 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62290 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MANRRVGRGCWEVSPTERRPPAGLRGPAAEEEASSPPVLSLSHFCRSPFL 50
51 CFGDVLLGASRTLSLALDNPNEEVAEVKISRFPAADLGFSVSQRCFVLQP 100
101 KEKIVISVNWTPFKEGRVREIMTFLVNDVLKHQAILLGNAEEQKKKKRSL 150
151 WDTIKKKKISASTSHNRRVSNIQNVNKTFSVSQKVDRVRSPLQACENLAM 200
201 NEGGPPTENNSLTLEENKIPISPISPAFNECHGATCLPLSVRRSTTYSSL 250
251 HASENRELLNVDSANVSKVSFNEKAVTETSFNSINVNGQSGENSKLSLTP 300
301 NYSSTLNITQSQIHFLSPDSFVNNSHGANNGLELVTCLSSDMFMKDNSKP 350
351 VHLESTIAHEINQKILSPDSFIKDNYGLNQDLESESVNPILSPNQFLKDN 400
401 MAYMCTSQQTCKVPLSNENSQVPQSPQDWRKSEVSPRIPECQGSKSPKAI 450
451 FEELVEMKSNYYSFINQNNPKFSAVQDISSHSHNKQPKRRPILSATVTKR 500
501 KATCTRENQTEINKPKAKRCLNSAVGEHEKVINNQKEKEDFHSYLPIIDP 550
551 ILSKSKSYKNKITPSSTTASVARKRKSDGSMEDANVRVAVTEHTEVREIK 600
601 RIHFSPSEPKTSAVKKTKNVITPISKRISNREKLNLKKKTDLLMFKTPIS 650
651 KTNKRTKPIIAVAQSSLTFIKPLKTDIPRHPLPFAAKNMFYDERWKEKQE 700
701 QGFTWWLNFILTPDDFTVKTNTSEVNAATLLLGIENQHKISVPRAPTKEE 750
751 MSLRAYTARCRLNRLRRAACRLFTSEKMVKAIKKLEIEIEARRLIVRKDR 800
801 HLWKDVGERQKVLNWLLSYNPLWLRIGLETTYGELISLEDNSDVTGLAMF 850
851 ILNRLLWNPDIAAEYRHPTVPHLYRDGHEEALSKFTLKKLLLLVCFLDYA 900
901 KISRLIDHDPCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLP 950
951 VNHVQTPFDEFDFAVTNLAVDLQCGVRLVRTVELLTQNWDLSKKLRIPAI 1000
1001 SRLQKMHNVDIVLQVLKSRGIELSDEHGNTILSKDIVDRHREKTLRLLWK 1050
1051 IAFAFQVDIXLNLDQLKEEIAFLKHTKGIKKTISLLSCHYDDLINKKKGK 1100
1101 RDSDSFEQYSENIKLLMDWVNAVCXFYNKKVENFTVSFSDGRVLCYLIHH 1150
1151 YHPCYVPFDAICQRTTQTVECTQTGSVVLNSSSESDDSSLDMSLKAFDHE 1200
1201 NTSELYKELLENEKKNFQLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVI 1250
1251 TYLSFLCARLLDLRKEIRAARLIQTTWRKYKLKTDLKRHQETDKAARIIQ 1300
1301 SAVINFLAKQRLRKRVNAALVIQKYWRRVLAQRKLLMLKKEKLEKVQNKA 1350
1351 ASLIQGYWRRYSTRKRFLKLKYYSIILQSRIRMIIAVTSYKRYLWATVTI 1400
1401 QRHWRAYLRRKQDQQRYEMLKSSTLIIQSMFRKWKQRKMQSQVKATIILQ 1450
1451 RAFREWHLRKQAKEENSAIVIQSWYRMHKELRKYIYIRSCXVIIQKRFRC 1500
1501 FQAQKLYKRKKESILTIQKYYKAYLKGKIERTNYLQKRAAAIRLQAAFRR 1550
1551 LKAHNLCRQIRAACVIQSYWRMRQDRVRFLNLKKTIIKLQAHVRKHQQLQ 1600
1601 KYKKMKKAAVIIQTHFRAXIFARKVLASYQKTRSAVIVLQSAYRGMQARK 1650
1651 MYIHILTSVIKIQSYYRAYVSKKEFLSLKNATIKLQSIVKMKQTRXQYLH 1700
1701 LRAAALFIQQCYRSKKIAAQKREEYMQMRESCIKLQAFVRGYLVRKQMRL 1750
1751 QRKAVISLQSYFRMRKARQYYLKMYKAIIVIQNYYHAYKAQVNQRKNFLQ 1800
1801 VKKAATCLQAAYRGYKVRQLIKQQSIAALKIQSAFRGYNKRVKYQSMLQS 1850
1851 IIKIQRWYRAYKTLHDTRTHFLKTKAAVISLQSAYRGWKVRKQIRREHQA 1900
1901 ALTIQSAFRMAKAQXQFRLFKTAALVIQQNFRAWTAGRKQRMEYIELRHA 1950
1951 VLMLQSMWKGKTLRRQLQRQHKCAIIIQSYYRMHVQQKKWKTMKKAALLI 2000
2001 QKYYRAYSIGREQNHLYLKTKVAVVTLQSAYRGMKVRKRIKDCNKAAVTI 2050
2051 QSKYRAYKTKKKYATYRASAIIIQRWYRGIKITNHQHKKYLNLKKTAIKI 2100
2101 QSVYRGIRVRRHIQHMHRAATFIKAMFKMHQSRISYHTMRKAAIVIQVRF 2150
2151 RAYYQGKMQREKYLTILKAVKILQASFRGVRVRWTLRKMQIAATLIQSNY 2200
2201 RRYRQQTYFNKLKKITKTVQQRYRAMKERNIQFQRYNKLRHSVIYIQAIF 2250
2251 RGKKARRHLKMMHIAATVIQRRFRTLMMRRRFLSLKKTAIWIQRKYRAHL 2300
2301 CTKHHLQFLQVQNAVIKIQSSYRRWMIRKRMREMHRAATFIQATFRMHRL 2350
2351 HMRYQALKQASVVIQQQYQANRAAKLQRQHYLRQRHSAVILQAAFRGMKT 2400
2401 RRHLXSMHFSATLIQSRFRSLLVRRRFISLKKATIFVQRKYRATICAKHK 2450
2451 LHQFLHLRKAAITIQSSYRRLMVKKKLQEMQRAAVLIQATFRMHRTYITF 2500
2501 RTWKHASILIQQHYRTYRAAKLRRENYIRQWHSAVVIQAAYKGMKARQLL 2550
2551 REKHKAAIIIQSTYRMYRQYCFYQKLQWATKIIQEKYRANKKQQKAFQHN 2600
2601 ELKKETCVQASFQDMNIKKQIXEQHQAAIIIQKHCKAFKIRKHYLHLRAT 2650
2651 VVSIQRRHRKLAAVRTQAVICIQSYYRGFKVRRDIQNMHQAATLIQSFYR 2700
2701 MHKAKVDYQTKKTAIVVIQNYYRLYVRVKTERKSFLAVQKSVRTIQAAFR 2750
2751 GMKVRQKLKNVSEERMAAVVNQSALCCYRSKTQYEAVQSEGVMIQEWYKA 2800
2801 SGLACSQEAEYHSQSRAAVTIQKAFCRMATRKLETQKCAALRIQFFFQMA 2850
2851 VYRRRFVQQKRAAITLQHYFRTWQTRKQFLLYRKAAVVLQNHYRAFLSAK 2900
2901 HQRQVYLQIRSSVIIIQARSKGFIQKRKFQQIKNSTIKIQAMWRRXRAKK 2950
2951 YLCKVKAACKIQAWYRCWRAHKEYLAILKAVKIIQGCFYTKLERTRFLNV 3000
3001 RASAIIIQRKWRAILSAKIAHEHFLMIKRHRAACLIQAHYRGYKGRQVFL 3050
3051 RQKSAALIIQKYIRAREAGKRERIKYIEFKKSTVILQALVRGWLVRKRII 3100
3101 EQRAKIRLLHFTAAAYYHLNALRIQRXYKLYLAVKNANKQINSVICIQRW 3150
3151 FRARLQQKRFIQKYHSIKKIEHEGQECLSQRNRAASVIQKAVXHFLLRKK 3200
3201 QEKFTNGIIKIQALWRGYSWRKKNDCTKIKAIRLSLQVVNREIREENKLY 3250
3251 KRTALALHYLLTYKHLSAILEALKHLEVVTRLSPLCCENMAQSGAISKIF 3300
3301 VLIRSCNRSVPCMEVIRYAVQVLLNVSKYEKTTSAVYDVENCIDILLELL 3350
3351 QIYREKPGNKVADKGGSIFTKTCCLLAILLKTTNRASDVRSRSKVVDRIY 3400
3401 SLYKLTAHKHKMNTERILYKQKKNSSISIPFIPETPVRTRIVSRLKPDWV 3450
3451 LRRDNMEEITNPLQAIQMVMDTLGIPY 3477
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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