 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10059 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MRNSSKGQDPNFSYDSILSTPTPSARRTIGPRAPKSKTTYHKPPSSIESV 50
51 STLIQPNKSQSVTSPYVKQFTFSSKEYNSHNKHALQNSQLPLPKTPEKST 100
101 VHRPKANKVEVTDLPSSSSVEHLHTSKHLKGPRLPKNIIKSSEDVQIAPV 150
151 TPPVHSRSFDPLPKPPVPSVPVSKTKRRTKHKLAPVVEVPEITNEVSPKF 200
201 TSTNDEQVYRLRSIRAGSPNSVCSFQFEIPSTRPPSLDQLIHLFNDFLRH 250
251 PVFDFDENAIEMLQSCTPDEKWCFIRSNFAGFDDPSFQIPELAAVHRPVS 300
301 WFVIQLWNKTISNLQLITLSSLLSTQSDRWISLFLELQGLRALHNLLTYF 350
351 NSSAVVQPQQAEVPRCMLTLLKKKPTLVTSNSYIFQAITVTLISPNLLPR 400
401 KVAADLLTWVLSLKEPLVVSILETGFKEINAEYEKEVPLFFGWIKSFKDI 450
451 ILEKELARTPPSSPARNSASSSPSNIAFLEYCTSTMEFINQLIVACEELE 500
501 QGFDLDILDSLRESGIHEVIQLLRNFPDQQLEKQLNIYESEEERRTISQT 550
551 THEDVDSFMSNESSILSSFNEFASNEVGRLLESTIQNILLAKGTEKQKVK 600
601 LIKVFNSLLQRILLNSKVSNESFEDSLQASLNMLTERFYSDDTARNALKE 650
651 AKASRAMAEKMVIERDAMAAQVNLGAEDLIAKLNKEVEDQKDVILSQKRT 700
701 NETLKTEIDALQKSHVTQIQRSEVELRELYLLINSDSFQGSTNSKERIIE 750
751 YLLDKLDLRKKEIAAESTLWSNDGIDDKLRDLREQMSRQSSQPSTVSTIL 800
801 QIPDKKFHRPFPRHLHRYVGRSASESLTSEKDESIKSMKGIDDFANLEIP 850
851 GKGIESNVVIKDISNQTHEINSVENKAETVSNNSKITNFDIPNDATSLPT 900
901 IITHPTPPPPPPLPVKTSLNTFSHPDSVNIVANDTSVAGVMPAFPPPPPP 950
951 PPPLVSAAGGKFVSPAVSNNISKDDLHKTTGLTRRPTRRLKQMHWEKLNS 1000
1001 GLEFTFWTGPSDEANKILETLHTSGVLDELDESFAMKEAKTLVKKTCART 1050
1051 DYMSSELQKLFGIHFHKLSHKNPNEIIRMILHCDDSMNECVEFLSSDKVL 1100
1101 NQPKLKADLEPYRIDWANGGDLVNSEKDASELSRWDYLYVRLIVDLGGYW 1150
1151 NQRMNALKVKNIIETNYENLVRQTKLIGRAALELRDSKVFKGLLYLILYL 1200
1201 GNYMNDYVRQAKGFAIGSLQRLPLIKNANNTKSLLHILDITIRKHFPQFD 1250
1251 NFSPELSTVTEAAKLNIEAIEQECSELIRGCQNLQIDCDSGALSDPTVFH 1300
1301 PDDKILSVILPWLMEGTKKMDFLKEHLRTMNTTLNNAMRYFGEQPNDPNS 1350
1351 KNLFFKRVDSFIIDYSKARSDNLKSEEEEASQHRRLNLVNNHKEHVLERA 1400
1401 MSENNKMDNEAMDGFLDKLRNVKLESHHKPRNRSAITMGKEHLIEAPNTS 1450
1451 TKSSPAKNELFVPKRSSVKSDLAKVRPRYPKGSESTDGLSDALNITPTKK 1500
1501 GEVSSKAKKGYNYEKRRSGRQVSDSYVLNKNSKNKSNKGRSASYTFSDPS 1550
1551 SLEDSNRQKPFNGEKFRRFSSKSRRGSQNRDSKKTGKARKDKGINNNQTS 1600
1601 PQNKPSKESLKSDTISNEKKVFPQKASKVNLLTPTISNGTRASKHANEKE 1650
1651 NTFPRGVENNLVAPMIPNNTELNEDTSAVSRNLENATNDLKETFPTTTTI 1700
1701 STARAKPGNNDINTILRRNNSRGRRRMLQQMSPLKSNKFSGTNDLNFQQA 1750
1751 TKPDGSNKSSYMERLEKLKQNSERHLQSVGGKKVYSSEETPVNKILVSPS 1800
1801 VSILDHNRILSQSTPIKSPQRAQEMLAGLLSGKLAPKENEK 1841
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.) |
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