 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62296 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MATRRVGRGCWEVSPTERRPCAGLRGPAAEEEAASPPVLFLSHFCRSPFL 50
51 CFGDVRLGTSRTLPLTLDNPNEEVAEVEISHLPAADLGFSVSQRRFVLQP 100
101 KEKIVISVNWTPFKEGRVRETMTFLVNDVLKHQAILLGNAEEQKKKKRNL 150
151 WDTIKKKKISASTSHNRRVSNIQNVKTFSVSQKVDRVRSPLHACENLAMN 200
201 EGGPPTENNHLTLEENKILISPISPAFSECHGATCLPLSVRRSTAYSSLH 250
251 ALENRELLNVDSANVSKDNFNEKVVSETSFNSINVSDQSGENNKLILTPN 300
301 YSSTLNITQSQRNFLSPDSFVNNSHGENNELELGTCLSSDMFMKDNPKPM 350
351 LLESTTAREMYQKILSPDSFIKDNYGLNQNLESESVXSILSPNQMACMCT 400
401 SQQTYKVPSSNENSQVPQSLQDWRKSKVFPCVPECQGSKSPKATFEELVE 450
451 MKSNCCSFIKQNQPKFPAVKDISSHSHNKQPKRRPILSATVTKRKPTRTR 500
501 ENQTEINKPKAKRCLNSAVGENEKIINNQKEKEDFYSYLPIIDPVLSKSK 550
551 SYKNEITPSLTTASVARKRKSDGSMGDANERVAVTEHTEVQEIKRIHFSP 600
601 SEPKTATVKKTKNVITPISKCVSNREKLSLKKKTEFKTPIFKTSKRTKPI 650
651 IAVAQSNLTFIKSLKTDIPRHPMPFAAKNMFYDERWKEKQEQGFTWWLNF 700
701 ILTPDDFTVKTNISEVNAATLLLGVENQHKISVPRAPTKEEMSLRAYTAR 750
751 CMLNRLRRAACRLFTSEKMVKAIKKLEIEIEARRLVVRKDRHLWKDVGER 800
801 QKVLNWLLSYNPLWLRIGLETVYGELISLEDNSDVTGLAVFILSRLLWNP 850
851 DIAAEYRHPTVPHLYGDGHEEALSKFTLKKLLLLVCFLDYAKISRLIDHD 900
901 PCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLPVNHVQTPFD 950
951 EFDFAVTNLAVDLQCGVRLVRTMELLTQNWDLSKKLRIPAISRLQKMHNV 1000
1001 DIVLQVLKSRGIELSDEHGNTILSKNIVDRHREKTLGLLWKIVFAFQVNI 1050
1051 SLNLDQLKEEIAFLKHTKSIKKTVSLLSCHSDALTNKKKGKRDSGSFEQY 1100
1101 GENIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHHYHPCYVPFD 1150
1151 AISQRTTQTVECTHTGSVVLNSSSESDESSLDMSLKAFDQENTSELYKEL 1200
1201 LENEKKNFHLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVITYLSFLCAR 1250
1251 LLDLRKEIRAARLIQTTWRKYKLKTDLKRHQERDKAARIIQSAVINFLRK 1300
1301 QRLRKTLNAALIIQKYWRRVLAQKKLLMLKKEKLERVQNKAASLIQGYWR 1350
1351 RYSTRKRFLKLKYYSIVLQSRIRMIIAVTCYKRYLWATVTIQRHWRASLR 1400
1401 RKQDQQRYEKLKSSSLIIQAMFRRWKQRKMQLQVKATITLQRAFREWHLR 1450
1451 KRAKEEKSAIVIQSWYRMHKQLRKYVYVRSCVVIIQKRFRCFQAQKLYKR 1500
1501 RKESILTIQKYYRAYLKGKIERTNYLQKRAAAIQLQAAFRRLKAHNLHRQ 1550
1551 IRAACVIQSYWRMRQDRVRFLNLKKIIIKLQAHVRKHQQLRKYKKMKKAA 1600
1601 VIIQTHFQAYIFARKVLASYQKTRSAVIVLQSAYRGMQARKMYIHILTSV 1650
1651 IKIQSYYRAYVSKKEFLSLKNATIKLQSIVRMKQTRKQYLHLRATALFIQ 1700
1701 QCYHSKKLAAQKREEYMQMRESCIKLQAFVRGYLVRKQMRLQRKAVISLQ 1750
1751 SYFRMRKSRQYYLKMYKAVIIIQNYYHSYKAQVNQRKNFLQVKKAATCLQ 1800
1801 AAYRGYKVRQLIKQQSVAAVKIQSAFRGYSKRVKYQSVLQSIIKIQRWYR 1850
1851 AYKTLHGIRTHFLKTKAAVISLQSAYRGWKVRKQIRREHQAAMKIQSAFR 1900
1901 MAKAQKQFRLFKTAALVIQQHLRAWTAGRKQRMEYIELRHSVLMLQSMWK 1950
1951 GKALRRQLQRQHKCAVIIQSYYRMHVQQKKWKIMKKAALLIQKYYRAYSI 2000
2001 GREQHCLYLKTKAAVVTLQSAYRGMKVRKRIKDCNKAAITIQSKYRAYKT 2050
2051 KKKYAAYRASAIIIQRWYRSIKITNHQYKEYLNLKKTAIKIQAVYRGIRV 2100
2101 RRHIQCMHRAATFIKAMFKMHQSRIRYHTMRKATIVIQVRYRAYYQGKMQ 2150
2151 REDYLKFLKAVNVLQANFRGVRVRRTLRKLQIAATVIQSNYRRYRQQTYF 2200
2201 NKLKKITKTVQQRYRAVKERNIQFQRYNKLRHSVIHIQAIFRGMKVRRHL 2250
2251 KTMHIAATLIQRRFRTLMMRRRFLSLKKTAIWIQRKYRAHLCTKHHLQFL 2300
2301 RLQNAAIKIQSSYRRWVVRKKMREMHRAATFIQATFRMHRVHMRYQALKQ 2350
2351 ASVVIQQQYQANRAAKLQRQHYLRQRHSAVILQAAFRGMETRRRLKSMHS 2400
2401 SAILIQSRFRSLLVRRRFISLKKAAIFIQRKYRATICAKHNLHQFLQLRK 2450
2451 AAVTIQSSYRRLMVKKKLQEMHRAAVLIQATFRMHKTYITFQTWKHASIL 2500
2501 IQQHYRTYRASKLQRENYTKQWHSALIIQAAYRGMKARQLLREKRKAAII 2550
2551 IQSTYRMYRQYCLYQNLQWATKIIQEKYRANKKKHKALQHNELKKAEACV 2600
2601 QASFQDMNIKKLIQEQHQTSLTIQKHCNAFKIKKQYLHLRAPVVSIQRRY 2650
2651 RKLTAVHTQAVICIQSYYRGFKVRRDIQNMHLAATRIQSLYRMHRAKVDY 2700
2701 QTKKTAIVVIQNYYRLYVRVKTERNSFLAVQKSVRTIQAAFRSMKVRQKL 2750
2751 KNLSQEKMAAIVSPSAVYCYRIEAQSEAVGSEGVIIQEWYKTSCLAHSQE 2800
2801 AEYHSQSRAAVTIQKAFRRMITRKLETQKCAALRIQFFLQMAVYRRRFVQ 2850
2851 QKRAAVTLQHYFRTWQTRKQFLLYRKAAVVLQNHYRAFLSAKHQRQVYLQ 2900
2901 IRSSVIIIQARTKGFIQKRKFQKIKNSTIKIQAVWRRYRDKKSLCKVKAA 2950
2951 CKIQAWYRCWRAHKEYLAILKAVKIIQGSFYXKLERTRFLNMRASAIIIQ 3000
3001 RKWRAILSAKIAHEHFLMIQRHQAACLIQAHFRGYKGRQVFLRQKSAALN 3050
3051 IQKYIRAREAGRRERIKYIELKKSTVTLQALVRGWLVRKRILEQRAKIRL 3100
3101 LHFTAAAYYHLKALRIQRAYKLYLALKNANKQVNSAICIQRWFRARLQQK 3150
3151 RFIQICHSIKKIEHEGQERLSQQNRAASVIQKAVRHFLLRKKQEKFTSGI 3200
3201 IKFQALWRGYSWRKNNDCTKIKAIRLSLQVVNREIREENKLYRRAALALH 3250
3251 YLLTYKHLSAILEAVKHLEVVTRLSPFCCENMAQSGAISKIFVLIRSCNR 3300
3301 SVPCMEVIRYAVQVLLNVAKYEKTTSAVYDVENCVDTLLELLQMYREKPG 3350
3351 NKVADKSGSIFTKTCCLLATLLKTTNRASDVRSRSKVVDRIYSLYKLTAY 3400
3401 KHKVNTERLHYKQKKDSSTSIPFIPETPVRTRIVSRLKPDWVLRRDNLEE 3450
3451 ITNPLQAIQMVMDTLGIPY 3469
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.