 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62283 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MATRRVGRGCWEVSPTERRPCAGLRGPAAEEEAASPPVLLLSHFCRSPFL 50
51 CFGDVRLGASRTLPLTLDNPNEEVAEVKISHLPAADLGFSVSQRCFVLQP 100
101 KEKIVISVNWTPFKEGRVREIMTFLVNDVLKHQAILLGNAEEQKKKKRNL 150
151 WDTIKKKKISASTSHKRRVSNIQNVNKTFSVSQKVDRVRSPLQACENLAM 200
201 NEGGPPTENNYLTLEENKILISPISPTFNECHGATCLPLSVRRSTAYSSL 250
251 HASENRELLNVDSASVSKDDFNEKVVSETSFNSINVNDQSGENNKLILTP 300
301 NCSTLNITQSQRSFLSPDSFVNNSHGDNNELELGTCLSSDMFMKDNSKPV 350
351 HLESTTAHEIYQKILSPDSFIKDNYGLNQDLESESINSILSPNQFLKDSM 400
401 ACMHTSQQTCKVTLSNENSQVPQSLQDWRKSEVFPCIPECQGSKSPKATF 450
451 EELVEMKSNCCSFIKQNQPKFPAVKDISSHSHNKQPKRRPILSATVTKRK 500
501 PTCPRENQTEINKPKAKRCLNSAVGENEKIINNQKEKEDFHSYLPIIDPV 550
551 LSKSKSYKNEITPSLTTASVARKRKSDGSMGDANVRVAVTEHTEVREIKR 600
601 IHFSPFECKTSTVKKTKNVVTPILKRISNREKLSLKKKTEFKTPIFKTSK 650
651 RTKPIIAVAQSNLTFIKPLKTGIPRHPMPFAAKNMFYDERWKEKQEQGFT 700
701 WWLNFILTPDDFTVKTNISEVNAATLLLGLENQHKISVPRAPTKEEMSLR 750
751 AYTARCRLNRLRRAACRSFTSEKMVKAIKKLEIEIEARRLIVRKDRHLWK 800
801 DVGERQKVLNWLLSYNPLWLRIGLETIYGELISLEDNSDVTGLAVFILNR 850
851 LLWNPDIAAEYRHPTVPHLYRDGHEEALSKFTLKKLLLLVCFLDYAKISR 900
901 LIDHDPCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLPVNHV 950
951 QTPFDEFDFAVANLAVDLQCGVRLVRTMELLTQNWDLSKKLRIPAISRLQ 1000
1001 KMHNVDIVLQVLKSRGIELSDEHGNTILSKDIVDRHREKTLGLLWKIAFA 1050
1051 FQVNISLNLDQLKEEIAFLKHTKSIKKTVSLSSCQSDALTNKKKGKRDSG 1100
1101 SFEQYGENIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHHYHPC 1150
1151 YVPFNAICQRTTQTVECTQTGSVVLNSSSESDESSLDMSLKAFDQENTSE 1200
1201 LYKELLENEKKNFHLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVITYLS 1250
1251 FLCARLLDLRKEIRAARLIQTTWRKYKLKTDLKRHQERDKAARIIQSAVI 1300
1301 NFLTKQRLRKKLNAALVIQKYWRRVLAQKKLLMLKKEKLERVQNKAASLI 1350
1351 QGYWRRYSTRKRFLKLKYYSVILQSRIRMIIAVTCYKRYLWAAVTIQRHW 1400
1401 RAYLRRKQDQQRYEMLKSSSLIIQAMFRRWKRRKMQLQVKATITLQRAFR 1450
1451 EWHLRKRAKEEKSAIVIQSWYRMHKQLQKYVYVRSCVVIIQKRFRCFQAQ 1500
1501 KLYKRRKESILTIQKYYRAYMKGKIERTNYLQKRAAAIQLQAAFRRLKAH 1550
1551 NLHRKIRAACVIQSYWRMRQDRVRFLNLKKIIIKLQAHVRKHQQLQKYKK 1600
1601 MKKAAVTIQTHFQAYIFARKVLASYQKTRSAVIVLQSAYRGMQARKMYIH 1650
1651 ILTSVIKIQSYYRAYVSKKEFLSLKNATVKLQSIVKMKQTRKQYLHLRAT 1700
1701 VLFIQRCYRSKKLAAQKREEYMQMRESCIKLQAFVRGYLVRKQMRLQRKA 1750
1751 VISLQSYFRMRKARQYYLKMYKAVIIIQNYYHSYKAQVNQRKNFLQVKKA 1800
1801 ATCLQAAYRGYKVRQLIKQQSIAAVKIQSAFRGYRKRVKYQSVLQSIIKI 1850
1851 QRWYRAYKTLSDIRTHFLKTKAAVISLQSAYRGWKVRKQIRREHQAAMKI 1900
1901 QSAFRMAKAQKQFRLFKTAALVIQQHLRAWTAGRKQRMEYIELRHSVLML 1950
1951 QSMWKGKTLRRQLQRQHKCAVIIQSYDRMHVQQKKWKIMKKAVLLIQKYY 2000
2001 RAYSIGREQHHLYLKTKAAVVTLQSAYRGMKVRKRIKDCNKAAITIQSKY 2050
2051 RAYKTKKKYAACRASAIIIQRWYRGIKITNHQYKEYLNLKKTAIKIQAVY 2100
2101 RGIRVRRHIQHMHRAATFIKAMFKMHQSRIRYHTMRKATIVIQVRYRAYY 2150
2151 QGKMQRENYLKILKAVNVLQANFRGVRVRRTLRKLQIAATLIQSNYRRYR 2200
2201 QQTYFNKLKKITKTVQQRYRAVKERNIQFQRYNKLRHSVIHIQAIFRGMK 2250
2251 ARRHLKTMHIAATLIQRRFRTLMMRRRFLSLKKTAIWVQRKYRAHLCTKH 2300
2301 HLQFLRLQNAAIKIQSSYRRWMIRKKIREMHRAATFIQATFRMHRVHMRY 2350
2351 QALKQASVVIQQQYQANRAAKLQRQHYLRQRHSAVILQAAFRGMKTRRHL 2400
2401 KSMHSSAILIQSRFRSLLVRRRFISLKKAAIFIQRKYRATICAKHKLHQF 2450
2451 LQLRKAAITIQSSYRRLMVKKKLQEMHRAAVLIQATFRMHRTYITFQTWK 2500
2501 HASILIQQHYRTYRASKLQRENYTKQWHSAVIIQAAYRGMKARQLLREKH 2550
2551 KAAIIIQSTYRMYRQCCLYQKLQWATKIIQEKYRANKKKHKALQHNELKK 2600
2601 AETCLQASFQDMNIKLIQEQDQTSIIIQKHCKAFKIKKHYLHLRAPVVSI 2650
2651 QRRYRKLTAVRTQAVICIQSYYRGFKVRRDIQNMHLAATRIQSFYRMHRA 2700
2701 TVDYQTKKTAVVLIQNYYRLYVRVKTERKSFLAVQKSVRTIQAAFRGMKV 2750
2751 RQKLKNVSQEKMAAIVNQSALCCYRSKAQYEAVRSEGVIIQEWYKASCLA 2800
2801 CSQEAEYHSQSRAAVTIQKAFRRMITRKVETQKCAALRIQFFLQMAVYRR 2850
2851 RFVQQKRAAVTLQHYFRMWQTRKQFLLYRKAAVVLQNHYRAFLSAKHQRQ 2900
2901 VYLRIRSSVIIIQARIKGFIQKRKFQKIRNSTIKIQAMWRRYRAKKSLCK 2950
2951 VKAACKIQAWYRCWRAHKEYLAILKAVKIIQGCFYTKLERTRFLNLRASA 3000
3001 IIIQRKWRAILSAKIAHEHFLMIQRHRAACLIQAHFRGYKGRQVFLRQKS 3050
3051 AALNIQKYIRAREAGRRERIKYIELKKSTVILQALVRGWLVRKRILEQRA 3100
3101 KIRLLHFTAAAYYHLKALRIQRAYKLYLALKKANKQINSVICIQRWFRAR 3150
3151 LQQKRFIQKCHSIKKIEHEGQERLSQQNRAASVIQKAVRHFLLRRKQEKF 3200
3201 TSGIIKFQALWRGYSWRKNNDCTKIKAIRLSLQVVNREIREENKLYRRTA 3250
3251 LALHYLLTYKHLSAILEAVKHLEVVTRLSPLCCENMAQSGAISKIFVLIR 3300
3301 SCNRSVPCMEVIRYAVQVLLNVAKYEKTTSAVYDVENCVDTLLELLQMYR 3350
3351 EKPGNKVADKSGSIFTKTCCLLATLLKTTNRASDVRSRSKVVDRIYSLYK 3400
3401 LTACKHKVNTERLLYKQKKDSSISIPFIPETPVRTRIVSRLKPDWVLRRD 3450
3451 NLEEIINPLQAIQMLMDTLGIPY 3473
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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