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

NucPred

Fetching Q23658 from www.uniprot.org...

The NucPred score for your sequence is 0.91 (see score help below)

   1  MLGASSSEEEDDDFQEDHDSLPIAQVKKRSLLSGAMTPRSSSPAPSDSVS    50
  51  QTNSLKRNNSSSQGRRKDSVQSQTPARSARIMSNVSKPIMQNSQEFDGDE   100
 101  EEGEEGSTTVVGSGDECGGPALSKEEQERMKAEREEEDHKKNLQMYMFLA   150
 151  RCIAYPFNGQQTGDMARRQMKVNKQELARIRERFTLFLKGETNIAADEAF   200
 201  TKAIQSYFEVFLKSERVQKVVHAGGFSQHDFREVFRLNIEKRVRSLPDIE   250
 251  GLSKDTVLNSWLAKFDAIIKGDETDQNRNARGRSRNPQNAMSADAVLGKE   300
 301  QLYDVFQQILGVKKFEHQIIFNALQLDNPDEQAAAIRREFATREEALKDP   350
 351  IKMKRLTPKFVVKDMETLYMDEVRMSINTLIGNLETVPVTTRGATVGKRK   400
 401  DKSRSRSIEDLSLFNSLKRRTSSGSLNKGDSEDGDVTLTKSDVSLALSME   450
 451  VVVMEVQGLKSVQPSKIVYCTMEVDGHKLQTDHAEASKPKWDTQGDFSTK   500
 501  NPLPVVKVKLYTEVKSMIAFEDKELGKVIIQPTPNCARSPEWYTMTLPKS   550
 551  SQDQNLKIRIAIRVEKPPNLKYCGYCYCIGRNAWKKWKKRFFCLVQVSQY   600
 601  AFAVCSFRQKKADPTEFIQLDGFTIDYMPESDPELSAQGGKHFFTAIKEG   650
 651  DELKFATDDENERHLWVQALYRATGQAYKPVPPKQSTIAPKAQGFQDKAS   700
 701  KHGMDAMIQADSINFDHDHFYSDVQRLTLDFRINEPICSLGWFSPGQAFV   750
 751  LEEYSARYMVRGCFRHVTLLSNLLDKADDGLLIDPALIHYSFAFCASHVH   800
 801  GNRCMPDRQGPEGVGTVTLEEKEKFQEIKERLRVLLEKQITNFRYCFPFG   850
 851  RPEGALKGTLGLLERVLMKDVVSPVPPEEVRAVIRKCLEDAALVNYTRIC   900
 901  NEAKIEQRMGIDVSPAQRIEDMIRVTEFCIDLLKENEEHHGEAFAWFSDL   950
 951  LSDHSEIFWSLYSVDLDSALEVQPHDSWDSFPLFQMLNDFLLSESSLKGG  1000
1001  IFHNKIVQQFQPLVVRYIDLMEHSIAQAIDKGFSKEKWESRKEGCATSED  1050
1051  IYWKLDALHTFVIDLNWPEEDFRKYLQTKMKSLTSDMISKVSDCTFTAFD  1100
1101  SWMQRAKKSTDYMLPSEVCVQINVMFSSKSRAVRVTVDSGEYKYQSKLDE  1150
1151  TLETMLKTMESCIQEKLHGVLESVLSRLARYDEGNPIGAILNIAPKPASI  1200
1201  FNKLKTMAGDTSAQATTTARQPLTAQQSSGQIGNSYVTFFHGCTELLRQV  1250
1251  IIDEIWVNGLFEHWYDNQMKSINEWLTERLQQSLSATQYISLSTIVKKVY  1300
1301  QDFSLQGIDEERLNSKTYQSISRRLQLEESNSHIQEAAINGSAQGSVFPT  1350
1351  SLGNATAAVSNMSSMVEGAGAKMFSLFK                        1378

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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