 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62289 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MANRRVGRGCWEVSPTERRPPAGLRGPAAEEEASSPPVLSLSHFCRSPFL 50
51 CFGDVLLGASRTLSLALDNPNEEVAEVKISHFPAADLGFSVSQRCFVLQP 100
101 KEKIVISVNWTPLKEGRVREIMTFLVNDVLKHQAILLGNAEEQKKKKRSL 150
151 WDTIKKKKISASTSHNRRVSNIQNVNKTFSVSQKVDRVRSPLQACENLAM 200
201 NEGGPPTENNSLTLEENKIPISPISPAFNECHGATCLPLSVRRSTTYSSL 250
251 HASENRELLNVHSANVSKVSFNEKAVTETSFNSVNVNGQSGENSKLSLTP 300
301 NYSSTLNITQSQIHFLSPDSFVNNSHGANNELELVTCLSSDMFMKDNSKP 350
351 VHLESTIAHEIYQKILSPDSFIKDNYGLNQDLESESVNPILSPNQFLKDN 400
401 MAYVCTSQQTCKVPLSNENSQVPQSPEDWRKSEVSPRIPECQGSKSPKAI 450
451 FEEIVEMKSNYYSFIKQNNPKFSAVQDISSHSHNKQPKRRPILSATVTKR 500
501 KATCTRENQTEINKPKAKRCLNSAVGEHEKVINNQKEKDFHSYLPIIDPI 550
551 LSKSKSYKNEVAPSSTTASVARKRKSDGSMEDANVRVAVTEHTEVREIKR 600
601 IHFSPSEPKTSAVKKTKNVITPISKHISNREKLNLKKKTDLSIFRTPISK 650
651 TNKRTKPIIAVAQSNLTFIKPLKTDIPRHPMPFAAKNMFYDERWKEKQEQ 700
701 GFTWWLNFILTPDDFTVKTNISEVNAATLLLGIENQHKISVPRAPTKEEM 750
751 SLRAYTARCRLNRLRRAACRLFTSEKMVKAIKKLEIEIEARRLIVRKDRH 800
801 LWKDVGERQKVLNWLLSYNPLWLRIGLETTYGELISLEDNSDVTGLAMFI 850
851 LNRLLWNPDIAAEYRHPTVPHLYRDGHEEALSKFTLKKLLLLVCFLDYAK 900
901 ISRLIDHDPCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLPV 950
951 NHVQTPFDEFDFAVTNLAVDLQCGVRLVRTMELLTQNWDLSKKLRIPAIS 1000
1001 RLQKMHNVDVVLQVLKSRGIELSDEHGNTILSKDIVDRHREKTLRLLWKI 1050
1051 AFAFQVDISLNLDQLKEEIAFLKHTKSIKKTISLLSCHSDDLINKKKGKR 1100
1101 DSGSFEQYSENIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHHY 1150
1151 HPCYVPFDAICQRTTQTVECTQTGSVVLNSSSESDDSSLDMSLKAFDHEN 1200
1201 TSELYKELLENEKKNFHLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVIT 1250
1251 YLSFLCARLLDLRKEIRAARLIQTTWRKYKLKTDLKRHQEREKAARIIQL 1300
1301 AVINFLAKQRLRKRVNAALVIQKYWRRVLAQRKLLMLKKEKLEKVQNKAA 1350
1351 SLIQGYWRRYSTRRRFLKLKYYSIILQSRIRMIIAVTSYKRYLWATVTIQ 1400
1401 RHWRAYLRRKQDQQRYEMLKSSTLVIQSMFRKWKQRKMQSQVKATVILQR 1450
1451 AFREWHLRKHAKEENSAIIIQSWYRMHKELQKYIYIRSCVVIIQKRFRCF 1500
1501 QAQKLYKRKKESILTIQKYYKAYLKGKIERTNYLQKRAAAIQLQAAFRRL 1550
1551 KAHNLCRQIRAACVIQSYWRMRQDRVRFLNLKKTIIKLQAHVRKHQQRQK 1600
1601 YKKMKKAAVIIQTHFRAYIFARKVLASYQKTRSAVIVLQSAYRGMQARKM 1650
1651 YVHILTSVIKIQSYYRAHVSKKEFLSLKNATIKLQSIVKMKQTRKQYLHL 1700
1701 RAAALFIQQCYRSKKIAAQKREEYMQMRESCIKLQAFVRGYLVRKQMRLQ 1750
1751 RKAVISLQSYFRMRKARQYYLKMYKAIIVIQNYYHAYKAQVNQRKNFLQV 1800
1801 KKAATCLQAAYRGYKVRQLIKQQSIAALKIQSAFRGYNKRVKYQSVLQSI 1850
1851 IKIQRWYRAYKTLHDTRTHFLKTKAALISLQSAYRGWKVRKQIRREHQAV 1900
1901 LKIQSAFRMAKAQKQFRLFKTAALVIQQNFKAWTAGRKQRMEYIELRHAV 1950
1951 LMLQSMWRGKTLRRQLQRQHKCAVIIQSYYRMHVQQKKWKIMKKAALLIQ 2000
2001 KYYRAYSIGREQNHLYLKTKAAVVTLQSAYRGMKVRKRIKDCNKAAVTIQ 2050
2051 SKYRAYKTKKKYATYRASAIIIQRWYRGIKITNHQHKEYLNLKKTAIKIQ 2100
2101 SVYRGIRVRRHIQHMHRAATFIKAVFKMHQSRISYHTMRKAAIVIQVRFR 2150
2151 AYYQGKTQREKYLTILKAVKILQASFRGVRVRRTLRKMQIAATLIQSNYR 2200
2201 RYRKQTYFNKLKKITKTVQQRYRAMKERNIQFQRYNKLRHSVIYIQAIFR 2250
2251 GKKARRHLKMMHIAATLIQRRFRTLMMRRRFLSLKKTAILIQRKYRAHLC 2300
2301 TKHHLQFLQVQNAVIKIQSSYRRWMIRKRMREMHRAATFIQATFRMHRLH 2350
2351 MRYQTLKQASVVIQQQYQANRAAKLQRQHYLRQRHSAVILQAAFRGMKTR 2400
2401 RHLKSMHSSATLIQSRFRSLLVRRRFISLKKATIFVQRKYRATICAKHKL 2450
2451 HQFLHLRKAAITIQSSYRRLMVKKKLQEMQRAAILIQATFRMHRTYTTFQ 2500
2501 TWKHASILIQQHYRTYRAAKLQRENYIRQWHSAVVIQAAYKGMKARHLLR 2550
2551 EKHKASIIIQSTYRMYRQYCFYQKLQWATKIIQEKYRANKKKQKAFQHNE 2600
2601 LKKETCVQAGFQDMNIKKQIQEQHQAAIIIQKHCKAFKIRKHYLHLRATV 2650
2651 VSIQRRYRKLTAVRTQAVICIQSYYRGFKVRKDIQNMHQAATLIQSFYRM 2700
2701 HRAKVDYETKKTAIVVIQNYYRLYVRVKTERKNFLAVQKSVRTIQAAFRG 2750
2751 MEVRQKLKNVSEEKVAAIVNQSALCCYRSKTQYEAVQSEGVMIQEWYKAS 2800
2801 GLACSQEAEYHSQSRAAVTIQKAFCRMATRKLETQKYAALRIQFFLQMAV 2850
2851 YRRRFVQQKRAAITLQHYFRTWQTRKQFLLYRKAAVVLQNHYRAFLSAKH 2900
2901 QRQVYLQIRSSVIIIQARSKGFIQKRKFQEIKNSTIKIQAMWRRYRAKKY 2950
2951 LCKVKAACKIQAWYRCWRAHKEYLAILKAVKIIQGCFYTKLERTRFLNVR 3000
3001 ASAIIIQRKWRAILSAKIAHEHFLMIKRHRAACLIQAHYRGYKGRQVFLR 3050
3051 QKSAALIIQKYIRAREAGKRERIKYIEFKKSTVILQALVRGWLVRKRILE 3100
3101 QRAKIRLLHFTAAAYYHLNALRIQRAYKLYLAMKHANKQVNSVICIQRWF 3150
3151 RARLQEKRFIQKYHSVKKIEHEGQECLSQRNRAASVIQKAVRHFLLRKKQ 3200
3201 EKFTSGIIKIQALWRGYSWRKKNDCTKIKAIRLSLQVVNREIREENKLYK 3250
3251 RTALALHYLLTYKHLSAILEALKHLEVVTRLSPLCCENMAHSGAISKIFV 3300
3301 LIRSCNRSVPCMEVIRYAVQVLLNVSKYEKTTSAVYDVENCIDILLELLQ 3350
3351 IYREKPGNKVADKGGSIFTKTCCLLAILLKTTNRASDVRSRSKVVDRIYS 3400
3401 LYKLTAHKHKMNTERILYKQKKNSSISIPFIPETPVRTRIVSRLKPDWVL 3450
3451 RRDNMEEITNPLQAIQMVMDTLGIPY 3476
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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