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

NucPred

Fetching P35187 from www.uniprot.org...

The NucPred score for your sequence is 1.00 (see score help below)

   1  MVTKPSHNLRREHKWLKETATLQEDKDFVFQAIQKHIANKRPKTNSPPTT    50
51 PSKDECGPGTTNFITSIPASGPTNTATKQHEVMQTLSNDTEWLSYTATSN 100
101 QYADVPMVDIPASTSVVSNPRTPNGSKTHNFNTFRPHMASSLVENDSSRN 150
151 LGSRNNNKSVIDNSSIGKQLENDIKLEVIRLQGSLIMALKEQSKLLLQKC 200
201 SIIESTSLSEDAKRLQLSRDIRPQLSNMSIRIDSLEKEIIKAKKDGMSKD 250
251 QSKGRSQVSSQDDNIISSILPSPLEYNTSSRNSNLTSTTATTVTKALAIT 300
301 GAKQNITNNTGKNSNNDSNNDDLIQVLDDEDDIDCDPPVILKEGAPHSPA 350
351 FPHLHMTSEEQDELTRRRNMRSREPVNYRIPDRDDPFDYVMGKSLRDDYP 400
401 DVEREEDELTMEAEDDAHSSYMTTRDEEKEENELLNQSDFDFVVNDDLDP 450
451 TQDTDYHDNMDVSANIQESSQEGDTRSTITLSQNKNVQVILSSPTAQSVP 500
501 SNGQNQIGVEHIDLLEDDLEKDAILDDSMSFSFGRQHMPMSHSDLELIDS 550
551 EKENEDFEEDNNNNGIEYLSDSDLERFDEERENRTQVADIQELDNDLKII 600
601 TERKLTGDNEHPPPSWSPKIKREKSSVSQKDEEDDFDDDFSLSDIVSKSN 650
651 LSSKTNGPTYPWSDEVLYRLHEVFKLPGFRPNQLEAVNATLQGKDVFVLM 700
701 PTGGGKSLCYQLPAVVKSGKTHGTTIVISPLISLMQDQVEHLLNKNIKAS 750
751 MFSSRGTAEQRRQTFNLFINGLLDLVYISPEMISASEQCKRAISRLYADG 800
801 KLARIVVDEAHCVSNWGHDFRPDYKELKFFKREYPDIPMIALTATASEQV 850
851 RMDIIHNLELKEPVFLKQSFNRTNLYYEVNKKTKNTIFEICDAVKSRFKN 900
901 QTGIIYCHSKKSCEQTSAQMQRNGIKCAYYHAGMEPDERLSVQKAWQADE 950
951 IQVICATVAFGMGIDKPDVRFVYHFTVPRTLEGYYQETGRAGRDGNYSYC 1000
1001 ITYFSFRDIRTMQTMIQKDKNLDRENKEKHLNKLQQVMAYCDNVTDCRRK 1050
1051 LVLSYFNEDFDSKLCHKNCDNCRNSANVINEERDVTEPAKKIVKLVESIQ 1100
1101 NERVTIIYCQDVFKGSRSSKIVQANHDTLEEHGIGKSMQKSEIERIFFHL 1150
1151 ITIRVLQEYSIMNNSGFASSYVKVGPNAKKLLTGKMEIKMQFTISAPNSR 1200
1201 PSTSSSFQANEDNIPVIAQKSTTIGGNVAANPPRFISAKEHLRSYTYGGS 1250
1251 TMGSSHPITLKNTSDLRSTQELNNLRMTYERLRELSLNLGNRMVPPVGNF 1300
1301 MPDSILKKMAAILPMNDSAFATLGTVEDKYRRRFKYFKATIADLSKKRSS 1350
1351 EDHEKYDTILNDEFVNRAAASSNGIAQSTGTKSKFFGANLNEAKENEQII 1400
1401 NQIRQSQLPKNTTSSKSGTRSISKSSKKSANGRRGFRNYRGHYRGRK 1447

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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