SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q12150 from www.uniprot.org...

The NucPred score for your sequence is 0.71 (see score help below)

   1  MEAISQLRGVPLTHQKDFSWVFLVDWILTVVVCLTMIFYMGRIYAYLVSF    50
  51  ILEWLLWKRAKIKINVETLRVSLLGGRIHFKNLSVIHKDYTISVLEGSLT   100
 101  WKYWLLNCRKAELIENNKSSSGKKAKLPCKISVECEGLEIFIYNRTVAYD   150
 151  NVINLLSKDERDKFEKYLNEHSFPEPFSDGSSADKLDEDLSESAYTTNSD   200
 201  ASIVNDRDYQETDIGKHPKLLMFLPIELKFSRGSLLLGNKFTPSVMILSY   250
 251  ESGKGIIDVLPPKERLDLYRNKTQMEFKNFEISIKQNIGYDDAIGLKFKI   300
 301  DRGKVSKLWKTFVRVFQIVTKPVVPKKTKKSAGTSDDNFYHKWKGLSLYK   350
 351  ASAGDAKASDLDDVEFDLTNHEYAKFTSILKCPKVTIAYDVDVPGVVPHG   400
 401  AHPTIPDIDGPDVGNNGAPPDFALDVQIHGGSICYGPWAQRQVSHLQRVL   450
 451  SPVVSRTAKPIKKLPPGSRRIYTLFRMNISIMEDTTWRIPTRESSKDPEF   500
 501  LKHYKETNEEYRPFGWMDLRFCKDTYANFNISVCPTVQGFQNNFHVHFLE   550
 551  TEIRSSVNHDILLKSKVFDIDGDIGYPLGWNSKAIWIINMKSEQLEAFLL   600
 601  REHITLVADTLSDFSAGDPTPYELFRPFVYKVNWEMEGYSIYLNVNDHNI   650
 651  VNNPLDFNENCYLSLHGDKLSIDVTVPRESILGTYTDMSYEISTPMFRMM   700
 701  LNTPPWNTLNEFMKHKEVGRAYDFTIKGSYLLYSELDIDNVDTLVIECNS   750
 751  KSTVLHCYGFVMRYLTNVKMNYFGEFFNFVTSEEYTGVLGAREVGDVTTK   800
 801  SSVADLASTVDSGYQNSSLKNESEDKGPMKRSDLKRTTNETDIWFTFSVW   850
 851  DGALILPETIYSFDPCIALHFAELVVDFRSCNYYMDIMAVLNGTSIKRHV   900
 901  SKQINEVFDFIRRNNGADEQEHGLLSDLTIHGHRMYGLPPTEPTYFCQWD   950
 951  INLGDLCIDSDIEFIKGFFNSFYKIGFGYNDLENILLYDTETINDMTSLT  1000
1001  VHVEKIRIGLKDPVMKSQSVISAESILFTLIDFENEKYSQRIDVKIPKLT  1050
1051  ISLNCVMGDGVDTSFLKFETKLRFTNFEQYKDIDKKRSEQRRYITIHDSP  1100
1101  YHRCPFLLPLFYQDSDTYQNLYGAIAPSSSIPTLPLPTLPDTIDYIIEDI  1150
1151  VGEYATLLETTNPFKNIFAETPSTMEPSRASFSEDDNDEEADPSSFKPVA  1200
1201  FTEDRNHERDNYVVDVSYILLDVDPLLFIFAKSLLEQLYSENMVQVLDDI  1250
1251  EIGIVKRLSNLQEGITSISNIDIHIAYLNLIWQETGEEGFELYLDRIDYQ  1300
1301  MSEKSLEKNRTNKLLEVAALAKVKTVRVTVNQKKNPDLSEDRPPALSLGI  1350
1351  EGFEVWSSTEDRQVNSLNLTSSDITIDESQMEWLFEYCSDQGNLIQEVCT  1400
1401  SFNSIQNTRSNSKTELISKLTAASEYYQISHDPYVITKPAFIMRLSKGHV  1450
1451  RENRSWKIITRLRHILTYLPDDWQSNIDEVLKEKKYTSAKDAKNIFMSVF  1500
1501  STWRNWEFSDVARSYIYGKLFTAENEKHKQNLIKKLLKCTMGSFYLTVYG  1550
1551  EGYEVEHNFVVADANLVVDLTPPVTSLPSNREETIEITGRVGSVKGKFSD  1600
1601  RLLKLQDLIPLIAAVGEDDKSDPKKELSKQFKMNTVLLVDKSELQLVMDQ  1650
1651  TKLMSRTVGGRVSLLWENLKDSTSQAGSLVIFSQKSEVWLKHTSVILGEA  1700
1701  QLRDFSVLATTEAWSHKPTILINNQCADLHFRAMSSTEQLVTAITEIRES  1750
1751  LMMIKERIKFKPKSKKKSQFVDQKINTVLSCYFSNVSSEVMPLSPFYIRH  1800
1801  EAKQLDIYFNKFGSNEILLSIWDTDFFMTSHQTKEQYLRFSFGDIEIKGG  1850
1851  ISREGYSLINVDISISMIKLTFSEPRRIVNSFLQDEKLASQGINLLYSLK  1900
1901  PLFFSSNLPKKEKQAPSIMINWTLDTSITYFGVLVPVASTYFVFELHMLL  1950
1951  LSLTNTNNGMLPEETKVTGQFSIENILFLIKERSLPIGLSKLLDFSIKVS  2000
2001  TLQRTVDTEQSFQVESSHFRVCLSPDSLLRLMWGAHKLLDLSHYYSRRHA  2050
2051  PNIWNTKMFTGKSDKSKEMPINFRSIHILSYKFCIGWIFQYGAGSNPGLM  2100
2101  LGYNRLFSAYEKDFGKFTVVDAFFSVANGNTSSTFFSEGNEKDKYNRSFL  2150
2151  PNMQISYWFKRCGELKDWFFRFHGEALDVNFVPSFMDVIESTLQSMRAFQ  2200
2201  ELKKNILDVSESLRAENDNSYASTSVESASSSLAPFLDNIRSVNSNFKYD  2250
2251  GGVFRVYTYEDIETKSEPSFEIKSPVVTINCTYKHDEDKVKPHKFRTLIT  2300
2301  VDPTHNTLYAGCAPLLMEFSESLQKMIKKHSTDEKPNFTKPSSQNVDYKR  2350
2351  LLDQFDVAVKLTSAKQQLSLSCEPKAKVQADVGFESFLFSMATNEFDSEQ  2400
2401  PLEFSLTLEHTKASIKHIFSREVSTSFEVGFMDLTLLFTHPDVISMYGTG  2450
2451  LVSDLSVFFNVKQLQNLYLFLDIWRFSSILHTRPVQRTVNKEIEMSSLTS  2500
2501  TNYADAGTEIPWCFTLIFTNVSGDVDLGPSLGMISLRTQRTWLATDHYNE  2550
2551  KRQLLHAFTDGISLTSEGRLSGLFEVANASWLSEVKWPPEKSKNTHPLVS  2600
2601  TSLNIDDIAVKAAFDYHMFLIGTISNIHFHLHNEKDAKGVLPDLLQVSFS  2650
2651  SDEIILSSTALVVANILDIYNTIVRMRQDNKISYMETLRDSNPGESRQPI  2700
2701  LYKDILRSLKLLRTDLSVNISSSKVQISPISLFDVEVLVIRIDKVSIRSE  2750
2751  THSGKKLKTDLQLQVLDVSAALSTSKEELDEEVGASIAIDDYMHYASKIV  2800
2801  GGTIIDIPKLAVHMTTLQEEKTNNLEYLFACSFSDKISVRWNLGPVDFIK  2850
2851  EMWTTHVKALAVRRSQVANISFGQTEEELEESIKKEEAASKFNYIALEEP  2900
2901  QIEVPQIRDLGDATPPMEWFGVNRKKFPKFTHQTAVIPVQKLVYLAEKQY  2950
2951  VKILDDTH                                            2958

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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