 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O35161 from www.uniprot.org...
The NucPred score for your sequence is 0.46 (see score help below)
1 MAPSSPRVLPALVLLAAAALPALELGAAAWELRVPGGARAFALGPGWSYR 50
51 LDTTRTPRELLDVSREGPAAGRRLGLGAGTLGCARLAGRLLPLQVRLVAR 100
101 GAPTAPSLVLRARAYGARCGVRLLRRSARGAELRSPAVRSVPGLGDALCF 150
151 PAAGGGAASLTSVLEAITNFPACSCPPVAGTGCRRGPICLRPGGSAELRL 200
201 VCALGRAAGAVWVELVIEATSGTPSESPSVSPSLLNLSQPRAGVVRRSRR 250
251 GTGSSTSPQFPLPSYQVSVPENEPAGTAVIELRAHDPDEGDAGRLSYQME 300
301 ALFDERSNGYFLIDAATGAVTTARSLDRETKDTHVLKVSAVDHGSPRRSA 350
351 ATYLTVTVSDTNDHSPVFEQSEYRERIRENLEVGYEVLTIRATDGDAPSN 400
401 ANMRYRLLEGAGGVFEIDARSGVVRTRAVVDREEAAEYQLLVEANDQGRN 450
451 PGPLSASATVHIVVEDENDNYPQFSEKRYVVQVPEDVAVNTAVLRVQATD 500
501 RDQGQNAAIHYSIVSGNLKGQFYLHSLSGSLDVINPLDFEAIREYTLRIK 550
551 AQDGGRPPLINSSGLVSVQVLDVNDNAPIFVSSPFQAAVLENVPLGHSVL 600
601 HIQAVDADAGENARLQYRLVDTASTIVGGSSVDSENPASAPDFPFQIHNS 650
651 SGWITVCAELDREEVEHYSFGVEAVDHGSPAMSSSASVSITVLDVNDNDP 700
701 MFTQPVYELRLNEDAAVGSSVLTLRARDRDANSVITYQLTGGNTRNRFAL 750
751 SSQSGGGLITLALPLDYKQERQYVLAVTASDGTRSHTAQVFINVTDANTH 800
801 RPVFQSSHYTVSVSEDRPVGTSIATISATDEDTGENARITYVLEDPVPQF 850
851 RIDPDTGTIYTMTELDYEDQAAYTLAITAQDNGIPQKSDTTSLEILILDA 900
901 NDNAPRFLRDFYQGSVFEDAPPSTSVLQVSATDRDSGPNGRLLYTFQGGD 950
951 DGDGDFYIEPTSGVIRTQRRLDRENVAVYNLWALAVDRGSPNPLSASVGI 1000
1001 QVSVLDINDNPPVFEKDELELFVEENSPVGSVVARIRANDPDEGPNAQIM 1050
1051 YQIVEGNVPEVFQLDLLSGDLRALVELDFEVRRDYMLVVQATSAPLVSRA 1100
1101 TVHIRLLDQNDNPPELPDFQILFNNYVTNKSNSFPSGVIGRIPAHDPDLS 1150
1151 DSLNYTFLQGNELSLLLLDPATGELQLSRDLDNNRPLEALMEVSVSDGIH 1200
1201 SVTALCTLRVTIITDDMLTNSITVRLENMSQEKFLSPLLSLFVEGVATVL 1250
1251 STTKDDIFVFNIQNDTDVSSNILNVTFSALLPGGTRGRFFPSEDLQEQIY 1300
1301 LNRTLLTTISAQRVLPFDDNICLREPCENYMKCVSVLRFDSSAPFISSTT 1350
1351 VLFRPIHPITGLRCRCPPGFTGDYCETEIDLCYSNPCGANGRCRSREGGY 1400
1401 TCECFEDFTGEHCQVNVRSGRCASGVCKNGGTCVNLLIGGFHCVCPPGEY 1450
1451 EHPYCEVSTRSFPPQSFVTFRGLRQRFHFTVSLAFATQDRNALLLYNGRF 1500
1501 NEKHDFIALEIVEEQLQLTFSAGETTTTVTPQVPGGVSDGRWHSVLVQYY 1550
1551 NKPNIGHLGLPHGPSGEKVAVVTVDDCDAAVAVHFGSYVGNYSCAAQGTQ 1600
1601 SGSKKSLDLTGPLLLGGVPNLPEDFPVHSRQFVGCMRNLSIDGRIVDMAA 1650
1651 FIANNGTRAGCASQRNFCDGTSCQNGGTCVNRWNTYLCECPLRFGGKNCE 1700
1701 QAMPHPQRFTGESVVLWSDLDITISVPWYLGLMFRTRKEDGVLMEATAGT 1750
1751 SSRLHLQILNSYIRFEVSYGPSDVASMQLSKSRITDGGWHHLLIELRSAK 1800
1801 EGKDIKYLAVMTLDYGMDQSTVQIGNQLPGLKMRTIVIGGVTEDKVSVRH 1850
1851 GFRGCMQGVRMGETSTNIATLNMNDALKVRVKDGCDVEDPCASSPCPPHS 1900
1901 HCRDTWDSYSCICDRGYFGKKCVDACLLNPCKHVAACVRSPNTPRGYSCE 1950
1951 CGPGHYGQYCENKVDLPCPKGWWGNPVCGPCHCAVSQGFDPDCNKTNGQC 2000
2001 QCKENYYKPPAQDACLPCDCFPHGSHSRACDMDTGQCACKPGVIGRQCNR 2050
2051 CDNPFAEVTSLGCEVIYNGCPRAFEAGIWWPQTKFGQPAAVPCPKGSVGN 2100
2101 AVRHCSGEKGWLPPELFNCTSGSFVDLKALNEKLNRNETRMDGNRSLRLA 2150
2151 KALRNATQGNSTLFGNDVRTAYQLLARILQHESRQQGFDLAATREANFHE 2200
2201 DVVHTGSALLAPATEASWEQIQRSEAGAAQLLRHFEAYFSNVARNVKRTY 2250
2251 LRPFVIVTANMILAVDIFDKLNFTGAQVPRFEDIQEELPRELESSVSFPA 2300
2301 DTFKPPEKKEGPVVRLTNRRTTPLTAQPEPRAERETSSSRRRRHPDEPGQ 2350
2351 FAVALVVIYRTLGQLLPEHYDPDHRSLRLPNRPVINTPVVSAMVYSEGTP 2400
2401 LPSSLQRPILVEFSLLETEERSKPVCVFWNHSLDTGGTGGWSAKGCELLS 2450
2451 RNRTHVTCQCSHSASCAVLMDISRREHGEVLPLKIITYAALSLSLVALLV 2500
2501 AFVLLSLVRTLRSNLHSIHKNLITALFFSQLIFMVGINQTENPFLCTVVA 2550
2551 ILLHYVSMGTFAWTLVENLHVYRMLTEVRNIDTGPMRFYHVVGWGIPAIV 2600
2601 TGLAVGLDPQGYGNPDFCWLSLQDTLIWSFAGPVGTVIIINTVIFVLSAK 2650
2651 VSCQRKHHYYERKGVVSMLRTAFLLLLLVTATWLLGLLAVNSDTLSFHYL 2700
2701 FAAFSCLQGIFVLLFHCVAHREVRKHLRAVLAGKKLQLDDSATTRATLLT 2750
2751 RSLNCNNTYSEGPDMLRTALGESTASLDSTTRDEGVQKLSVSSGPARGNH 2800
2801 GEPDASFIPRNSKKAHGPDSDSDSELSLDEHSSSYASSHTSDSEDDGGEA 2850
2851 EDKWNPAGGPAHSTPKADALANHVPAGWPDESLAGSDSEELDTEPHLKVE 2900
2901 TKVSVELHRQAQGNHCGDRPSDPESGVLAKPVAVLSSQPQEQRKGILKNK 2950
2951 VTYPPPLPEQPLKSRLREKLADCEQSPTSSRTSSLGSGDGVHATDCVITI 3000
3001 KTPRREPGREHLNGVAMNVRTGSAQANGSDSEKP 3034
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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