 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62288 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MATRRAGRSWEVSPTERRPSARPRNSAAEEAAASPPVLSLSHFCRSPFLC 50
51 FGDVRLGASRTLPLALDNPNEEVAEVKIAHFPAAEQGFSISPRSFELQPK 100
101 EKIIISVNWTPLKEGRVREIVTFLVNDILKHQAILLGNAEEKKKKKRSLW 150
151 DTINKKKMSTSSSDKRNSYIQNVNTTFCVSQKADRVRSPLQACENLAMKE 200
201 GCFLTENNSLSLEENKIPISPISPIFKECHGETSLPLSVRRSTTYTSLHA 250
251 CENGELLKVEGADGSEDFNFNEKVTSETSFSSIHNMSGQIEENSKLILTP 300
301 TCCSTLNITQSQGNFLSPDSFVNNSHAANNEPEVATCLSLDTFRKDNSSP 350
351 VHLESKTVHKTYRTILSPDSFINDNYGLNQDLEPESINPILSPNQFVKDN 400
401 MAYICISQQTCKLSSLSNKNSQVSQSPQDQRTNGVLPCFRECQGSQSPEA 450
451 IFEESRTLEMKSDCYSFTKNQPKFPVIQNISSYSHDKRTRRPILSATVTK 500
501 SKSICSRENQTEANKPKAKRCLNSVAGEFEKPTDTQNEKSGFQSCLPVID 550
551 PVFSKSKSYKNAVIPSSKTALVARKRKSEGNKEDANVRVTVTEHTEVREI 600
601 KRIHFSPVESKMATVKKTKKMITPISKHISYREKSNLRKKTDSLVYRTPH 650
651 SKTNKRTKPVVAVAQSTLTFIKPLKTDIPRHPMPFAAKNMFYDERWKEKQ 700
701 EQGFTWWLNFILTPDDFTVKTNISEVNAATLLLGVESQHKISVARAPTKD 750
751 EMSLRAYTARCRLNRLRRAACRLFTSENMVKAIKKLEIEIEARRLIVRKD 800
801 RHLWKDVGERQKVLNWLLSYNPLWLRIGLETIYGELISLEDNSDVTGLAV 850
851 FILNRLLWNPDIAAEYRHPSVPHLYRDGHEEALSKFTLKKLLLLVCFLDY 900
901 AKISRLIDHDPCLFCKDAEFKTSKEILLAFSRDFLSGEGDLSRHLSFLGL 950
951 PVNHVQTPFDEFDFAVTNLAVDLQCGVRLVRIMELLTRDWNLSKKLRIPA 1000
1001 ISRLQKMHNVDIVLQILRSRGIQLNDEHGNAILSKDIVDRHREKTLTLLW 1050
1051 KIAFAFQVDISLNLDQLKEEIDFLKHTQSLKKTTSALSCHSDAIINKEKD 1100
1101 KRNSGSFERYSESIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIH 1150
1151 HYHPYYVPFDAICQRTTQTVECTQTGSVVLNSSSESDGSSLDLSLKALDH 1200
1201 ENTSELYKELLENEKRNFQLVRSAVRDLGGIPAMIHHSDMSNTIPDEKVV 1250
1251 ITYLSFLCARLLDLCKETRAARLIQTTWRKYKQKTDLKRHQERDKAARII 1300
1301 QSAVISFLSKQRLKKEINAALAIQKHWRRLLAQRKLLMLKKEKLEKVQNK 1350
1351 SALVIQRYWRRYSTRKQFLKLKYYSVILQSRIRMILAVTSYKRYLWATVT 1400
1401 IQRHWLAYLRRKRDQQRYEMLKSSCLIIQSVFRRWKQHKMRLQIKATIIL 1450
1451 QRAFREWHVRKRAKEEKSAVVIQSWYRMHKELRKYIHLRSCVVIIQTRFR 1500
1501 CLQAQKSYKRRREAILTIQKFYRAHLKGKTERANYLQKRAAAIQLQAAFR 1550
1551 GMKARNLHRQIRAACVFQSYWRMRRDRFRFLNLKKITIKLQAQVRMHQQL 1600
1601 QKYKKIKKAALIIQIHLRASVLAKRALASYQKTRSAVIVLQSAYRGMQAR 1650
1651 RKFIHILTSIIKIQSYYRAYISRKKFLRLKHATVKLQSIVKMKQTRKQYL 1700
1701 HLRAATLFIQQWYRSIKVAALKREEYVQMRESCIKLQAFVRGHLVRKQMR 1750
1751 SQRKAAVSLQSYFRMRKMRQHYLEMYKAAVVIQNYYRAYKAQVSQRKNFL 1800
1801 QVKRAVTCVQAAYRGYKVRQLIKQQSIAALKIQTAFRGYSKRKKYQYVLQ 1850
1851 STIKIQTWYRTYRTVRDVRMQFLKTKAAVISLQSAYRGWKVRTQIRRELQ 1900
1901 AAVRIQSAFRMAQTQKQFRLFKTAALVIQQHLRAWSAGKKQRMEYTELRN 1950
1951 AALMLQSTWKGKIVRRQIRKQHKCAVIIQSYYRMHVQQKKWDIMKKAARL 2000
2001 IQMYYRAYRIGRRQRQLYLKTKAAIVIIQSAYRSMRVRKKIKEYNKAAVA 2050
2051 IQSTYRAYKAKKNYATYRASAVLIQRWYRNIKIANRQRKEYLNLKKTAVK 2100
2101 IQAVFRGIRVRRRIQHMHTAATFIKAMFKMHQAKVRYHKMRTAAVLIQVR 2150
2151 YRAYCQGKIQRAKYLTILKAVTVLQASFRGVRVRQTLRKMQNAAIRIQSC 2200
2201 YRRYRQQTYFNKLKKVTQTVQQRYRAVKERNVQFQRYNKLRHSAICIQAG 2250
2251 FRGMKARRHLRMMHLAATLIQRRFRTLKMRRRFLSLRKTALWVQRKYRAT 2300
2301 VCAKHHLQQFLRLQKAVITLQSSYRGWVVRKKMQEMHRAATVIQAAFRMH 2350
2351 RAHVRYQAVRQASVVIQQRHQANRAAKLQRQRYLRQRHSALILQAAFRSM 2400
2401 KARRHLKMMHSSAVLIQSRFRGLVVRKRFVSLKKAAVFVQRRYRATTCAR 2450
2451 RHLHQFLKVQKAVITIQSSYRRLMAKKKVQAMHRAAALIQATYKMHRTYV 2500
2501 TFQAWKHASILIQQHYRIYRAAKLQRENYVRQRHSALVIQAAYKGMKARQ 2550
2551 LLREKHRAAIIIQSTYRMYRQYLFYRKIQWATKVIQKIYRAKKRKALQHD 2600
2601 ALRKVAACVQADFQDMIIRKQIQEQHQAATVLQKHLKASKVRKHYLHFRA 2650
2651 KVVFVQRRYRALSAVRTQAVICIQSSYRGFKVRKGIQRMHLAATLIQSLY 2700
2701 RMHRAKLDYRAKKTAVVLIQYYYRSYVRVKTERKNFLALQKSVRIIQAAF 2750
2751 RGMKVRQKLKNLSEAKMAAIEKRSAFCRHRTETPYEAVQSSALRIQKWHR 2800
2801 ASLVACSQEAECHSQGRAAVTIQKAFCKTATEILETQKHAALRIQSFLQM 2850
2851 AVYRRRFVQQKRAAVTLQQYFRTWQARKQFLLYRKAASVLQNHHRGFLSA 2900
2901 KPQREAYLHVRSSVIIIQARTRGFIQKRKFQKIKDSTIKIQAAWRSYKAR 2950
2951 KYLCKVKAACKIQAWYRSWKARKEYLAILKAVKVIQGCFYTKLERTRFLN 3000
3001 MRASTIIIQRKWRAMLSGRIAHEHFLMIKRHQAACLIQANFRRYKGRQVF 3050
3051 LRQKSAALTIQRYIRARKAGKCERIKYVELKKSTVVLQALVRGWLVRKRI 3100
3101 SEQRTKIRLLHFTAAAYCHLSALRIQRAYKLHMVMKNAKKQVNSVICVQR 3150
3151 WFRTRLQQKRFAQKCHSVIKSQRELQEHMSQQNRAASVIQKAVRRFLLRK 3200
3201 KKEKINNGITKIQALWRGYSWRKKNDGTKIKAIRLSLQLVNREIREENKL 3250
3251 YKRTALALHCLLTYKHLSAILEALKHLEVVTRLSPLCCENMAQSGAVSKI 3300
3301 FVLIRSCNRSVPCMEVIRYSVQVLLNVAKYEKTTAAVYHVENCIDTLLDL 3350
3351 LQMYREKPGDKVADKGGSIFTKTCCLLAILLKTTNRASDVRSRSKVVDRI 3400
3401 YSLYKLTARKHKMNTERILYTQKKNSSISIPFIPETPIRTRIVSRLKPDW 3450
3451 VLRRDNMEEIT 3461
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.) |
Go back to the NucPred Home Page.