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

NucPred

Fetching P35748 from www.uniprot.org...

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

   1  MAQKGQLSDDEKFLFVDKNFINSPVAQADWVAKRLVWVPSEKQGFEAASI    50
51 KEEKGDEVVVELVENGKKVTVGKDDIQKMNPPKFSKVEDMAELTCLNEAS 100
101 VLHNLRERYFSGLIYTYSGLFCVVVNPYKQLPIYSEKIVDMYKGKKRHEM 150
151 PPHIYAIADTAYRSMLQDREDQSILCTGESGAGKTENTKKVIQYLAVVAS 200
201 SHKGKKDTSITGELEKQLLQANPILEAFGNAKTVKNDNSSRFGKFIRINF 250
251 DVTGYIVGANIETYLLEKSRAIRQAREERTFHIFYYLIAGAKEKMRNDLL 300
301 LEGFNNYTFLSNGFVPIPAAQDDEMFQETVEAMSIMGFSEEEQLSVLKVV 350
351 SSVLQLGNIVFKKERNTDQASMPDNTAAQKVCHLMGINVTDFTRSILTPR 400
401 IKVGRDVVQKAQTKEQADFAVEALAKATYERLFRWILSRVNKALDKTHRQ 450
451 GASFLGILDIAGFEIFEVNSFEQLCINYTNEKLQQLFNHTMFILEQEEYQ 500
501 REGIEWNFIDFGLDLQPCIELIERPNNPPGVLALLDEECWFPKATDKSFV 550
551 EKLCTEQGNHPKFQKPKQLKDKTEFSIIHYAGKVDYNASAWLTKNMDPLN 600
601 DNVTSLLNASSDKFVADLWKDVDRIVGLDQMAKMTESSLPSASKTKKGMF 650
651 RTVGQLYKEQLGKLMTTLRNTTPNFVRCIIPNHEKRSGKLDAFLVLEQLR 700
701 CNGVLEGIRICRQGFPNRIVFQEFRQRYEILAANAIPKGFMDGKQACILM 750
751 IKALELDPNLYRIGQSKIFFRTGVLAHLEEERDLKITDVIMAFQAMCRGY 800
801 LARKAFAKRQQQLTAMKVIQRNCAAYLKLRNWQWWRLFTKVKPLLQVTRQ 850
851 EEEMQAKEDELQKIKERQQKAESELQELQQKHTQLSEEKNLLQEQLQAET 900
901 ELYAEAEEMRVRLAAKKQELEEILHEMEARLEEEEDRGQQLQAERKKMAQ 950
951 QMLDLEEQLEEEEAARQKLQLEKVTAEAKIKKLEDDILVMDDQNNKLSKE 1000
1001 RKLLEERISDLTTNLAEEEEKAKNLTKLKNKHESMISELEVRLKKEEKSR 1050
1051 QELEKLKRKMDGEASDLHEQIADLQAQIAELKMQLAKKEEELQAALARLE 1100
1101 DETSQKNNALKKIRELEGHISDLQEDLDSERAARNKAEKQKRDLGEELEA 1150
1151 LKTELEDTLDTTATQQELRAKREQEVTVLKKALDEETRSHEAQVQEMRQK 1200
1201 HTQVVEELTEQLEQFKRAKANLDKTKQTLEKENADLAGELRVLGQAKQEV 1250
1251 EHKKKKLEVQLQELQSKCSDGERARAELNDKVHKLQNEVESVTGMLSEAE 1300
1301 GKAIKLAKEVASLGSQLQDTQELLQEETRQKLNVSTKLRQLEDERNSLQE 1350
1351 QLDEEMEAKQNLERHISTLNIQLSDSKKKLQDFASTVESLEEGKKRFQKE 1400
1401 IESLTQQYEEKAAAYDKLEKTKNRLQQELDDLVVDLDNQRQLVSNLEKKQ 1450
1451 KKFDQLLAEEKNISSKYADERDRAEAEAREKETKALSLARALEEALEAKE 1500
1501 ELERTNKMLKAEMEDLVSSKDDVGKNVHELEKSKRALETQMEEMKTQLEE 1550
1551 LEDELQATEDAKLRLEVNMQALKVQFERDLQARDEQNEEKRRQLQRQLHE 1600
1601 YETELEDERKQRALAAAAKKKLEGDLKDLELQADSAIKGREEAIKQLLKL 1650
1651 QAQMKDFQRELEDARASRDEIFATAKENEKKAKSLEADLMQLQEDLAAAE 1700
1701 RARKQADLEKEELAEELASSLSGRNALQDEKRRLEARIAQLEEELEEEQG 1750
1751 NMEAMSDRVRKATQQAEQLSNELATERSTAQKNESARQQLERQNKELKSK 1800
1801 LQEMEGAVKSKFKSTIAALEAKIAQLEEQVEQEAREKQAAAKALKQRDKK 1850
1851 LKEMLLQVEDERKMAEQYKEQAEKGNAKVKQLKRQLEEAEEESQRINANR 1900
1901 RKLQRELDEATESNEAMGREVNALKSKLRRGNETSFVPTRRSGGRRVIEN 1950
1951 ADGSEEEVDARDADFNGTKSSE 1972

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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