 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q29116 from www.uniprot.org...
The NucPred score for your sequence is 0.50 (see score help below)
1 MGVVTRLLVGTFLASLALPAQGGVLKKVIRHKRQTGVNVTLPEESQPVVF 50
51 NHVYNIKLPVGSQCSVDLESASGDKDLAAPSEPSESVQEHTVDGENQIVF 100
101 THRINIPRRACGCAAAPDVKELLSRLEELENLVSSLREQCTSGAGCCLQP 150
151 AEGRLDTRPFCSGRGNFSTEGCGCVCEPGWKGPNCSEPECPSNCHLRGQC 200
201 VDGQCVCNEGFTGEDCSQLACPSDCNDQGKCVNGVCVCFEGYSGVDCSRE 250
251 TCPVPCSEEHGRCVDGRCVCQEGFAGEDCNEPLCLHNCHGRGRCVENECV 300
301 CDEGFTGEDCGELICPKDCFDRGRCINGTCYCDEGFEGEDCGRLACPHGC 350
351 RGRGRCEEGQCVCDEGFAGADCSERRCPSDCHNRGRCLDGRCECDDGFEG 400
401 EDCGELRCPGGCSGHGRCVNGQCVCDEGRTGEDCSQLRCPNDCHGRGRCV 450
451 QGRCECEHGFQGYDCSEMSCPHDCHQHGRCVNGMCVCDDGYTGEDCRELR 500
501 CPGDCSQRGRCVDGRCVCEHGFAGPDCADLACPSDCHGRGRCVNGQCVCH 550
551 EGFTGKDCGQRRCPGDCHGQGRCVDGQCVCHEGFTGLDCGQRSCPNDCSN 600
601 WGQCVSGRCICNEGYSGEDCSQVSPPKDLIVTEVTEETVNLAWDNEMRVT 650
651 EYLIVYTPTHEDGLEMQFRVPGDQTSTTIRELEPGVEYFIRVFAILENKK 700
701 SIPVSARVATYLPTPEGLKFKSIKETSVEVEWDPLDIAFETWEIIFRNMN 750
751 KEDEGEITKSLRRPETTYRQTGLAPGQEYEISLHIVKNNTRGPGLKRVTT 800
801 TRLDAPSQIEAKDVTDTTALITWFKPLAEIDGIELTYGIKDVPGDRTTID 850
851 LTHEENQYSIGNLKPDTEYEVSLISRRADMSSNPAKETFTTGLDAPRNLR 900
901 RISQTDNSITLEWRNGKAAADTYRIKYAPISGGDHAEVEVPRSPQTTTKA 950
951 TLTGLRPGTEYGIGVSAVKGDKESDPATINAATDLDPPKDFRVSELKESS 1000
1001 LTLLWRTPLAKFDRYRLNYGLPSGQPVEVQLPRNATSYILRGLEPGQEYT 1050
1051 ILLTAEKGRHKSKPARVKASTAGEPEIGNLSVSDITPESFSLSWTATEGA 1100
1101 FETFTIEIIDSNRFLETMEYNISGAERTAHISGLRPGNDFIVYLSGLAPG 1150
1151 IQTKPISATATTEAEPEVDNLLVSDATPDGFRLSWTADEGVFDSFVLKIR 1200
1201 DTKKQSEPLEITLLASERTRDITGLREATEYEIELYGISSGKRSQPVSAI 1250
1251 ATTAMGSPKEITFSDITENSATVSWMVPTAQVESFRITYVPITGGAPSVV 1300
1301 TVDGTKTQTRLLRLLPGVEYLVSVIAVKGFEESEPVSGTLTTALDGPSGL 1350
1351 VTANITDSEALAMWQPAIAPVDHYVISYTGDRVPEITRTVSGNTVEYALT 1400
1401 NLEPATEYTLRIFAEKGPQKSSTITTKFTTDLDSPRDLTATEVQSETALL 1450
1451 TWRPPRASVTGYLLVYESVDGTLKEVVVGPETTSYSLSGLSPSTHYTARI 1500
1501 QALNGPLRSKMSQTVFTTIGLLYPFPRDCSQAMLNGDTTSGLYTIYVNND 1550
1551 KAQKLEVFCDMTSDSGGWIVFLRRKNGREDFYRNWKAYAAGFGDLKEEFW 1600
1601 LGLDALSKITAQGQYELRVDLRDHGETAYAVYDRFSVGDARTRYKLKVEG 1650
1651 YSGTAGDSMAYHNGRSFSTFDKDTDSAITNCALSYKGAFWYKNCHRVNLM 1700
1701 GRYGDNSHSQGVNWFHWKGHEYSIQFAEMKLRPSNFRNLEGRRKRA 1746
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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