 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P51593 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MKVDRTKLKKTPTEAPADCRALIDKLKVCNDEQLLLELQQIKTWNIGKCE 50
51 LYHWVDLLDRFDGILADAGQTVENMSWMLVCDRPEKEQLKMLLLAVLNFT 100
101 ALLIEYSFSRHLYSSIEHLTTLLASSDMQVVLAVLNLLYVFSKRSNYITR 150
151 LGSDKRTPLLTRLQHLAESWGGKENGFGLAECCRDLQMLKYPPSATTLHF 200
201 EFYADPGAEVKIEKRTTSNTLHYIHIEQLDKISESPSEIMESLTKMYSIP 250
251 KDKQMLLFTHIRLAHGFSNHRKRLQAVQARLHAISILVYSNALQESANSI 300
301 LYNGLIEELVDVLQITDKQLMEIKAASLRTLTSIVHLERTPKLSSIIDCT 350
351 GTASYHGFLPVLVRNCIQAMIDPSMDPYPHQFATALFSFLYHLASYDAGG 400
401 EALVSCGMMEALLKVIKFLGDEQDQITFVTRAVRVVDLITNLDMAAFQSH 450
451 SGLSIFIYRLEHEVDLCRKECPFVIKPKIQRPSTTQEGEEMETDMDVADV 500
501 TMESSPGSSISMEHRLDVELRASSSNSSTSISGPGPGPGPGPGPGPGPGP 550
551 GPGPGPGLGPSLGPGPGPGPRPGVQCIPQRAALLKSMLNFLKKAIQDPAF 600
601 SDGIRHVMDGSLPTSLKHIISNAEYYGPSLFLLATEVVTVFVFQEPSLLS 650
651 SLQDNGLTDVMLHALLIKDVPATREVLGSLPNVFSALCLNARGLQSFVQC 700
701 QPFERLFKVLLSPDYLPAMRRRRSSDPLGDTASNLGSAVDELMRHQPTLK 750
751 TDATTAIIKLLEEICNLGRDPKYICQKPSIQKADGTTSAPPPRSNHAAEE 800
801 ASSEDEEEEEVQAMQSFNSAQQNETEPNQQVVGTEERIPIPLMDYILNVM 850
851 KFVESILSNNTTDDHCQEFVNQKGLLPLVTILGLPNLPIDFPTSAACQAV 900
901 AGVCKSILTLSHEPKVLQEGLLQLDLILSSLEPLHRPIESPGGSVLLREL 950
951 ACAGNVADATLSAQATPLLHALTAAHAYIMMFVHTCRVGQSEIRSISVNQ 1000
1001 WGSQLGLSVLSKLSQLYCSLVWESTVLLSLCTPNSLPSGCEFGQADMQKL 1050
1051 VPKDEKAGTTQGGKRSDGEQDGTAGSMDASAQGLLEGIELDGDTLAPMET 1100
1101 DEPSSSDSKGKSKITPAMAARIKQIKPLLSASSRLGRALAELFGLLVKLC 1150
1151 VGSPVRQRRSHHAASTTTAPTPAARSTASALTKLLTKGLSWQPPPYTPTP 1200
1201 RFRLTFFICSVGFTSPMLFDERKYPYHLMLQKFLCSGGHNALFETFNWAL 1250
1251 SMGGKVPVSEGLEHSDLPDGTGEFLDAWLMLVEKMVNPTTVLESPHSLPA 1300
1301 KLPGGVQSFPQFSALRFLVVTQKAAFTCIKNLWNRKPLKVYGGRMAESML 1350
1351 AILCHILRGEPVIRERLSKEKEGSRGEEEAGQEEGGSRREPQVNQQQLQQ 1400
1401 LMDMGFTREHAMEALLNTSTMEQATEYLLTHPPPIIGGVVRDLSMSEEDQ 1450
1451 MMRAIAMSLGQDIPMDQRAESPEEVACRKEEEERKAREKQEEEEAKCLEK 1500
1501 FQDADPLEQDELHTFTDTMLPGCFHLLDELPDTVYRVCDLIMTAIKRNGA 1550
1551 DYRDMILKQVVNQVWEAADVLIKAALPLTTSDTKTVSEWISQMATLPQAS 1600
1601 NLATRILLLTLLFEELKLPCAWVVESSGILNVLIKLLEVVQPCLQAAKEQ 1650
1651 KEVQTPKWITPVLLLIDFYEKTAISSKRRAQMTKYLQSNSNNWRWFDDRS 1700
1701 GRWCSYSASNNSTIDSAWKSGETSVRFTAGRRRYTVQFTTMVQVNEETGN 1750
1751 RRPVMLTLLRVPRLSKNSKSSNGQELEKTLEESKETDIKRKENKGNDIPL 1800
1801 ALESTNTEKDASLEETKIGEILIQGLTEDMVTVLIRACVSMLGVPVDPDT 1850
1851 LHATLRLCLRLTRDHKYAMMFAELKSTRMILNLTQSSGFNGFTPLVTLLL 1900
1901 RHIIEDPCTLRHTMEKVVRSAATSGAGSTTSGVVSGSLGSREINYILRVL 1950
1951 GPAACRNPDIFTEVANCCIRIALPAPRGSGTASDDEFENLRIKGPNAVQL 2000
2001 VKTTPLKPSSLPVIPDTIKEVIYDMLNALAAYHAPEEADKSDPKPGGMTQ 2050
2051 EVGQLLQDMGDDVYQQYRSLTRQSSDFDTQSGFSLNSQVFAADGAPAETS 2100
2101 TTGTSQGEGASTPEETREGKKDKEGDRTSEEGKQKGKGSKPLMPTSTILR 2150
2151 LLAELVRSYVGIATLIANYSYTVGQSELIKEDCSVLAFVLDHLLPHTQNA 2200
2201 EDKDTPALARLFLASLAAAGSGTDAQVALVNEVKAALGRALAMAESTEKH 2250
2251 ARLQAVMCIISTIMESCPSTSSFYSSATAKTQHNGMNNIIRLFLKKGLVN 2300
2301 DLARVPHSLDLSSPNMANTVNAALKPLETLSRIVNQPSSLFGSKSASSKS 2350
2351 KSEQDAQGASQDSSSHQQDPGEPGEAEVQEEDHDVTQTEVADGDIMDGEA 2400
2401 ETDSVVIAGQPEVLSSQEMQVENELEDLIDELLERDGGSGNSTIIVSRSG 2450
2451 EDESQEDVLMDEAPSNLSQASTLQANREDSMNILDPEDEEEHTQEEDSSG 2500
2501 SNEDEDDSQDEEEEEEEDEEDDQEDDEGEEGDEDDDDDGSEMELDEDYPD 2550
2551 MNASPLVRFERFDREDDLIIEFDNMFSSATDIPPSPGNIPTTHPLMVRHA 2600
2601 DHSSLTLGSGSSTTRLTQGIGRSQRTLRQLTANTGHTIHVHYPGNRQPNP 2650
2651 PLILQRLLGPSAAADILQLSSSLPLQSRGRARLLVGNDDVHIIARSDDEL 2700
2701 LDDFFHDQSTATSQAGTLSSIPTALTRWTEECKVLDAESMHDCVSVVKVP 2750
2751 IVNHLEFLRDEELEERREKRRKQLAEEETKIIDKGKEDKENRDQSAQCTV 2800
2801 SKTNDSTEQNVSDGTPMPDSYPTTPSSTDAPTSESKETLGTLQPSQQQPT 2850
2851 LPPPPSLGEISQELQSPAEEVGNSTQLLMPIELEELGPTRPSGEAETTQM 2900
2901 ELSPAPTITSLSPERAEDSDALTAVSSQLEGSPMDTSSLASCTLEEAVGD 2950
2951 TPAAGSSEQPTAGSSTPGDAPSVGADVQGRPDVSRESTQPPEDSSPPASS 3000
3001 ESSSTRDSAVAISGADSRGILEEPLPSTSSEEEDPLAGISLPEGVDPSFL 3050
3051 AALPDDIRREVLQNQLGIRPPTRTAPSTNSSTPAVVGNPGVTEVSPEFLA 3100
3101 ALPPAIQEEVLAQQRAEQQRRELAQNASSDTPMDPVTFIQTLPSDLRRSV 3150
3151 LEDMEDSVLAVMPPDIAAEAQALRREQEARQRQLMHERLFGHSSTSALSA 3200
3201 ILRSPAFTSRLSGNRGVQYTRLAVQRGGTFQMGGSSSHNRPSGSNVDTLL 3250
3251 RLRGRLLLDHEALSCLLVLLFVDEPKLNTSRLHRVLRNLCYHAQTRHWVI 3300
3301 RSLLSILQRSSESELCIETPKLSTSEERGKKSSKSCASSSHENRPLDLLH 3350
3351 KMESKSSNQLSWLSVSMDAALGCRTNIFQIQRSGGRKHTEKHASSGSTVH 3400
3401 IHPQAAPVVCRHVLDTLIQLAKVFPSHFTQQRTKETNCESDRERGSKQAC 3450
3451 SPCSSQSSSSGICTDFWDLLVKLDNMNVSRKGKNSVKSVPVSAGGEGETS 3500
3501 LHSLEASPLGQLMNMLSHPVIRRSSLLTEKLLRLLSLISIALPENKVSEV 3550
3551 QTNSSNSGSSTAATSNTSTTTTTTTATAPTPTPPAATTPVTSAPALVAAT 3600
3601 AISTITVAASTTVTTPTTATTTVSTSTTKGNKSPAKVGEGGSSSVDFKMV 3650
3651 SSGLTENQLQLSVEVLTSHSCSEEGLEDAANVLLQLSRGDSGTRDTVLKL 3700
3701 LLNGARHLGYTLCKQIGTLLAELREYNLEQQRRAQCETLSPDGLPEEQPQ 3750
3751 TTKLKGKMQSRFDMAENVVIVASQKRPLGGRELQLPSMSMLTSKTSTQKF 3800
3801 FLRVLQVIIQLRDDTRRANKKAKQTGRLGSSGLGSASSIQAAVRQLEAEA 3850
3851 DAIIQMVREGQRARRQQQAATSESSNQSETSVRREESPMDVDQPSPSAQD 3900
3901 TQSIVVSDGTPQGEKEKEERPPELPLLSEQLSLDELWDMLGECLKELEES 3950
3951 HDQHAVLVLQPAVEAFFLVHATERESKPPVRDTRESQLAHIKDEPPPLSP 4000
4001 APLTPATPSSLDPFFSREPSSMHISSSLPPDTQKFLRFAETHRTVLNQIL 4050
4051 RQSTTHLADGPFAVLVDYIRVLDFDVKRKYFRQELERLDEGLRKEDMAVH 4100
4101 VRRDHVFEDSYRELHRKSPEEMKNRLYIVFEGEEGQDAGGLLREWYMIIS 4150
4151 REMFNPMYALFRTSPGDRVTYTINPSSHCNPNHLSYFKFVGRIVAKAVYD 4200
4201 NRLLECYFTRSFYKHILGKSVRYTDMESEDYHFYQGLVYLLENDVSTLGY 4250
4251 DLTFSTEVQEFGVCEVRDLKPNGANILVTEENKKEYVHLVCQMRMTGAIR 4300
4301 KQLAAFLEGFYEIIPKRLISIFTEQELELLISGLPTIDIDDLKSNTEYHK 4350
4351 YQSNSIQIQWFWRALRSFDQADRAKFLQFVTGTSKVPLQGFAALEGMNGI 4400
4401 QKFQIHRDDRSTDRLPSAHTCFNQLDLPAYESFEKLRHMLLLAIQECSEG 4450
4451 FGLA 4454
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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