 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q01886 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MTMPHHSSGPAKDSPLCRFPPFPGGNPVFTNIRREKVNFQLPPPDHALLA 50
51 AAWAVLLRLYTGHVKTCFESATSDQEANLVTYEARDSDTLQTIVLRGACV 100
101 SSTAEEKAGLRDLNTAVVRTTVSIDSWTDEMLQDKIAALLQPGKEIVLFQ 150
151 TPSGCVLVYMQSFMSAMEVKNVSSTLTYIMSSDPDKTAIRNLSISPRDLA 200
201 QIMRWNDRKLKSERTNLVYDLFSARAHEQDANMAIDAWDGRMSYTELERV 250
251 SSTWARQLQKQGISQGSWVLFCFEKSRLAVVSMIAILKAGGVCVPIDPRY 300
301 PVERIRDIIRTTNATIALVGAGKTAALFKSADTAVQTIDITKDIPHGLSD 350
351 TVVQSNTKIDDPAFGLFTSGSTGVPKCIVVTHSQICTAVQAYKDRFGVTS 400
401 ETRVLQFSSYTFDISIADTFTALFYGGTLCIPSEEDRMSNLQDYMVSVRP 450
451 NWAVLTPTVSRFLDPGVVKDFISTLIFTGEASREADTVPWIEAGVNLYNV 500
501 YGPAENTLITTATRIRKGKSSNIGYGVNTRTWVTDVSGACLVPVGSIGEL 550
551 LIESGHLADKYLNRPDRTEAAFLSDLPWIPNYEGDSVRRGRRFYRTGDLV 600
601 RYCDDGSLICVGRSDTQIKLAGQRVELGDVEAHLQSDPTTSQAAVVFPRS 650
651 GPLEARLIALLVTGNKDGTPHNQQSLPKPAFAQCPPDLVKYATSSLQQRL 700
701 PSYMVPSVWLGIDFLPMSVSGKLDRAVLQDQLESLSPSDYAEILGTTGLE 750
751 VDPGGAASSVASDSDLRDMNDDSLLLTACSRVLNLPAGKISYSQSFIHAG 800
801 GDSITAMQVSSWMKRFTGKRIGVKDLLVSPSISTAASCIKSAQDGSRNFV 850
851 AVRPGQRIPVSPIQKLFFQTAEASKSWNHYHQSFLFRIDQPIKPQTIEDA 900
901 ISLVMQRHPMLQARFERTEEGDWYQYIPIDVERRASVEVIGSLSTDDREA 950
951 AMLRARQSIDLTEGPLIRCQLFNNNVDEASRLFFVVIHHAVVDLVSWRII 1000
1001 MEELEAHLATDSTPDRGEAYQESVPFLAWCQVQAEAVKDIPVDRTVPLIP 1050
1051 KIPTADFGYWGLKHDENVYGNTVERKIPLGHSITEDLLYKCHDSLHTKTI 1100
1101 DVLLAAVLVSFRRSFLDRPVPAVFNEGHGREPGGEDAVDLSRTVGWFTTI 1150
1151 SPVYVPEVSPGDILDVVRRVKDYRWATPNNGFDYFSTKYLTQSGIKLFED 1200
1201 HLPAEILFNYEGRYQAMESEQTVLKPESWHAGEASKDQDPGLRRFCLFEI 1250
1251 STAVLPDGQLHLTCSWNKNMRHQGRIRLWLDTLLPAAIGEIVSSLALASP 1300
1301 QLTLSDVELLRLYDYSSLDILKKSILSIPAVQTLDDLEGVYPGSPMQDAL 1350
1351 FLSQSKSQDGAYEVDFTWRVATSLQNSQPAVDIGCLVEAWKDTVALHAAL 1400
1401 RTVILESSLPATGILHQVVLRSHDPDIVILDVRDVTAAITILDSYPPPTE 1450
1451 EGIALIKRPPHRLLICTTIEGSVLIKFQVNHLVFDGMSTDKIIQDLSKAY 1500
1501 TCRHSNKLPDHSESKLHDGTYGNRPTKPPLAEFIRYIRDPQRKQDSINYW 1550
1551 KNALRGATTCSFPPLFDQITSEKAMPRQSWASVPIPLCVDSKELSKTLAN 1600
1601 LGITMSTMFQTVWAIVLRIYSQNGQSVFGYLTSGRDAPVDGIDSAVGNFI 1650
1651 AMLVCFFDFDDDGVHTVADMARKIHNASANSISHQACSLAEIQDALGLST 1700
1701 STPLFNTAFTYLPKRPTNVKAGEPEHHLCFEELSMSDPTEFDLTLFVEPT 1750
1751 QESNEVSAHLDFKLSYISQAYATSIASTVAHILSELVHDPYRALNTLPIV 1800
1801 SEHDTAIIRSWNDHLFPPATECIHETFSRKVVEHPQREAICSWDGSLTYA 1850
1851 ELSDLSQRLSIHLVSLGIKVGTKIPICFEKSMWTIVTILAVVQAGGVFVL 1900
1901 LEPGHPESRLSGIIKQVQAELLLCSPATSRMGALQNISTQMGTEFKIVEL 1950
1951 EPEFIRSLPLPPKPNHQPMVGLNDDLYVVFTSGSTGVPKGAVATHQAYAT 2000
2001 GIYEHAVACGMTSLGAPPRSLQFASYSFDASIGDIFTTLAVGGCLCIPRE 2050
2051 EDRNPAGITTFINRYGVTWAGITPSLALHLDPDAVPTLKALCVAGEPLSM 2100
2101 SVVTVWSKRLNLINMYGPTEATVACIANQVTCTTTTVSDIGRGYRATTWV 2150
2151 VQPDNHNSLVPIGAVGELIIEGSILCRGYLNDPERTAEVFIRSPSWLHDL 2200
2201 RPNSTLYKTGDLVRYSADGKIIFIGRKDTQVKMNGQRFELGEVEHALQLQ 2250
2251 LDPSDGPIIVDLLKRTQSGEPDLLIAFLFVGRANTGTGNSDEIFIATSTS 2300
2301 SLSEFSTVIKKLQDAQRAMEVLPLFMVPQAYIPIEGGIPLTAAGKIDRRM 2350
2351 LRKLCEPFNRNDLISFTSKALSTSVKDAETTDTVEDRLARIWEKVLGVKG 2400
2401 VGRESDFFSSGGNSMAAIALRAEAQRSGFTLFVADIFTNPRLADMAKLFS 2450
2451 HGQSVSPSSSTLRTKVPISSLQKRSSGLQTAAPVSNGSPVRRCQKENIID 2500
2501 CPVAFEYEEGPSDTQLKEASRICGISSRSIEDVFPCTPMQEALVALSLIP 2550
2551 GAQASYALHAAFELRPGLDRNRFRSAWESTVKAQPILRSRIISGSNGSSV 2600
2601 VVTSATDSIPQLDVSGLDTFLEQQLQVGFAPGAPLFRLAFVYSKADDCDY 2650
2651 FVISAHHAIYDGWSLNLIWSQVLALYTNGELPPPGPSFKHFARNLNLVQS 2700
2701 KLDSEDFWRKLLVKPDQESFRFPDVPVGHKPATRCTTNFHFPFSMQSKIG 2750
2751 TTANTCINAAWAITLAQYSSNKTVNFGVTLWGRDFPMIDIEHMTGPTIVT 2800
2801 VPRQVNVIPESSVAEFLQDLQKSLAVVLPHQHLGLHRIQALGPIARQACD 2850
2851 FSTLLVVNHGSSISWSELEAADIVPVPLRSSDLYAYPMVVEVENASSDTL 2900
2901 DIRVHSDPDCIEVQLLERLMEQFGHNLQTLCRAASFDPGKRIAELMDDTA 2950
2951 TTHLRTLFSWNSRVKDSPDVAAIAVHKLLEETAQSQPAESAIVAHDGQLS 3000
3001 YMQMDRCADVLARQIRKTNMISAQSPFVCIHLLRSATAVVSMLAVLKAGG 3050
3051 AFMPVDISQPRSRLQNLIEESGAKLVLTLPESANALATLSGLTKVIPVSL 3100
3101 SELVQQITDNTTKKDEYCKSGDTDPSSPAYLLYTSGTSGKPKGVVMEHRA 3150
3151 WSLGFTCHAEYMGFNSCTRILQFSSLMFDLSILEIWAVLYAGGCLFIPSD 3200
3201 KERVNNLQDFTRINDINTVFLTPSIGKLLNPKDLPNISFAGFIGEPMTRS 3250
3251 LIDAWTLPGRRLVNSYGPTEACVLVTAREISPTAPHDKPSSNIGHALGAN 3300
3301 IWVVEPQRTALVPIGAVGELCIEAPSLARCYLANPERTEYSFPSTVLDNW 3350
3351 QTKKGTRVYRTGDLVRYASDGTLDFLGRKDGQIKLRGQRIELGEIEHHIR 3400
3401 RLMSDDPRFHEASVQLYNPATDPDRDATVDVQMREPYLAGLLVLDLVFTD 3450
3451 EVMGIPCTSLTSANTSENLQTLVTELKKSLRGVLPHYMVPLHFVAVSRLP 3500
3501 TGSSGKLDHAFVRACLRELTAPLDGNFPKVEQVLTTNESVLRQWWGTVLA 3550
3551 MDPHSIQRGDDFFSLGGSSISAMRLVGLARSSGHKLQHEDIFMCPRLADM 3600
3601 AGQISFVQEASVSPTTSPTIKFDLLDDCEVDEVIDHILPQLDMNKELIED 3650
3651 VYPCTPLQESLMAATARHGEAYTMIQSITVLASQLAQLKKAMDVVFRDFE 3700
3701 VLRTRIALGPSQQALQVVVKHEELSWESFPSIQSFKDHFYRSLGYGKPLA 3750
3751 RLAVITQALDTKQPISHGTREARTKNSQDTVMVVVGAHHSIYDAHVLSMI 3800
3801 WRRLYREFIGSQADGILEAETSRSEGVVPFKSYVEKLLRGKDNDESLLFW 3850
3851 KEKLRGVSSSQFPPASWPRVLEHQPSATQTLITKVSLPTSSRKKLGATVA 3900
3901 TVAYAAWALTIAHYTADPDVVFGATLSGRETMAGSISHPESIAGPTIITV 3950
3951 PLRIIIDFQTVVSDFLSTLQKDIVRAAYFGQMMGLNSIAHIDNDCRDACG 4000
4001 FKSIIVVQVPDEGENHDGRAANPFQMSLESIGHFPAPLVVEVEQSESTDV 4050
4051 LIRMAYDPVLVPEKLAHFISDTFTTTMSNLSAANPKAKVESIPALSEAHL 4100
4101 AELDVTCPEWILGKAKDEKIRTESHQCLQDLVCRRAQQSPNSQAIDSWDG 4150
4151 SISYHELDGLSSILAEHLSQLGVRPEAPVCLLFEKSKWAVVAMIGIIKAG 4200
4201 GCFVPLDPSYPHERLEHIISETGSSVIVTSAAYSKLCLSLSVRGIVCDGS 4250
4251 VFSSTKKPLPSTADSPPSFSVRPNQAAYILFTSGSTGKPKGVVMEHHSVC 4300
4301 SALIALGKRMGLGPQSRVLQFNSYWFDVMLLDIFGTLVYGGCLCIPKEEQ 4350
4351 RMSNLSGWVQKFKVNTMLLSTSVSRLMQPADTPSLETLCLTGEAVLQSDV 4400
4401 DRWAPKLHLIAGYGPTETCIMSVSGELTPSSPANLIGKPVSCQAWVINPL 4450
4451 KETELAPYGATGELYIQGPTVARGYLHDDVLTSKAFIVDPQWLTGYKTNE 4500
4501 NQWSRRAYKTGDLVFWGPQSNLYYVRRKDSSQVKIRGQRVELAEIEEVIR 4550
4551 QHIPPDVTVCVDLLSSDDQNTRIILGAVLGIGDRALGGPEDLEVIGYMDD 4600
4601 LKSHIIPALEASLPHHMIPEAYVPFVQLPTLGSGKLDRKTVRRVAGPLAF 4650
4651 SLPQASARHPNQPTVTHTQKLLRQLWCKILPQLDESAVNKQDNFLGIGGD 4700
4701 SIAAIKLVALLRQHGISLAVAEIFTRPTLEAMSSLIDEHNFVVSHAGILS 4750
4751 DVTRNTSGVMRQTTNLIAGRHSMAVEKSRECDNSTLPCTEYQQMFLAGTE 4800
4801 AFTGAHSAQFIFRLPEKIDLDRLQAAFDHCADWYPNLRTQIHKDADTGRL 4850
4851 LHDISPIGVKVPWSCHYSDDLNTVLSHDKKFPPGLDGPLHRVTIMRHRDP 4900
4901 TESMLVWTLNHAAYDAWSLRMMLEHITEAYANPDYEPSYSLGWTAFVLHT 4950
4951 ENTKEASRSFWSSYLSDVKPARLMFNYNLVSNPRQDRLYEARINIPKRVL 5000
5001 SQATAATVLLAGLTLLVARVCDTRDVILAHLLTGRTLPLAGIENCPGPTI 5050
5051 TKVPLRIPLMDQDLVTLELDSVAKKITAELMRVMPHEHSGLSAIREFIPQ 5100
5101 AEGTTTSSGKFHAGSVLGRLPLDLVIHPKGGLDLLGKHGLGLQNEGFRLV 5150
5151 APPSGGLSMECALVDDDDDKRSDTISVDVSVLWDQRAATQEDVIELVHSL 5200
5201 QGIFTKRNLAASICLMYK 5218
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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