 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62286 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MATRRAGRSWEVSPSGPRPAAGEAAAASPPVLSLSHFCRSPFLCFGDVRL 50
51 GGSRTLPLLLHNPNDEAARVEVCRAPAAQQGFSVSPRRFQLQPKEKIVIS 100
101 VNWTPLKEGRVRETVTFLVNDVLKHQAILLGNAEEQKKKKRSLWGTIKKK 150
151 KMSASSNNKRISSAQNVNKTFCVSQKADRVRNPLQACENLDVNEGCSPTE 200
201 NNSLILEENKIPISPISPIFKECNGETSLPLSVRRSTTYISLHARENGDL 250
251 LKVEDASILEDFSFSEKVVNETSFNSTNNINDQTEENCKLILTPACSSTL 300
301 NITQSQGNFLSPDSFVKNSRAANNELEVVTCLSSNILMKDNSMPLHLESK 350
351 SVHENYRKILSPDSFINDNYGLNQDLESEPINPILSPNQFVKDNMAYTCI 400
401 YQQTCKLSLSSNKNSQFSQSQDPRTNGILPCIPECQSSKSPKATFEEHRA 450
451 LEMKSDCYSFTKNQPKFSAMQDISSCSQDKLTRRPILSATITKRKSNFTR 500
501 ENQKETNKPKAKRCLNSVAGEFEKVTDNIKEKDGFQSCLPVIDPVFSKPK 550
551 SYKIVVTPPSKTTLIARKRRSEGDREDANVRITATGHSEIQEIKRIHFSP 600
601 VESKTSVLKKTKKMTTPISKHFNREKLNLKKKTDSRIYRTPNSKTSKKTK 650
651 PIVAVAQSTLTFIKPLKTDIPRHPMPFAAKNMFYDERWKEKQEQGFTWWL 700
701 NFILTPDDFTVKTNISEVNAATLLLGVESQHKISVPRAPTKDEMSLRAYT 750
751 ARCRLNRLRRAACRLFTSEKMVKAIKKLEIEIEARRLIVRRDRHLWKDVG 800
801 ERQKVLNWLLSYNPLWLRIGLETVFGELLSLEDNSDVTGLAVFILNRLLW 850
851 NPDIAAEYRHPTVPHLYRDGHEEALSKFTLKKLLLLVCFLDYAKISKLID 900
901 HDPCLFCKDAEFKTSKDILLAFSRDFLSGEGDLSRHLSLLGLPVNHVQTP 950
951 FDEFDFAVTNLAVDLQCGVRLVRIMELLTRDWNLSKKLRMPAISRLQKMH 1000
1001 NVDIVLQILRSQGIQLNDEHGNAILSKDIVDRHREKTLALLWKIAFAFQV 1050
1051 DISLNLDQLKEEIDFLKHTQSMKKMSAQSCHSDVIINKKKDNRNSGSFEQ 1100
1101 YSESIKLLMEWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHHYHPYYVPF 1150
1151 DAICQRTTQTIECTQTGSVVLNSSSESDGSSLDLSLKAFDHENTSELYKE 1200
1201 LLENEKKNFQLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVITYLSFLCS 1250
1251 RLLDLCKETRAARLIQTTWRKYKLKTDMKRHQEQDKAARIIQSAIINFLT 1300
1301 KRRLKKEISAALAIQKYWRRFLAQRKLLMLKKEKLEKVQNESASIIQRYW 1350
1351 RRYSTRKQFLKLRYYSIILQSRIRMIIAVTSYKRYLWATVTIQRHWRASL 1400
1401 RRKHDQQRYKMLKSSCLIIQSMFRRWKRRKMQLQIKATRVLQRAFREWHV 1450
1451 RKRAKEEKSAIVIQSWYRMHKELRKYVQIRSCVVIIQTRFRCLQAQKLYK 1500
1501 RKKEAILTIQKYYKAYLKGKMERTNYLQKRAAAIRLQTAFRRMKARNLYR 1550
1551 QSRAACVLQSYWRMRQDRFRFLNLKKITTKLQAQVRKHQQLQKYRKIKKA 1600
1601 ALVIQIHFRAYVSAKKVLASYQKTRSAVLVLQSAYRGMQARKKFIHILTS 1650
1651 IIKIQSCYRAYISRKRFLRLKNATVKLQSIVKMRQTRKRYLHWRAASLFI 1700
1701 QRWYRSAKLAALKRHQYVQMRESCIKLQAFVRGHLVRKQIRLQRQAAISL 1750
1751 QSYFRMRKKRQYYLEIYKATLVIQNYYRAYKAQVNQRKNFLQVKRAVTCL 1800
1801 QAAYRGYKVRQLIKQQSIAALKIQTAFRGYRERKKYQYVLQSTIKIQRWY 1850
1851 RTCRTVRDVRTQFLKTRAAVISLQCAFRGWKVRKQIRRERQAAVRIQSAF 1900
1901 RMAKAQKQFKLLKTAALVIQQHLRAWTAGKRQRMEYIELREAALRLQSTW 1950
1951 KGKRVRRQVQKQHKCAVIIQSNYRMYVQQKKWKIMKKAARLIQMYYRAYS 2000
2001 IGRKQRQLYLKTKAAAVVLQSAYRSMKVRKKIKECNRAAVTIQSTYRAYK 2050
2051 TKKNYTNCRASAIIIQRWYRGTKIANHQRKEYLNLKKTAVKIQAIYRGIR 2100
2101 VRRHNRHLHMAATVIQAMFKMHQAKMRYHKMRTAAVIIQVRYRAYREGKI 2150
2151 QRAKYLTIMKAITVLQASFRGTRVRQTLRKMQSAATLIQSYYRRHRQQAY 2200
2201 FTKLKKVTRTIQQRYRAVKERNTQLQRYNKLRHSIICIQAGFRGMKARRH 2250
2251 LKSMHLAATLIQRRFRTLRMRRRFLHLRKTAIWIQRKYRATVCAKHYFQH 2300
2301 FVRVQKAVIKIQSSYRGWMVRKKMQEVHRAATAIQAAFRMHRANVKYQAL 2350
2351 KHASVVIQQQFRASRAAKLQRQCYLQQRHSALILQAAFRGMKARGHLKNM 2400
2401 HSSATLIQSTFRSLVVRKRFISLKRATVFVQRKYRATICARHHLHQFLKL 2450
2451 KKAVITIQSSYRRLVAKKKLQEMHRAAVLIQATYRMHRTYVTFQTWKHAS 2500
2501 ILIQQHYRTYRAAKLQRENSVPQRRSALIIQAVYKGMKARQLLREKHRAA 2550
2551 IIIQSTYRMYRQYLFYQKIQWATKVIQEKYRANKKKALQHSALRNAAAAC 2600
2601 TEADFQDMILRKPTQEQHQAATIIQKHFKASKVRKHYLHLRANVIFVQRR 2650
2651 YRALTAVRTQAVVCTQSSYRSCKVQEDMQHRHLAATRIQSFYRMHRAKLD 2700
2701 YQAKKTAVAVIQNYFRSYIRVKVEREKFLAVQKSVRIIQAAFRGMKVRER 2750
2751 LKSLSEVNMVASAKQSAFYSCRTEAQCAAVHGSALRTQKWHGASLVTCSQ 2800
2801 EAEYPQREALVATQRAFCEMVTRKLETQKCAALRIQYFLQMAVCRRRFVQ 2850
2851 QKRAAVTLQQYFRTWQTRRQFLLYRKAAVVLQNHHRAFLSTKHQRQVYLQ 2900
2901 IRSSIIMIQARTRGFIQKRRFQKIKDSTIKIQAVWRSYKARKYLHQVKAA 2950
2951 CKIQAWYRYWKARKDYLAVLKAVKIIQGCFYTKLERRRFLNVRASTIIIQ 3000
3001 RKWRAILSGRIACEHFLMIKRHQAACLIQANFRRYKGRQVFLRQKSAALT 3050
3051 IQRYIRARKAGKCQRIKYVELKKSTVVLQALVRGWLVRKRILEQRTKIRL 3100
3101 LHFTAAAYYHLSALKIQRAYKRHMAMKNANKQVNAVICIQRWFRTRLQQK 3150
3151 RFAQKYPSILTSQHEVPECMSQQNRAASVIQKAVRRFLLRKKQEKFNNAI 3200
3201 SRIQSLWRGYSWRKKNDCTKIKAIRLSLQLVNREIREENKLYKRTALALH 3250
3251 YLLTYKHLSAILEALKHLEVVTRLSPLCCENMAQSGAVSKIFVLIRSCNR 3300
3301 SIPCMEVIRYAVQVLLNVAKYEKTTAAVSDVENCIDTLLDLLQMYREKPG 3350
3351 DKVADKGGSIFTKTCCLLAVLLKTTNRASSVRSKSKVVDRIYSLYKLTAR 3400
3401 KHKMNTERTLYKQKMNSSISTPFIPETPVRTRIVSRLKPDWVLRRDNMEE 3450
3451 ITNPLQAIQMVMDTLGIPY 3469
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.) |
Go back to the NucPred Home Page.