 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O59722 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MSEVETPTAASPTFPVETSHRLDTLHTSSTEQIIKDSENVVHTTLKLPTS 50
51 TLSREFWMKDERTNNCSLCETEFTLFRRKHHCRICGKIICKYCLKEAPGF 100
101 IFRLQGSIKVCRPCASILVNNYSRSQLFNHSLNESKNRDLTEQHPFVTLD 150
151 ELNSNDQVLSSFGDLSSTFEMPNNIHPPEVAPMIAIPSSRSNYDSPGWAH 200
201 HSIFLDWSKRNLDSNVINVEDSESGKYNALTITNSYDAGPSSVSTDYRPV 250
251 NFGKVPSYSKLRKNKAFSSAKVSDMYLSADERNRLEDFSKGDRGLSFVNL 300
301 SPNIKATSYDRLSTVINEPFISRSSSLTDERGLADSGNSYHHFSDSDDES 350
351 LFNDGLGLSFHANSAIIKQRQQNVASIQRYGNESYLSNFLKAFLPKTVCD 400
401 YLFPSSTIPDGLPALIENFNARVDKVNHPGGTEEPLPYQGKSRASSVVTS 450
451 SKSTCILPPWILFSDSFNQLVCTFLGKLLFQMLNDEGVDSPMQWVLCLPK 500
501 ILLKMALDLGPDIRSGDDIDVRSYVKIKKIPGGSIQDCFLVNGVLFSKKA 550
551 SSKSMDRSLRRPRIALLTFSLDYACDEQRILSLDLIISQQEEYIINLVNR 600
601 ICMLKPNLVFAQGQIPSIALKYFEEHGVIAFHGLKESVLYDIARCCRADI 650
651 ISSIDKLSLCPRLGTCGRFQLRTYVVDENKGLRKTFAILDRCSERLGCTI 700
701 VLRGADYNQLSKVKKIVELVVLIAYHIKLECALLRDKFVNMPELFETTYQ 750
751 SLSRKSLPSFASTAADKEKSQNHEKKSLNSDNQSLRPLENENQSVSSTQG 800
801 SNSPLELINNLPASDDYSSITKALKTRFLTFSPFLSKPLPRLLNQVNYYQ 850
851 FIRNKLLKDVKLHPYSPTGSFVMKQSENDNVEESYEESYKFFCIDERYHF 900
901 LEKQWTLYYSHSKLMFSPFSSQRIILLYSIINKETSVPCIGPERCLLEFY 950
951 RETDCTLGQYIEDSCLNTNVSCGGEYCKTNDMLWHYRSYVHGNSRISVFL 1000
1001 ESFSCPVPGLEEKIIMWSYCKFCKKNTHITVMSEETWKYSFGKYLEFMFY 1050
1051 NSQIRDRFEFCDHSVMAQHVHYFGYCNMALRFQRDLIEIFELFVPSVTLR 1100
1101 NNPSYIKELKEKEYKRLKGVIEKCLSSVASRINQIKCDWVTDPEKFESCT 1150
1151 SEISKFRTLLSSDYTELYSEFDSIYLNSSTSDYLSLNSILRVLQGKMVKW 1200
1201 EQRFLDYQRLYLPSYKELSKIAAAQIKKVFLERPLSQTPLDLPETLENTQ 1250
1251 IDIYPSFKTESTDDQLEKVTQTNVASNKRVAPYADSMANVGSPESDCFSV 1300
1301 ATSSDIPKANIDFTNDISTQNTFPASPVSNSGFSRQTYPNISQRQGVNML 1350
1351 SHKRKSASTSDRRFVNASSTSGMNMPISSSISAKISSIQNSTKYSPRKPI 1400
1401 PAKDVRVSSLVRRFEELSLQLQEKQKRDEELIKARRKRALPVVPSKPVVE 1450
1451 VFNDLNEAFDDENSEDENGINDTKENRATESNFSGVDSMSKERENVSSNE 1500
1501 DNSPEAFEDIFGILFKNESGLEEQQNLEPSSQMDKEGSKLPTSGPLADKT 1550
1551 SVYRILSAFWNEWNSLNPPPFEFPLQPTEHMFSDSNVIIREDEPSSLISF 1600
1601 TLSSPDYLSKMVEIEDSMDEALTNQGLQGSTQFKIENLMLKPTGTHLKYQ 1650
1651 FEEGSARLSCKVFFAEQFSALRRACGCEETFVTSLARCSLWESSGGKSGS 1700
1701 AFLKTFDKKYILKVLSRLESDSLLNFAPAYFDYISKVFFHELPTALTKIF 1750
1751 GFYRVDIRNPTTGTICKTDIMIMENVFYDECPSRIFDLKGSMRNRHVEST 1800
1801 GKVDEVLLDENLVELIYESPIFVSEQLKSLLHSCLWNDTLFLSKLNIMDY 1850
1851 SLIVGIDYTKKELYVGIIDFIRTYTWDKKLESWVKEKGLVGRGPEPTIVT 1900
1901 PKQYKNRFRKAMDCYILASQDFETGEGFKFCE 1932
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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