 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62292 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MANRRVGRGCWEVSPTERRPPAALRGPATEEEASSPPVLFLSHFCRSPFL 50
51 CFGDVLLGDSRTLPLALDNPNEEVAEVKISHFPAADLGFSVSQRCFVLQP 100
101 KEKIVISVNWTPFKEGRVREIMTFLVNDVLKHQAILLGNAEKQKKKKRSL 150
151 WDTIKKKKISASTSHNRRVSNIQNVNKTFSVSQKVDRVRSPLQACENLAM 200
201 NEGGPPTENNSLTLEENKIPISPISPAFNECHGATCLPLSVRRSTTYSSL 250
251 HASENRELLNVDSANVSKVSFNEKAVTETSFNSINVNDQSGENSKLILTP 300
301 NYSSTLNITQSQINFLSPDSFVNNSHGANNELELVTCLSSDMFMTDNSKP 350
351 VHLQSTTAHEIYQKILSPDSFIKDNYGLNQDLESESVNPILSPNQFLKDN 400
401 MAYMCTSQQTYKVPLSNENSQVPQSPQDWSKSEVSPCIPEYQGSKSPKAI 450
451 FEELVEMKSNYYSFIKQNNPKFSAVQDISSHSHDKQPKRRPILSATVTKR 500
501 KPTCTRENQTEINKPKAKRCLNSAVGEHEKVIHNQKEKEDFHSYLPVIDP 550
551 VLSKSKSYKNQIMPSLTTASVARKRKSDGSMEDANVRVAVTEHTEEREIK 600
601 RIHFSPSEPKTSAVKKTKNVITPISKCISNREKLNLKKKTDLLIFKTPIS 650
651 KTSKRTKPIIAVAQSNLTFIKPLKTDIPRHPMPFAAKNMFYDERWKEKQE 700
701 QGFTWWLNFILTPDDFTVKTNISEVNAATLLLGVENQHKISVPRAPTKEE 750
751 MSLRAYTARCRLNRLRRAACRLFTSEKMVKAIKKLEIEIEARRLIVRKDR 800
801 HLWKDVGERQKVLNWLLSYNPLWLRIGLETIYGELISLEDNSDVTGLAMF 850
851 ILNRLLWNPDIAAEYRHPTVPHLYRDGHEGALSKFTLKKLLLLICFLDYA 900
901 KISKLIDHDPCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLP 950
951 VNHVQTPFDEFDFAITNLAVDLQCGVRLVRTMELLTQNWNLSKKLRIPAI 1000
1001 SRLQKMHNVDIVLQVLKSRGIELSDEHGNTILSKDIVDRHREKTLRLLWK 1050
1051 IAFAFQVDISLNLDQLKEEIAFLKHTKSIKKTISLLSCHSDALINKKKGK 1100
1101 RDSGSFEQYSENIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHH 1150
1151 YHPCYVPFDAICQRTTQTVECTQTGSVVLNSSSESDDSSLDMSLKAFDHE 1200
1201 NTSELYKELLENEKKNFQLIRSAVRDLGGIPAMINHSDMSNTIPDEKVVI 1250
1251 TYLSFLCARLLDLRKEIRAARLIQTTWRKYKLKTDLKRHQERDKAARIIQ 1300
1301 SAVINFLAKQRLRKRVNAALIIQKYWRRVLAQRKLLILKKEKLEKVQNKA 1350
1351 ASLIQGYWRRYSTRKRFLKLKYYSIILQSRIRMIIAVTSYKRYLWATVTI 1400
1401 QRHWRAYLRRKQDQQRYEMLKSSSLIIQSMFRKWKRRKMQSQVKATVILQ 1450
1451 RAFREWHLRKRAKEENSAIVIQSWYRMHKELRKYIYIRSCVIVIQKRFRC 1500
1501 FQAQKLYKRKKESILTIQKYYKAYLKGKIERTNYLQKRAAAIQLQAAFRR 1550
1551 LKAHNLCRQIRAACVIQSYWRMRQDRVRFLNLKKTIIKLQAHIRKHQQLQ 1600
1601 KYKKMKKAAVIIQTHFRAYIFTRKVLASYQKTRSAVIVLQSAYRGMQARK 1650
1651 VYIHILTSVIKIQSYYRAYVSKKEFLSLKNTTIKLQSIVKMKQTRKQYLH 1700
1701 LRAAALFIQQCYRSKKITTQKREEYMQMRESCIKLQAFVRGYLVRKQMRL 1750
1751 QRKAVISLQSYFRMRKARQYYLKMCKAIMVIQNYYHAYKAQVNQRKNFLR 1800
1801 VKKAATCLQAAYRGYKVRQLIKQQSIAALKIQSAFRGYNKRVKYQSVLQS 1850
1851 IIKIQRWYRAYKTLHDTRTHFLKTKAAVVSLQSAYRGWKVRKQIRREHQA 1900
1901 ALKIQSAFRMAKAQKQFRLFKTAALVIQQNFRAWTAGRKQRMEYIELRHA 1950
1951 VLILQSMWKGKTLRRQLQRQHKCAIIIQSYYRMHVQQKKWKIMKKAALLI 2000
2001 QKYYKAYSIGREQHHLYLKTKAAVVTLQSAYRGMKVRKRIKDCNKAAVTI 2050
2051 QSKYRAYKTKKKYATYRASAIIIQRWYRGIKITHRQHQEYLNLKKTAIKI 2100
2101 QSVYRGIRVRRHIQHMHRAATFIKAMFKMHQSRISYHTMRKAAIVIQVRF 2150
2151 RAYYQGKMHREKYLTILKAVKILQASFRGVRVRWTLRKMQIAATLIQSNY 2200
2201 RRYKQQTYFNKLKKITKTIQQRYRAVKERNIQFKRYNKLRHSVIYIQAIF 2250
2251 RGKKARRHLKMMHVAATLIQRRFRTLMMRRRFLSLKKTAVWIQRKYRAHL 2300
2301 CTKHHLQFLQVQNAVIKIQSSYRRWMIRKKMREMHRAATFIQATFRMHRV 2350
2351 HMRYQALKQASVVIQQQYRANRAAKLQRQHYLRQRRSAVILQAAFRGVKT 2400
2401 RRHLKSMHSSATLIQSRFRSLLVRRRFISLKKATIFVQRKYRATICAKHK 2450
2451 LHQFLQLRKAAITIQSSYCTIRRLMVKKKLQEMQRAAVLIQATFRMHRTC 2500
2501 VTFQTWKQASILIQQHYRTYRAAKLQKENYIRQWHSAVVIQTAYKGMKAR 2550
2551 QHLREKHKAAIIIQSTYRMYRQYCFYQKLQWATKIIQEKYRANKKKQKAL 2600
2601 QHNELKKETCVQASFQDMNIQKQIQEQHQAAIIIQKHCKAFKIRKHYLHL 2650
2651 RATVVSIQRRYRKLTAVRTQAVICIQSYYRGFKVRRDIQNMHRAATLIQS 2700
2701 FYRMHRAKVDYQTKKTAIVVIQNYYRLYVRVKTERKSFLPVQKSVRTIQA 2750
2751 AFRGMKVRQKLKIVSEEKMAAIVNQSALCCYRSKTQYEAVQSEGVMIQEW 2800
2801 YKASDLACSQEAECHSQSRAAVTIQNAFRRMVTRKLETQKCAALRIQFFL 2850
2851 QMAVYRRRFVQQKRAAITLQHYFRTWQTRKQFLLYRKAAVVLQNHYRAFL 2900
2901 SAKHQRQVYLQIRSSVIIIQARSKGFIQKRKFQEIKNSTIKIQAMWRRYR 2950
2951 AKKYLCKVKAACKIQAWYRCWRAHKEYLAILKAVKIIQGCFYTKLERTWF 3000
3001 LNVRASAIIIQRKWRAILSAKIAHEHFLMIKRHRAACLIQAHYRGYKERQ 3050
3051 VFLRQKSAALIIQKYIRAREAGKRERIKYIEFKKSTVILQALVRGWLVRK 3100
3101 RILEQKTKIRLLHFTAAAYYHLNALRIQRAYKLYLAVKNANKQVNSVICI 3150
3151 QRWFRARLQQKKFIQKYSIKKIEHEGQECLSQQNRAASVIQKAVRHFVLR 3200
3201 KKQEKFTSGIIKIQALWRGYSWRKKNDCTKIKAIRLSLQVVNREIREENK 3250
3251 LYKRTALALHYLLTYKHLSAILEALKHLEVVTRLSPLCCENMAQSGAISK 3300
3301 IFVLIRSCNRSVPCMEVIRYAVQVLLNVSKYEKTTSAVYDVENCIDTLLE 3350
3351 LLQIYREKPGNKVADKGGSIFTKTCCLLAVLLKTTNRASDVRSRSKVVDR 3400
3401 IYSLYKLTAHKHKMNTERILHKQKKNSSISIPFIPETPVRTRIVSRLKPD 3450
3451 WVLRRDNMEEITNPLQAIQMVMDTLGIPY 3479
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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