 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8T5T1 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MSGYFTDLQNAEVLRAAESIVSSGQAVLLCGPSASGRTALLSHLASKLGA 50
51 KPPIPLHLTTAQDTRDLIGSYVMTNKPGDFRFILGPLAYAAQSGRWISIE 100
101 EITTISQDSLLLLSSVVNTRTLSVGSYQISVHPDFRIHAKTSADPALLST 150
151 IVKSMFYPLVLPPLGNHLFQVLTKHCNRGICTILAHIHVQFETHRLTAGR 200
201 ISASDMVKWARRIDLDLKNGGAVLNKAVQEASAGTAATTLVSWLDDNTRM 250
251 VMIKNAWEAFVLRYSKEQTRRAFLDILRLSLNIHEELMLSTLLKIKVDIR 300
301 HDYTQKRLVCGRVSLPFYESPDERSLDDICRTNHTTRLLEIISSAIRNNE 350
351 PLLLVGPTGIGKTTCLQVIARALGKKLHVVNMSSQTDAADLLGGYVPATL 400
401 DRILRNLYDAITGSLGLYISVRKNQIFVTELHKSYAEKDIAKFLANCETA 450
451 LSGMRQVLRKEVGEVVEQGSTEQRVSHDKKYARRGTYENSLYFLQSLGDT 500
501 LEQATELHKILCHEVETKKPTMAFKYQEGLLVQALVKGDWILIDEINLAS 550
551 YDLLDVISQLVNPEHNEIAIPDKGFVAKNPHFRVFAAMNPGSDVGKKDLP 600
601 PTIRRCFTEINVSEMSDDVDIVALTKSYLRMDDAMIEDRQGLDPNVVYQL 650
651 FTVLKARAKTDLVTAAEAKSPCFSLRSLCRALSFAHRFRAAAGLRRSMYE 700
701 GFMLAFGSMLNEKSRQAVHALILDKLLNGNKEYLLNPFIIQPFNKPCTAM 750
751 IFQATTKDEAVTVEYQEPQAAHGFLEEEKSRARSTDYVLTKSTRSYMTTI 800
801 SRAIAAQLPILLEGTTSSGKTSLIKHIAKQFGCPITRINNHEHTDLSEYF 850
851 GSYQPDPLTGQLVFKDGPLVTGMKQGHWVILDELNMASSEILEALNRLLD 900
901 DNRELLVPDTQEIVRPAPGFLLFATQNPSGSYAGRKMLSEAFQNRFIMIE 950
951 FADISTEELKEILINRSTSRHLAPQYCEKLITTIQAIKMQLSHRNNNAVM 1000
1001 TNALITLRDLFRVADRLPRTLTELAMGIFELIGERQRDPADKQLVAEIIA 1050
1051 KNLGLKTFSVSAAEQEYAIRVRPIQSMIEKALKAGADSPKSAFLLKFQDI 1100
1101 VWTPSMIRLFALLFTSVSNGESPLLVGVSGAGKTTSVELIAAIMAQQLVQ 1150
1151 VNLHKHTESADFIGSLRPLRSRESMHAHLSVLKEYAATTTNVDKAVYAEI 1200
1201 KRLETTLGTKLFEWEDGPILNCMKRGHILLLDEVSLADESVLERLNSVLE 1250
1251 TSRELTVAENPNMPRPVIAHTGFKLIATMNPAGDYGKKELSPAMRSRLTE 1300
1301 FWMPHIRDINEVRMILTRKLQKTAIYRLGHQQGLDVVSLLADFFAEMDRI 1350
1351 TQSGRLGDVFTLSIRDILAVCDYITEVQTKEGTELGQMLIDAVTLSILDG 1400
1401 LPVRTQLGNMPACREVKHEMVLYLANRILSANLTDIDLIQDLTVSISKDT 1450
1451 NELTFLQASTQTVLATIPPGPQYNHAKALRKMTSFRLDAPTTIKNACRIA 1500
1501 KALRFQRPILLEGDPGVGKSALVSAIAEICGYSLVRINLSEQTDLSDLLG 1550
1551 SDLPAENGFRWVDGVLLKAVKEGAFILLDELNLANQTVLEGLNSLLDHRR 1600
1601 SLFIPELMLSVKSPDTLRIFGTQNPRSQGSGRKGLPQSFINRFLNVYVDV 1650
1651 LKSQDYDWILSNLFTDIPTYVLDYILGSTKQLRAGLEELKFGISGGPWEL 1700
1701 NVRDMMRLCDMLTTTPGLAQGITLAHAKHYAHILFGYRFRTADDDAYVQN 1750
1751 VLFSPHSLDSVAEATQSHHWYSLRPLWNVTEDVVKIGSLAIPRIDPVFRD 1800
1801 VLGTKLSYAHTSCFLTAQLVALEALAVAVTQNLSCILVGSPESGKSSILR 1850
1851 TLADVVRQPLVHISASTATDVSDLIGAYEQVDVLYDLKSVIRDLCLFVHS 1900
1901 LPIKASYSAISFSEFLKDLTASLDREIGDGLGSKSRAISMESIVSEVVQK 1950
1951 LLLEFMAKFSPDLDSGGLAAKNFSLFCSEILTQDTTPVDERFSPHMPTIP 2000
2001 ELCKSMASLYLSTQQSQTGRFVWRDSELIRALERGYWVELTNANFCMPSV 2050
2051 LDRLNSLLERGGGLEILEQGLEETRSIKVHPNFRLFISYAPTSDISRALR 2100
2101 NRSLELSIDIQVSHYNLLDIVRCVYTSSTASGVSLDPYLIKLVSLVYFEL 2150
2151 SRDNPEIVGLGLLFKWLKLLVTYHKRFSTSVGDPKAVSSWVDRSFSSVFI 2200
2201 ASTLNADLHRQYMQVYQNARLALQGDGSLEACTLVEAAAVLNQLTLGAVR 2250
2251 KLSISNSLQHSLALLELRTTVKPLDQLVSLADQEPQIMEPFSQVSGWLAI 2300
2301 CVGHSLDFQHHKELVSNLLTSTDINPMYLLTPPEPSSQALLKYISQGYLD 2350
2351 MYSSPDSIIHQVIKYVSLCSPFYASILKLFIEGKFTTKDSYLLLYLIELS 2400
2401 QHLATDPNNYDYVALFFSLFELLISHSIDPSTKTYSLGQRQVMRTFFDMI 2450
2451 LTDQYFESMPRLLSGYIRSLFISAARNDLETAWLAVRKNLALARISVHYL 2500
2501 PELFKVFRYVIERLNLCISPEGVVFLQEGLSDSITASLWKDILATVIYMA 2550
2551 CRADDQTGDYVPKLAILQDSANRIDLCASKLSGNQHPTARALAQMQSLFL 2600
2601 LFTGKAFCMQVSDTLEGWSQRDLTTQLPILLFRSMSFYHRNAAGPITYLR 2650
2651 GPIFVSTGEFVTSSLSVPALLTNAIEEQATSVLKSLLCRSEDFLSYVGNV 2700
2701 INSITTIVLRLFFAALEREAPSCDFTDGSFQLFALILETIVHSSNPLLAS 2750
2751 LTQGVEDILKAIISVQLSPSPLLLSGLVLTVASLVLYLLTSIANQRDPIV 2800
2801 MTKHREVLYRRLAKLIATSIEDSKLHALFVFGDGALSESSSIYKELSTLY 2850
2851 NSYAEAADQYAAELLVRADDEDNTFFEDLSSQLASLDGFEVHVLTFVSGV 2900
2901 LNLEKVLQMHESTECIEAILDGLAVAERQISTTLAALLPRLINYTVESSY 2950
2951 YLDMTIPIASFLMCLRVGLSAVISSVYAHVCDAINAARRLSSEGIGLQIT 3000
3001 LQDLLLLIQRVVVGTGDEFLDLLCYDKFIRSCCSSKTVSTLHVARLFTLS 3050
3051 LLQYSSCSKGVQGDVSQFLLADTLRPERVSSIAAQFEIEEARSYRKYIAD 3100
3101 HSIFKTRNQSSTAEGVSEEDELKTQIAALFPKNDSHFIKSDTSSDDEQPI 3150
3151 SMSAQTKVDYDDAYATSFFTRMNMILIIGNCILHLYTSDTEALPIQERLQ 3200
3201 NLSSILQAHVPHLSRPLVALQAVTYALSICAGGVSSNLSIRSPLTPETDR 3250
3251 LMYPLVTGATMGLVRVLSSRSPNVDYAKLQTGKTRRAIHCEDIYSRPYPY 3300
3301 ETAEFCSTLLRKIIVRTQHLLTEWPDNVQLLDIMHVCDKLMELPVLEAPL 3350
3351 VMFLTGAEVLVRHLETWDKGAPAVYKYVTNPNVPVPESLTKSLIHLMVTW 3400
3401 RRVELMSWNGFFSRVHTQFSNVSLTYWPELLIASLTYQRSVTQGEPSTEA 3450
3451 YYAQMREFLYESNVGEFPVRLKLLYTLSALLSNIEELAEPELVSSPHKTM 3500
3501 TSAVLFSIASEAVEFAKYMNKEFEEEAAQIKKRFEDQIRIFEWNERSSYT 3550
3551 SWLTAQNSHRLCTKIIREYTEVLQHATRAFHMGYSATYDRIPLSVTVQEY 3600
3601 LPLSSESGMLPPALSQELVERLNYCVEQMCHGRKESETLKRNEKNRLLSD 3650
3651 VLDVLKETLGCVLSSGFAFKQSFGWKLAHLCLPSVHSTFELMHMFHDSLC 3700
3701 QRCNEVEPHDIERMKRIIACLNQVFVQLSELVVELADIELLSRVMRTAPT 3750
3751 GSSKSQADSVLYVYLADNSVPQLLTTQVMSHVTKAHKMCMSLQEFLIGLP 3800
3801 LDSCKSSPTKLHEQIGGFLERFSADSVTALNRIKIFSKFSALIKRLCSIT 3850
3851 LIPLAFFEEAKAILQQILADLSPIFSALIAVLPTAPISTFVTEHAKYLAQ 3900
3901 VTEVIDTLTETAAQCRVEEAATVSSIEWPYFALPFASVGEEGLSVEIIVD 3950
3951 NAKKQVSAYMTAFFTSTLQPILQCATRHVVSLPVLPQKLLCQIETLKGDM 4000
4001 QALADVLAKFIGPLISGAIALLRTGFHCHVDDDQKETDNDNQSGLGLGDG 4050
4051 TGDQNVSKEAAKELCEDDLMGNQNDRDQQEQQEKDGDDEAIEMQNDFGGM 4100
4101 SESLHHDIEEEDSNGSDEEEVLEKEMGDEQGEAIDERNYNEDDDSAEEYS 4150
4151 DEQKKNTNNNNDTMELAAQEKDQDSINSELSDPSGEQHEEQADATGSTDE 4200
4201 QAQEDDYNDLDDKNLSGQSDLSVPEADGEDETVNEELEEEQQQMSDLSNP 4250
4251 DQDACAIEEDDDRDLPSSDENAEEHDEHEAPVDIDDNEASDEQSTYNDND 4300
4301 RDDAINISAQQQATNDEEEMQKDTEYDQENITDSNPDANEVGTNDQKQTH 4350
4351 EDNDQFRQENIEDQWEAESTENSQGEGAESADLKEGNPDMSLEEFQRIWK 4400
4401 ERLNIHDRESEKDEAAEPQDMPLQSNKTVEFDDSKSGRDGALGLTESKHR 4450
4451 NLTNQEFDNPNEERNVEHNSSCETSQSSHDRPPAEHLNPEISDEGEESST 4500
4501 ASDKQEQAVLSHMRESSKDLLNPEGEVYQELAVSLASEETKRAPEDVAAA 4550
4551 SARGNHLLLDLIKQTSAAAFSLAERLRIILEPTVTSDLKGDFRTGKKLNL 4600
4601 RRIIPFIASEFQKDKIWLRRTKPSKRVYQVLLAVDDSSSMAPIAKYALQA 4650
4651 ITLLFNACKFLEVGQLSVFSFGQKFELLLPITDQYNDASLAYAIGSFTFA 4700
4701 QNETRVSDAISIASDYLDSVRFFKGSDSALQLLLMISDGRLKEKGGVARE 4750
4751 IRKCMGRGQLPVLIVLDTDKKSIMDIKSVSFITDSSGKRVRQTSMYLDDF 4800
4801 PFQCYALLRHIEDLPETLVAVIKRWIESVSVYNAV 4835
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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