 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UPY3 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MKSPALQPLSMAGLQLMTPASSPMGPFFGLPWQQEAIHDNIYTPRKYQVE 50
51 LLEAALDHNTIVCLNTGSGKTFIAVLLTKELSYQIRGDFSRNGKRTVFLV 100
101 NSANQVAQQVSAVRTHSDLKVGEYSNLEVNASWTKERWNQEFTKHQVLIM 150
151 TCYVALNVLKNGYLSLSDINLLVFDECHLAILDHPYREIMKLCENCPSCP 200
201 RILGLTASILNGKCDPEELEEKIQKLEKILKSNAETATDLVVLDRYTSQP 250
251 CEIVVDCGPFTDRSGLYERLLMELEEALNFINDCNISVHSKERDSTLISK 300
301 QILSDCRAVLVVLGPWCADKVAGMMVRELQKYIKHEQEELHRKFLLFTDT 350
351 FLRKIHALCEEHFSPASLDLKFVTPKVIKLLEILRKYKPYERQQFESVEW 400
401 YNNRNQDNYVSWSDSEDDDEDEEIEEKEKPETNFPSPFTNILCGIIFVER 450
451 RYTAVVLNRLIKEAGKQDPELAYISSNFITGHGIGKNQPRNKQMEAEFRK 500
501 QEEVLRKFRAHETNLLIATSIVEEGVDIPKCNLVVRFDLPTEYRSYVQSK 550
551 GRARAPISNYIMLADTDKIKSFEEDLKTYKAIEKILRNKCSKSVDTGETD 600
601 IDPVMDDDDVFPPYVLRPDDGGPRVTINTAIGHINRYCARLPSDPFTHLA 650
651 PKCRTRELPDGTFYSTLYLPINSPLRASIVGPPMSCVRLAERVVALICCE 700
701 KLHKIGELDDHLMPVGKETVKYEEELDLHDEEETSVPGRPGSTKRRQCYP 750
751 KAIPECLRDSYPRPDQPCYLYVIGMVLTTPLPDELNFRRRKLYPPEDTTR 800
801 CFGILTAKPIPQIPHFPVYTRSGEVTISIELKKSGFMLSLQMLELITRLH 850
851 QYIFSHILRLEKPALEFKPTDADSAYCVLPLNVVNDSSTLDIDFKFMEDI 900
901 EKSEARIGIPSTKYTKETPFVFKLEDYQDAVIIPRYRNFDQPHRFYVADV 950
951 YTDLTPLSKFPSPEYETFAEYYKTKYNLDLTNLNQPLLDVDHTSSRLNLL 1000
1001 TPRHLNQKGKALPLSSAEKRKAKWESLQNKQILVPELCAIHPIPASLWRK 1050
1051 AVCLPSILYRLHCLLTAEELRAQTASDAGVGVRSLPADFRYPNLDFGWKK 1100
1101 SIDSKSFISISNSSSAENDNYCKHSTIVPENAAHQGANRTSSLENHDQMS 1150
1151 VNCRTLLSESPGKLHVEVSADLTAINGLSYNQNLANGSYDLANRDFCQGN 1200
1201 QLNYYKQEIPVQPTTSYSIQNLYSYENQPQPSDECTLLSNKYLDGNANKS 1250
1251 TSDGSPVMAVMPGTTDTIQVLKGRMDSEQSPSIGYSSRTLGPNPGLILQA 1300
1301 LTLSNASDGFNLERLEMLGDSFLKHAITTYLFCTYPDAHEGRLSYMRSKK 1350
1351 VSNCNLYRLGKKKGLPSRMVVSIFDPPVNWLPPGYVVNQDKSNTDKWEKD 1400
1401 EMTKDCMLANGKLDEDYEEEDEEEESLMWRAPKEEADYEDDFLEYDQEHI 1450
1451 RFIDNMLMGSGAFVKKISLSPFSTTDSAYEWKMPKKSSLGSMPFSSDFED 1500
1501 FDYSSWDAMCYLDPSKAVEEDDFVVGFWNPSEENCGVDTGKQSISYDLHT 1550
1551 EQCIADKSIADCVEALLGCYLTSCGERAAQLFLCSLGLKVLPVIKRTDRE 1600
1601 KALCPTRENFNSQQKNLSVSCAAASVASSRSSVLKDSEYGCLKIPPRCMF 1650
1651 DHPDADKTLNHLISGFENFEKKINYRFKNKAYLLQAFTHASYHYNTITDC 1700
1701 YQRLEFLGDAILDYLITKHLYEDPRQHSPGVLTDLRSALVNNTIFASLAV 1750
1751 KYDYHKYFKAVSPELFHVIDDFVQFQLEKNEMQGMDSELRRSEEDEEKEE 1800
1801 DIEVPKAMGDIFESLAGAIYMDSGMSLETVWQVYYPMMRPLIEKFSANVP 1850
1851 RSPVRELLEMEPETAKFSPAERTYDGKVRVTVEVVGKGKFKGVGRSYRIA 1900
1901 KSAAARRALRSLKANQPQVPNS 1922
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.) |
Go back to the NucPred Home Page.