 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62287 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MANRRVGRGCWEVSPTERRPPAGLRGPATEEEASSPPVLSLSHFCRSPFL 50
51 CFGDVLLGDSRTVPLALDNPNEEVAEVKISHFPAADLGFSVSQRCFVLQP 100
101 KDKIVISVDWTPFKEGRVREIMTFLINDVLKHQAILLGNAEKQKKKKRSL 150
151 WDTIKKKKISASTSHNRRVSNIQNVNKTFSVSQKVNRVRSPLQACENLAV 200
201 NEGGLPTENNSLTLEENKIPVSPISPAFNECHGATCLPLSVRRSTTYSSL 250
251 HASENRELLNVDNTNVSKVSFNEKAVAETTFNSMNVSGQSGENSKLILTP 300
301 NYSSTLNVTQSQINFLSPDSFVNNSHGANNELELVTCLSSDMFMTDNSKP 350
351 VHLQSTTAHEIYQKILSPDSFIKDNYGLNQDLESESVNLILSPNQFLKDN 400
401 MAYMCTSQQTCKVPLTNENSQVPQSPQDWSKSEVSPCIPECQGSKSPKAI 450
451 FEELVEMKSDYYSFIKQNNPKFSAVQDISSHSHNKQPKRRPILSATVTKR 500
501 KPTCTRENQTEINKPKAKRCLNSAVGGHEKVINNQKEKEDFHSYLPVIDP 550
551 VLSKSKSYKNQIMPSSTTASVARKRKSDESMEDANVRVAVTEHTEVREIK 600
601 RIHFSPSESKTSAVKKTKNVITPISKCISNREKLNLKKKTDLLIFKTPIS 650
651 KTNRRTKPIIAVAQSNLTFIKPLKTDIPRHPMPFAAKNMFYDERWKEKQE 700
701 QGFTWWLNFILTPDDFTVKTNISEVNAATLLLGVENQHKISVPRAPTKEE 750
751 MSLRAYTARCRLNRLRRAACRLFTSEKMVKAIKKLEIEIEARRLIVRKDR 800
801 HLWKDVGERQKVLNWLLSYNPLWLRIGLETIYGELISLEDNSDVTGLAMF 850
851 ILNRLLWNPDIAAEYRHPTVPHLYRDGHEGALSKFTLKKLLLLVCFLDYA 900
901 KISKLIDHDPCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLP 950
951 VNHVQTPFDEFDFAITNLAVDLQCGVRLVRTMELLTQNWSLSKKLRIPAI 1000
1001 SRLQKMHNVDIVLQVLKSRGIELSDEHGNTILSKDIVDRHREKTLRLLWK 1050
1051 IAFAFQVDISLNLDQLKEEIAFLKHTQSIKRTISLLSCHSDALINKKKGK 1100
1101 RDSGSFEQYSENIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHH 1150
1151 YHPCYVPFDAICQRTTQTVECTQTGSVVLNSSSESDDSSLDMSLKAFDHE 1200
1201 NTSELYKELLENEKKNFHLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVI 1250
1251 TYLSFLCARLLDLRKEIRAARLIQTTWRKYKLKTDLKRHQERDKAARIIQ 1300
1301 SAVINFLAKQRLRKRVNAALIIQKYWRRVLAQRKLLILKKEKLEKVQNKA 1350
1351 ASLIQGYWRRYSTRKRFLKLKYYSIILQSRIRMIIAVTSYKRYLWATVTI 1400
1401 QRHWRAYLRRKQDQQRYEMLKSSSLIIQSMFRKWKQRKMQSQVKATVILQ 1450
1451 RAFREWHLRKRAKEENSAIVIQSWYRMHKELRKYIYIRSCVIVIQKRFRC 1500
1501 FQAQKLYKRKKESILTIQKYYKAYLKGKIERTNYLQKRAAAIQLQAAFRR 1550
1551 LKAHNLCRQIRAACVIQSYWRMRQDRVRFLNLKKTIIKFQAHIRKHQQLQ 1600
1601 KYKKMKKAAVIIQTHFRAYIFARKVLASYQKTRSAVIVLQSAYRGMQARK 1650
1651 MYIHILTSVIKIQSYYRAYVSKKEFLSLKNATIKLQSIVKMKQTRKQYLH 1700
1701 LRAAALFIQQCYRSKKIATQKREEYMQMRESCIKLQAFVRGYLVRKQMRL 1750
1751 QRKAVISLQSYFRMRKARQYYLKMYKAIIVIQNYYHAYKAQVNQRKKFLR 1800
1801 VKKAATCLQAAYRGYKIRQLIKQQSIAAVKIQSAFRGYNKRVKYQSVLQS 1850
1851 IIKIQRWYRAYKTLHDTRTHFLKTKAAVVSLQSAYRGWKVRKQIRREHQA 1900
1901 ALKIQSAFRMAKAQKQFRLFKTAALVIQQNFRAWTAGRKQRMEYIELRHA 1950
1951 VLILQSMWKGKTLRRQLQRQHKCAIIIQSYYRMHVQQKKWKIMKKAALLI 2000
2001 QKYYKAYSIGREQHHLYLKTKAAVVTLQSAYRGMKVRKRIKDCNKAAVTI 2050
2051 QSKYRAYKTKKKYATYRASAIIIQRWYRGIKITHRQHKEYLNLKKTAIKI 2100
2101 QSVYRGIRVRRHIQHMHRAATYIKAMFKMHQSRISYHTMRKAAIVIQVRF 2150
2151 RAYYQGKMQREKYLTILKAVKILQASFRGVRVRWTLRKMQIAATLIQSNY 2200
2201 RRYKQQTYFNKLKKITKTIQQRYRAVKERNIQFQRYNKLRHSVIYIQAIF 2250
2251 RGKKARRHLKMMHVAATLIQRRFRTLMMRRRFLSLKKTAVWIQRKYRAHL 2300
2301 CTKHHLQFLQVQNAVIKIQSSYRRWMIRKKMREMHRAATFIQATFRMQRV 2350
2351 HMRYQALKQASVVIQQQYQANRAAKLQRQHYLRQRHSAVILQAAFRGMKT 2400
2401 RRHLKSMHSSATLIQSRFRSLLVRRRFISLKKATIFVQRKYRATICAKHK 2450
2451 LHQFLQLRKAAITVQSSYRRLMVKKKLQEMQRAAVLIQATFRMHRTYVTF 2500
2501 QTWKQASILIQQHYRTYRAAKLQKENYIRQWHSAVVIQAAYKGMKARQHL 2550
2551 REKHKAAIIIQSTYRMHRQYCFYQKLQWATKIIQEKYRANKKKQKALQHN 2600
2601 ELKKETCVQASFQDMNMQKQIQEQHQAAIIIQKHCKAFKIRKHYLHLRAT 2650
2651 VVSIQRRYRKLTVVRTQAVICIQSYYRGFKVRRDIQNMHRAATLIQSFYR 2700
2701 MHRAKVDYQTKKTAIVVIQNYYRLYIRVKTERKIFLAVQKSVRTIQAAFR 2750
2751 GMKVRQKLKNISEEKMAAIVNQSALCCYRSKTQYEAVQSEGVTIQEWYKA 2800
2801 SGLACSQEAEYHSQSRAAVTIQKAFRRMVTRKVETQKCAALRIQFFLQMA 2850
2851 VYRRRFVQQKRAAITLQHYFRTWQTRKQFLLYRKAAVVLQNHYRAFLSAK 2900
2901 HQRQVYLQIRSSVIIIQARSKGFIQKRKFQEIKNSTIKIQAMWRRYRAKK 2950
2951 YLCKVKAACKIQAWYRCWRAHKEYLAILKAVKIIQGGFYTKLERTWFLNV 3000
3001 RASAIIIQRKWRAILSAKIAHEHFLMIKRHRAACLIQAHYRGYKERQVFL 3050
3051 RQKSAALIIQKYIRAREAGKRERIKYIEFKKSTVILQALVRGWLVRKRIL 3100
3101 EQRTKIRLLHFTAAAYYHLNALRIQRAYKLYLAVKNANKQVNSVICIQRW 3150
3151 FRARLQQKKFIQKYYSIEKIEHEGQECLSQRNRAASVIQKAVRHFVLRKK 3200
3201 QEKFTSGIIKIQALWRGYSWRKKNDCTKIKAIRLSLQVVNREIREENKLY 3250
3251 KRTALALHYLLTYKHLSAILEALKHLEVVTRLSPLCCENMAQSGAISKIF 3300
3301 VLIRSCNRSVPCMEVIRYAVQVLLNVSKYEKTTSAVYDVENCVDTLLELL 3350
3351 QIYREKPGNKVADKGGSIFTKTCCLLAILLKTTNRASDVRSRSKVVDRIY 3400
3401 SLYKLTAHKHKMNTERILHKQKKNSSISIPFIPETPVRTRIVSRLKPDWV 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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