 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02455 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MSDHDTPMESIQNGENSDERLNAIASFFGCSLEQVKSFDGDVVKHLNDKL 50
51 LQFNELKSENLKVTVSFDELKASSLKKIDGLKTEMENVIRENDKIRKERN 100
101 DTFVKFESVENEKMKLSSELEFVKRKLDDLTEEKKETQSNQQRTLKILDE 150
151 RLKEIELVRVENNRSNSECKKLRSTIMDLETKQQGYITNDLNSRTELERK 200
201 TQELTLLQSNNDWLEKELRSKNEQYLSYRQKTDKVILDIRNELNRLRNDF 250
251 QMERTNNDVLKQKNNELSKSLQEKLLEIKGLSDSLNSEKQEFSAEMSLKQ 300
301 RLVDLLESQLNAVKEELNSIRELNTAKVIADDSKKQTPENEDLLKELQLT 350
351 KEKLAQCEKECLRLSSITDEADEDNENLSAKSSSDFIFLKKQLIKERRTK 400
401 EHLQNQIETFIVELEHKVPIINSFKERTDMLENELNNAALLLEHTSNEKN 450
451 AKVKELNAKNQKLVECENDLQTLTKQRLDLCRQIQYLLITNSVSNDSKGP 500
501 LRKEEIQFIQNIMQEDDSTITESDSQKVVTERLVEFKNIIQLQEKNAELL 550
551 KVVRNLADKLESKEKKSKQSLQKIESETVNEAKEAIITLKSEKMDLESRI 600
601 EELQKELEELKTSVPNEDASYSNVTIKQLTETKRDLESQVQDLQTRISQI 650
651 TRESTENMSLLNKEIQDLYDSKSDISIKLGKEKSSRILAEERFKLLSNTL 700
701 DLTKAENDQLRKRFDYLQNTILKQDSKTHETLNEYVSCKSKLSIVETELL 750
751 NLKEEQKLRVHLEKNLKQELNKLSPEKDSLRIMVTQLQTLQKEREDLLEE 800
801 TRKSCQKKIDELEDALSELKKETSQKDHHIKQLEEDNNSNIEWYQNKIEA 850
851 LKKDYESVITSVDSKQTDIEKLQYKVKSLEKEIEEDKIRLHTYNVMDETI 900
901 NDDSLRKELEKSKINLTDAYSQIKEYKDLYETTSQSLQQTNSKLDESFKD 950
951 FTNQIKNLTDEKTSLEDKISLLKEQMFNLNNELDLQKKGMEKEKADFKKR 1000
1001 ISILQNNNKEVEAVKSEYESKLSKIQNDLDQQTIYANTAQNNYEQELQKH 1050
1051 ADVSKTISELREQLHTYKGQVKTLNLSRDQLENALKENEKSWSSQKESLL 1100
1101 EQLDLSNSRIEDLSSQNKLLYDQIQIYTAADKEVNNSTNGPGLNNILITL 1150
1151 RRERDILDTKVTVAERDAKMLRQKISLMDVELQDARTKLDNSRVEKENHS 1200
1201 SIIQQHDDIMEKLNQLNLLRESNITLRNELENNNNKKKELQSELDKLKQN 1250
1251 VAPIESELTALKYSMQEKEQELKLAKEEVHRWKKRSQDILEKHEQLSSSD 1300
1301 YEKLESEIENLKEELENKERQGAEAEEKFNRLRRQAQERLKTSKLSQDSL 1350
1351 TEQVNSLRDAKNVLENSLSEANARIEELQNAKVAQGNNQLEAIRKLQEDA 1400
1401 EKASRELQAKLEESTTSYESTINGLNEEITTLKEEIEKQRQIQQQLQATS 1450
1451 ANEQNDLSNIVESMKKSFEEDKIKFIKEKTQEVNEKILEAQERLNQPSNI 1500
1501 NMEEIKKKWESEHEQEVSQKIREAEEALKKRIRLPTEEKINKIIERKKEE 1550
1551 LEKEFEEKVEERIKSMEQSGEIDVVLRKQLEAKVQEKQKELENEYNKKLQ 1600
1601 EELKDVPHSSHISDDERDKLRAEIESRLREEFNNELQAIKKKSFDEGKQQ 1650
1651 AMMKTTLLERKLAKMESQLSETKQSAESPPKSVNNVQNPLLGLPRKIEEN 1700
1701 SNSPFNPLLSGEKLLKLNSKSSSGGFNPFTSPSPNKHLQNDNDKRESLAN 1750
1751 KTDPPTHLEPSFNIPASRGLISSSSTLSTDTNDEELTSNNPAQKDSSNRN 1800
1801 VQSEEDTEKKKEGEPVKRGEAIEEQTKSNKRPIDEVGELKNDEDDTTENI 1850
1851 NESKKIKTEDEEEKETDKVNDENSI 1875
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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