 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O75592 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MMMCAATASPAAASSGLGGDGFYPAATFSSSPAPGALFMPVPDGSVAAAG 50
51 LGLGLPAADSRGHYQLLLSGRALADRYRRIYTAALNDRDQGGGSAGHPAS 100
101 RNKKILNKKKLKRKQKSKSKVKTRSKSENLENTVIIPDIKLHSNPSAFNI 150
151 YCNVRHCVLEWQKKEISLAAASKNSVQSGESDSDEEEESKEPPIKLPKII 200
201 EVGLCEVFELIKETRFSHPSLCLRSLQALLNVLQGQQPEGLQSEPPEVLE 250
251 SLFQLLLEITVRSTGMNDSTGQSLTALSCACLFSLVASWGETGRTLQAIS 300
301 AILTNNGSHACQTIQVPTILNSLQRSVQAVLVGKIQIQDWFSNGIKKAAL 350
351 MHKWPLKEISVDEDDQCLLQNDGFFLYLLCKDGLYKIGSGYSGTVRGHIY 400
401 NSTSRIRNRKEKKSWLGYAQGYLLYRDVNNHSMTAIRISPETLEQDGTVM 450
451 LPDCHTEGQNILFTDGEYINQIAASRDDGFVVRIFATSTEPVLQQELQLK 500
501 LARKCLHACGISLFDLEKDLHIISTGFDEESAILGAGREFALMKTANGKI 550
551 YYTGKYQSLGIKQGGPSAGKWVELPITKSPKIVHFSVGHDGSHALLVAED 600
601 GSIFFTGSASKGEDGESTKSRRQSKPYKPKKIIKMEGKIVVYTACNNGSS 650
651 SVISKDGELYMFGKDAIYSDSSSLVTDLKGHFVTQVAMGKAHTCVLMKNG 700
701 EVWTFGVNNKGQCGRDTGAMNQGGKGFGVENMATAMDEDLEEELDEKDEK 750
751 SMMCPPGMHKWKLEQCMVCTVCGDCTGYGASCVSSGRPDRVPGGICGCGS 800
801 GESGCAVCGCCKACARELDGQEARQRGILDAVKEMIPLDLLLAVPVPGVN 850
851 IEEHLQLRQEEKRQRVIRRHRLEEGRGPLVFAGPIFMNHREQALARLRSH 900
901 PAQLKHKRDKHKDGSGERGEKDASKITTYPPGSVRFDCELRAVQVSCGFH 950
951 HSVVLMENGDVYTFGYGQHGQLGHGDVNSRGCPTLVQALPGPSTQVTAGS 1000
1001 NHTAVLLMDGQVFTFGSFSKGQLGRPILDVPYWNAKPAPMPNIGSKYGRK 1050
1051 ATWIGASGDQTFLRIDEALINSHVLATSEIFASKHIIGLVPASISEPPPF 1100
1101 KCLLINKVDGSCKTFNDSEQEDLQGFGVCLDPVYDVIWRFRPNTRELWCY 1150
1151 NAVVADARLPSAADMQSRCSILSPELALPTGSRALTTRSHAALHILGCLD 1200
1201 TLAAMQDLKMGVASTEEETQAVMKVYSKEDYSVVNRFESHGGGWGYSAHS 1250
1251 VEAIRFSADTDILLGGLGLFGGRGEYTAKIKLFELGPDGGDHETDGDLLA 1300
1301 ETDVLAYDCAAREKYAMMFDEPVLLQAGWWYVAWARVSGPSSDCGSHGQA 1350
1351 SITTDDGVVFQFKSSKKSNNGTDVNAGQIPQLLYRLPTSDGSASKGKQQT 1400
1401 SEPVHILKRSFARTVSVECFESLLSILHWSWTTLVLGVEELRGLKGFQFT 1450
1451 ATLLDLERLRFVGTCCLRLLRVYTCEIYPVSATGKAVVEETSKLAECIGK 1500
1501 TRTLLRKILSEGVDHCMVKLDNDPQGYLSQPLSLLEAVLQECHNTFTACF 1550
1551 HSFYPTPALQWACLCDLLNCLDQDIQEANFKTSSSRLLAAVMSALCHTSV 1600
1601 KLTSIFPIAYDGEVLLRSIVKQVSTENDSTLVHRFPLLVAHMEKLSQSEE 1650
1651 NISGMTSFREVLEKMLVIVVLPVRNSLRRENELFSSHLVSNTCGLLASIV 1700
1701 SELTASALGSEVDGLNSLHSVKASANRFTKTSQGRSWNTGNGSPDAICFS 1750
1751 VDKPGIVVVGFSVYGGGGIHEYELEVLVDDSEHAGDSTHSHRWTSLELVK 1800
1801 GTYTTDDSPSDIAEIRLDKVVPLKENVKYAVRLRNYGSRTANGDGGMTTV 1850
1851 QCPDGVTFTFSTCSLSSNGTNQTRGQIPQILYYRSEFDGDLQSQLLSKAN 1900
1901 EEDKNCSRALSVVSTVVRASKDLLHRALAVDADDIPELLSSSSLFSMLLP 1950
1951 LIIAYIGPVAAAIPKVAVEVFGLVQQLLPSVAILNQKYAPPAFNPNQSTD 2000
2001 STTGNQPEQGLSACTTSSHYAVIESEHPYKPACVMHYKVTFPECVRWMTI 2050
2051 EFDPQCGTAQSEDVLRLLIPVRTVQNSGYGPKLTSVHENLNSWIELKKFS 2100
2101 GSSGWPTMVLVLPGNEALFSLETASDYVKDDKASFYGFKCFAIGYEFSPG 2150
2151 PDEGVIQLEKELANLGGVCAAALMKKDLALPIGNELEEDLEILEEAALQV 2200
2201 CKTHSGILGKGLALSHSPTILEALEGNLPLQIQSNEQSFLDDFIACVPGS 2250
2251 SGGRLARWLQPDSYADPQKTSLILNKDDIRCGWPTTITVQTKDQYGDVVH 2300
2301 VPNMKVEVKAVPVSQKKMSLQQDQAKKPQRIPGSPAVTAASSNTDMTYGG 2350
2351 LASPKLDVSYEPMIVKEARYIAITMMKVYENYSFEELRFASPTPKRPSEN 2400
2401 MLIRVNNDGTYCANWTPGAIGLYTLHVTIDGIEIDAGLEVKVKDPPKGMI 2450
2451 PPGTQLVKPKSEPQPNKVRKFVAKDSAGLRIRSHPSLQSEQIGIVKVNGT 2500
2501 ITFIDEIHNDDGVWLRLNDETIKKYVPNMNGYTEAWCLSFNQHLGKSLLV 2550
2551 PVDESKTNTDDFFKDINSCCPQEATMQEQDMPFLRGGPGMYKVVKTGPSG 2600
2601 HNIRSCPNLRGIPIGMLVLGNKVKAVGEVTNSEGTWVQLDQNSMVEFCES 2650
2651 DEGEAWSLARDRGGNQYLRHEDEQALLDQNSQTPPPSPFSVQAFNKGASC 2700
2701 SAQGFDYGLGNSKGDRGNISTSSKPASTSGKSELSSKHSRSLKPDGRMSR 2750
2751 TTADQKKPRGTESLSASESLILKSDAAKLRSDSHSRSLSPNHNTLQTLKS 2800
2801 DGRMPSSSRAESPGPGSRLSSPKPKTLPANRSSPSGASSPRSSSPHDKNL 2850
2851 PQKSTAPVKTKLDPPRERSKSDSYTLDPDTLRKKKMPLTEPLRGRSTSPK 2900
2901 PKSVPKDSTDSPGSENRAPSPHVVQENLHSEVVEVCTSSTLKTNSLTDST 2950
2951 CDDSSEFKSVDEGSNKVHFSIGKAPLKDEQEMRASPKISRKCANRHTRPK 3000
3001 KEKSSFLFKGDGSKPLEPAKQAMSPSVAECARAVFASFLWHEGIVHDAMA 3050
3051 CSSFLKFHPELSKEHAPIRSSLNSQQPTEEKETKLKNRHSLEISSALNMF 3100
3101 NIAPHGPDISKMGSINKNKVLSMLKEPPLHEKCEDGKTETTFEMSMHNTM 3150
3151 KSKSPLPLTLQHLVAFWEDISLATIKAASQNMIFPSPGSCAVLKKKECEK 3200
3201 ENKKSKKEKKKKEKAEVRPRGNLFGEMAQLAVGGPEKDTICELCGESHPY 3250
3251 PVTYHMRQAHPGCGRYAGGQGYNSIGHFCGGWAGNCGDGGIGGSTWYLVC 3300
3301 DRCREKYLREKQAAAREKVKQSRRKPMQVKTPRALPTMEAHQVIKANALF 3350
3351 LLSLSSAAEPSILCYHPAKPFQSQLPSVKEGISEDLPVKMPCLYLQTLAR 3400
3401 HHHENFVGYQDDNLFQDEMRYLRSTSVPAPYISVTPDASPNVFEEPESNM 3450
3451 KSMPPSLETSPITDTDLAKRTVFQRSYSVVASEYDKQHSILPARVKAIPR 3500
3501 RRVNSGDTEVGSSLLRHPSPELSRLISAHSSLSKGERNFQWPVLAFVIQH 3550
3551 HDLEGLEIAMKQALRKSACRVFAMEAFNWLLCNVIQTTSLHDILWHFVAS 3600
3601 LTPAPVEPEEEEDEENKTSKENSEQEKDTRVCEHPLSDIVIAGEAAHPLP 3650
3651 HTFHRLLQTISDLMMSLPSGSSLQQMALRCWSLKFKQSDHQFLHQSNVFH 3700
3701 HINNILSKSDDGDSEESFSISIQSGFEAMSQELCIVMCLKDLTSIVDIKT 3750
3751 SSRPAMIGSLTDGSTETFWESGDEDKNKTKNITINCVKGINARYVSVHVD 3800
3801 NSRDLGNKVTSMTFLTGKAVEDLCRIKQVDLDSRHIGWVTSELPGGDNHI 3850
3851 IKIELKGPENTLRVRQVKVLGWKDGESTKIAGQISASVAQQRNCEAETLR 3900
3901 VFRLITSQVFGKLISGDAEPTPEQEEKALLSSPEGEEKVYNATSDADLKE 3950
3951 HMVGIIFSRSKLTNLQKQVCAHIVQAIRMEATRVREEWEHAISSKENANS 4000
4001 QPNDEDASSDAYCFELLSMVLALSGSNVGRQYLAQQLTLLQDLFSLLHTA 4050
4051 SPRVQRQVTSLLRRVLPEVTPSRLASIIGVKSLPPADISDIIHSTEKGDW 4100
4101 NKLGILDMFLGCIAKALTVQLKAKGTTITGTAGTTVGKGVTTVTLPMIFN 4150
4151 SSYLRRGESHWWMKGSTPTQISEIIIKLIKDMAAGHLSEAWSRVTKNAIA 4200
4201 ETIIALTKMEEEFRSPVRCIATTRLWLALASLCVLDQDHVDRLSSGRWMG 4250
4251 KDGQQKQMPMCDNHDDGETAAIILCNVCGNLCTDCDRFLHLHRRTKTHQR 4300
4301 QVFKEEEEAIKVDLHEGCGRTKLFWLMALADSKTMKAMVEFREHTGKPTT 4350
4351 SSSEACRFCGSRSGTELSAVGSVCSDADCQEYAKIACSKTHPCGHPCGGV 4400
4401 KNEEHCLPCLHGCDKSATSLKQDADDMCMICFTEALSAAPAIQLDCSHIF 4450
4451 HLQCCRRVLENRWLGPRITFGFISCPICKNKINHIVLKDLLDPIKELYED 4500
4501 VRRKALMRLEYEGLHKSEAITTPGVRFYNDPAGYAMNRYAYYVCYKCRKA 4550
4551 YFGGEARCDAEAGRGDDYDPRELICGACSDVSRAQMCPKHGTDFLEYKCR 4600
4601 YCCSVAVFFCFGTTHFCNACHDDFQRMTSIPKEELPHCPAGPKGKQLEGT 4650
4651 ECPLHVVHPPTGEEFALGCGVCRNAHTF 4678
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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