEMA 3300
3301 QLAVGGPEKDTICELCGESHPYPVTYHMRQAHPGCGRYAGGQGYNSIGHF 3350
3351 CGGWAGNCGDGGMGGSTWYLVCDRCREKYLREKQAAAREKVKQSRRKPMQ 3400
3401 VKTPRALPTMEAHQVIKANALFLLSLSSAAEPSILCYHPAKPFQSQLPIV 3450
3451 KEGVSEDLPVKMPCLYLQTLARHHHENFVGYQDDNLFQDEMRYLRSTSVP 3500
3501 APYISVTPDASPNVFEEPESNMKSMPPSLETSPITDTDLAKRTVFQRSYS 3550
3551 VVASEYDKQHSILPARVKAIPRRRVNSGDTVGSSLLRHPSPELSRLISAH 3600
3601 SSLSKGERNFQWPVLAFVIQHHDLEGLEIAMKQALRKSACRVFAMEAFNW 3650
3651 LLCNVIQTTSLHDILWHFVAALTPSPVEAEEDEDEDNKSNKENAEQEKDT 3700
3701 RVCEHPLSDIVIAGEAAHPLPHTFHRLLQTISDLMMSLPSGSSLQQMALR 3750
3751 CWSLKFKQSDHQFLHQSNVFHHINNILSKSDDGDSEESFSISVQSGFEAM 3800
3801 SQELCIVMCLKDLTSIVDIKTSSRPAMIGSLTDGSTETFWESGDEDKNKT 3850
3851 KNITINCVKGINARYVSVHVDNSRDLGNKVTSMTFLTGKAVEELCRIKQV 3900
3901 DLDSRHIGWVTSELPGGDNQIIKIELKGPENTLRVRQVKVLGWKDGESTK 3950
3951 IAGQISASVAQQRSCEAETLRVFRLITSQVFGKLISGDAEPTPEQEEKAL 4000
4001 LSSPEGEEKVYNATSDADLKEHMVGIIFSRSKLTNLQKQVCAHIVQAIRM 4050
4051 EATRVREEWEHAISSKENANSQPSDEDASSDAYCFELLSMVLALSGSNVG 4100
4101 RQYLAQQLTLLQDLFSLLHTASPRVQRQVTSLLRRVLPEVTPNRLASIIG 4150
4151 VKSLPPADISDIIHSTEKGDWNKLGILDMFLGCIAKALTVQLKAKGTTIT 4200
4201 GTAGTTVGKGVTTVTLPMIFNSSYLRRGESHWWMKGSTPTQISEIIIRLI 4250
4251 KDMAAGHLSEAWSRVTKNAIAETIIALTKMEEEFRSPVRCIATTRLWLAL 4300
4301 ASLCVLDQDHVDRLSSGRWMGKDGQQKQMPMCDNHDDGETAAIILCNICG 4350
4351 NLCTDCDRFLHLHRRTKTHQRQVFKEEEEAIKVDLHEGCGRTKLFWLMAL 4400
4401 ADSKTMKAMVEFREHTGKPTTSSSEACRFCGSRSGTELSAVGSVCSDADC 4450
4451 QEYAKIACSKTHPCGHPCGGVRNEEHCLPCLHGCDKSATTLKQDADDMCM 4500
4501 ICFTEALSAAPAIQLDCSHVFHLQCCRRVLENRWLGPRITFGFISCPICK 4550
4551 NKINHIVLKDLLDPIKELYEDVRRKALMRLEYEGLHKSEAITTPGVRFYN 4600
4601 DAAGYAMNRYAYYVCYKCRKAYFGGEARCDAEAGQGDDYDPRELICGACS 4650
4651 DVSRAQMCPKHGTDFLEYKCRYCCSVAVFFCFGTTHFCNACHDDFQRMTS 4700
4701 IPKEELPHCPAGPKGKQLEGTECPLHVVHPPTGEEFALGCGVCRNAHTF 4749
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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