 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62285 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 PNEEAAAVWLSHGPAAERGFTIWPSAFVLQPKEKIVVSITWTPLKGGRIR 50
51 ETITFLVNDILKHQAILLGNAEEPKKKKRTLWDTINKKKASASSRHNKKA 100
101 SNIQNVNKTFNVSPKADRVRSPLQACENLATNGNCSPPESNPLILEENKL 150
151 PISPISPASQECHRGTCLPLPVRRSTTYTSLPASENGGLVKADGANTAED 200
201 FHFNEKGITETSFDSIDNVNSQIEENGKLTLTPNYSSSLNITQSQGHFLS 250
251 PDSFVNNSHASNNEPEFVKCLSPDMFVKGNTRPVILESKRVHEICRKILS 300
301 PDSFINDNYGLNEDLETESINPILSPNQFLKDNMAYICVSQQTCQLPLST 350
351 GHFQDSQPPQDERKNAAVPCISECQQLESPKATFEASKALEVMSNSYTFK 400
401 KQNQPKFSAVQDISSHSRKKPIKRRPILSATVTKRKPTCAVENQMETVKP 450
451 KAKRCLNVVVGDCXKETDDQKEKDDFHPFLPIRDLIXXRPKSSKNIVTPP 500
501 CKVASVARKRKSEGHTGDENVRITVTECXEXQEVKRPHFSPVESKTSTVK 550
551 HTKKVVTSSLKRVSHREKLNLKKKTDSLGYRTPKTNRRTRPFVPVAQSNL 600
601 TFIKPLKGIPRHPMPFAAKNMFYDERWKEKQEQGFTWWLNFILTPDDFTV 650
651 KTNISEVNASTLLLGVESQHKVSVPKAPTKDEVSLRAYTARCRLNRLRRA 700
701 ACRLFTSEKMVKAMKKLEIEIEARRLIVRKDRHLWKDVGERQKVLNWLLS 750
751 YNPLWLRIGLETIYGELVPLEDNSDVTGLAMFILNRLLWNPDIAAEYRHP 800
801 SVPHLYRDGHEEALSKFTLKKLLLLICFLDYAKISRLIDHDPCLFCKDAE 850
851 FKASKEILLAFSRDFLGGEGDLSRHLSLLGFPVTHVQMPFDEFDFAVKNL 900
901 AVDLQCGVRLVRTMELLTQNWNLSKKLRIPAISRLQKMHNVDIVLEILKS 950
951 RGIQLNDEHGNAILSKDIVDRHREKTLALLWKIALAFQVDISLNLDQLKE 1000
1001 EIDFLKNTQSMKKTMSALSCRPDAVISKKRDERHSGPFEQCSESVKLLMD 1050
1051 WVNAVCGFYNKKVENFTVSFSDGRVLCYLIHHYHPCYVPFDAICQRTTQT 1100
1101 VECTHTGSVVLNSSSESDGSFLDFSLKPPDQENTSELYKELLENEKKNFQ 1150
1151 LVRSAARDLGGIPAMIHHSDMSNTIPDEKVVITYLSFLCARLLDLRKETR 1200
1201 AARLIQTTWRQYKLKKDLKHHQERDKAARIIQSAIINFLTKQRFKKKVSA 1250
1251 ALVIQKYWRRALAKRKLLMLKKEKLERVHSKSASIIQRHWRRYSTRKQFL 1300
1301 KLKYYSIFLQSKIRMIIAVASYKRYHWATVTIQRRWRAHVRSKQDRQRYE 1350
1351 LLRSSTLVIQFAFRRWRRRKRQSQINAAITLQRAFRQWRVQKCAQEERAA 1400
1401 VVIQSWYRMHRELRKYIHLRSCVIIIQARFRCFQAQKLYTRTRESILTLQ 1450
1451 KHYRAYVKGKVERTGYLQKRAAAIRLQAAFRGRRARNLCRQIKAACVLQS 1500
1501 YWRMRQDRLRFLNLKKNIIRLQAHIRRRQQLHTYQKMKKAALIIQIHFRA 1550
1551 YMSAKEVLASYQKTRSAVIVLQSACRRMQARKKFLHILTSIVKIQSYYRA 1600
1601 YASRRKFLRLKKATVKLQSIVRMKLARKQYLHLRAIAQQREEHRRASCIK 1650
1651 LQAFLRGYLVRKQVRLQRKAAVSLQSYFRMRKMRLDYLKVCHAAVVIQRY 1700
1701 YRAHRAGAQQRKHFLQVRRAVTYLQATYRGYKVRRQLQQQSAAALKIQAA 1750
1751 FRGYRQRTKYQSVLQSALKIQRWYRTHKTVSAIRSHFFKTRTAAISLQSA 1800
1801 YRGWKVRKQMRKEHEAAVKIQSAFRTARAQKEFRVLKTAASVIQQHLRAR 1850
1851 AAGRRQRTEYTALRRAAVMLQSAWRGRAARRRIQKQQRCAIIIQAYYRRH 1900
1901 VQQKRWEIMKKAAHLIQMHYRAYRTGRKQHHLFLKTKXAAIILQSAFRGV 1950
1951 RVRKKVKEMHQAAATIQSRYRAYQARKKYASYRAAAVIIQRWYRAAKLAG 2000
2001 RQREEYLAVKKAALKIQAVYRGVRARRHIRRMHMAATLIKAAFKMQQSRR 2050
2051 RYQQMRTAAIIIQVRYRAYCQGRAQRAKYLMILKAVALLQAALRGARVRQ 2100
2101 SLRRMRTAATLIQAHYRGRRQQAYFNKLKKVTKTVQQKYRAARERHAQLR 2150
2151 RYNQLRRSAICIQAAFRGMRARRRLKAMHSAAAVIQRRFRTLGMRRRFLS 2200
2201 LRKTAVWVQRKYRAKVCTRHHVQQLRLQKAAIKIQSWYRGWMVRKKIQEM 2250
2251 RRAATVLQAAFRRHRTRARYQAWRCASQVIQQRFRAGRAARLQRRQYLQQ 2300
2301 RHSALVLQAAFRGMRVRRRLKRMHASATLIQSRFRSIMMRKRFLSLKKAA 2350
2351 VFVQRKYRATICAKHHLHQFLELQKAIIIIQASYQRRMVKKQLQEMHRAA 2400
2401 ALIQASFRMHRARLAFQTWKHAAVLIQQRYRACRAAKLQRALYIRWRHSA 2450
2451 VVIQAAYKGLKARQLLREKHRAAVIIQSTYRMYRQHFFYQKLQWATKVIQ 2500
2501 ERYRASKRKALQHDALKAATCARAGFQDMVVRRLIEERRHQAAITIQEHF 2550
2551 RAFKTRKHYLHFRAKVVFVQRRYRELMAVRTQAVICIQSCFRGFKARRGI 2600
2601 QRMHLAATRIQSCYRRHRARADYQAKKRAVVVIQNHYRSYIRVKMERKEF 2650
2651 LAIQKSARTIQAAFRGMKVRQKLKTMPDKKMAAPATQPAFYCHRTESQHE 2700
2701 AGESPALVAQGLYKTSLVGPSQETEQHSQRKAAVTIQKAFRKMVTRRLEK 2750
2751 QRRAAVRIQSFLQMAVYRRRFLQQKRAALTLQRCFRTQQSRKQFLLYREA 2800
2801 AVGLQNPHRTSLPAKHQRELYLQIRSSVIIIQARVKGFIQKRKFRELKDS 2850
2851 TIKIQAVWRRHKARKYLREVKAACRIQAWYRCWKARREYLAVLRAVRIIQ 2900
2901 RCFCXQQQRRRFLNVRASAVIIQRRWRTVLSGRTTHEQSLMTKRHQAACL 2950
2951 IQANFRGYKARQAFLQQKSAALTIQRYIRARKAGKHQRMKYVELKKSTVV 3000
3001 LQALVRGWLVRKRISEQRAKIRLLHFAAAAFYHLSALRIQRAYRRHVALK 3050
3051 HANNKQLNSAICIQRWFRARSQRKRFLQKYSIINIQREAREQARQHSRAA 3100
3101 SVIQRAVRRFLLRKKQENFNKRIAKIQALWRGYSWRKKNDSTKTKAIRQR 3150
3151 LQCVNREIREESKLYHRTAVALHHLLTYKYLSTVLEALKHLEAVTRLSSI 3200
3201 CCEKMAQSGAISKIFVLIRSCNRSVPCMEVIRYAMQVLLNVAKYEKTTSA 3250
3251 IYDVENCVDTLLELMQMYQEKSGDKVADKSRSIFTKTCCLLAVLLKTTTR 3300
3301 ALDVQSRSKVVDRIYSLYKLTAHKHKVNTERILCKQKKNSSVSLSFFPET 3350
3351 PVRTTMVSRLKPDWVLRRDNV 3371
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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