 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P27742 from www.uniprot.org...
The NucPred score for your sequence is 0.72 (see score help below)
1 MSPPGLLSEDGPGYSGGYADPTVPKVNWKQSNGKSAGGNGDVDAGNGNID 50
51 PSKSGVGVQVCFAGGLEGWKAGISKITERCDLSSIATNSTKYQLAVTGFS 100
101 DGPDDYNEYSVPFPSEVLVAMEEMCLARDISMRSVIQFAVHYVLKGFGGG 150
151 SHTVAASIDVGDDPNNIATSYTITPSIVCHESRQGQTVMQEIQSMEKLNQ 200
201 LRKQEMHPGEAGLSLIRMGLFDILVIFADANKCEGLIAGLPLAVMVCEGG 250
251 GRLQVRIHFSGSLFRQKTLVDIAEALNVLFAKAASGGATPVRDLELLSAE 300
301 QKQQLEEWNKTDGEYPECKRLNHLIEEATQLHEDKVAIVYKRRQLTYGEL 350
351 NAQANCFAHYLRSIGILPEQLVALFLEKSENLIVTILGIWKSGAAYVPID 400
401 PTYPDERVRFVLEDTQAKVIIASNHLAERLQSEVISDRELSIIRLEHCLS 450
451 AIDQQPSTFPRANLRDPSLTSKQLAYVTYTSGTTGFPKGILKQHTNVVNS 500
501 ITDLSARYGVTGDHHEAILLFSAYVFEPFVRQMLMALVNGHLLAMVDDAE 550
551 KYDAEKLIPFIREHKITYLNGTASVLQEYDFSSCPSLKRLILVGENLTES 600
601 RYLALRRHFKNCILNEYGFTESAFVTALNVFEPGSARNNTSLGRPVRNVK 650
651 CYILNKSLKRVPIGATGELHIGGLGISKGYLNRPDLTPQRFIPNPFQTDH 700
701 EKELGLNQLMYKTGDLARWLPNGEIEYLGRADFQIKLRGIRIEPGEIEST 750
751 LAGYPGVRTSLVVSKRLRHGEKETTNEHLVGYYVGDNTSVSETALLQFLE 800
801 LKLPRYMIPTRLVRVSQIPVTVNGKADLRALPSVDLIQPKVSSCELTDEV 850
851 EIALGKIWADVLGAHHLSISRKDNFFRLGGHSITCIQLIARIRQQLGVII 900
901 SIEDVFSSRTLERMAELLRSKESNGTPDERARPQLKTVAGEVANANVYLA 950
951 NSLQQGFVYQFLKNMGRSEAYVMQSVLRYDVNINPDLFKKAWKQVQHMLP 1000
1001 TLRLRFQWGQDVLQVIDEDQPLNWWFLHLADDSALPEEQKLLELQRRDLA 1050
1051 EPYDLAAGSLFRIYLIEHSSTRFSCLFSCHHAILDGWSLPLLFRKTHGTY 1100
1101 LHLLHGHSLRTLEDPYRQSQQYLQDHREDHLRYWAGIVNQIEERCDMNAL 1150
1151 LNERSRYKIQLADYDKVEDQQQLTLTVPDASWLSKLRQTCSAQGITLHSI 1200
1201 LQFVWHAVLHAYGGGTHTVTGTTISGRNLPVSGIERSVGLYINTLPLVIN 1250
1251 QLAYKNKTVLEAIRDVQAIVNGMNSRGNVELGRLQKNELKHGLFDSLFVL 1300
1301 ENYPILDKSEEMRQKSELKYTIEGNIEKLDYPLAVIAREVDLTGGFTFTI 1350
1351 CYARELFDEIVISELLQMVRDTLLQVAKHLDDPVRSLEYLSSAQMAQLDA 1400
1401 WNATDAEFPDTTLHAMFEKEAAQKPDKVAVVYEQRSLTYRQLNERANRMA 1450
1451 HQLKSDISPKPNSIIALVVDKSEHMIATILAVWKTGGAYVPIDPEYPDDR 1500
1501 IRYILEDTSAIAVISDACYLSRIQELAGESVRLYRSDISTQTDGNWSVSN 1550
1551 PAPSSTSTDLAYIIYTSGTTGKPKGVMVEHHGVVNLQISLSKTFGLRDTD 1600
1601 DEVILSFSNYVFDHFVEQMTDAILNGQTLVMLNDAMRSDKERLYQYIETN 1650
1651 RVTYLSGTPSVISMYEFSRFKDHLRRVDCVGEAFSQPVFDQIRDTFQGLI 1700
1701 INGYGPTEISITTHKRLYPFPERRTDKSIGQQIGNSTSYVLNADMKRVPI 1750
1751 GAVGELYLGGEGVARGYHNRPEVTAERFLRNPFQTDSERQNGRNSRLYRT 1800
1801 GDLVRWIPGSNGEIEYLGRNDFQVKIRGLRIELGEIEAVMSSHPDIKQSV 1850
1851 VIAKSGKEGDQKFLVGYFVASSPLSPGAIRRFMQSRLPGYMIPSSFIPIS 1900
1901 SLPVTPSGKLDTKALPTAEEKGAMNVLAPRNEIESILCGIWAGLLDISAQ 1950
1951 TIGSDSDFFTLGGDSLKSTKLSFKIHEVFGRTISVSALFRHRTIESLAHL 2000
2001 IMNNVGDIQEITPVDYDNRRKIAVSPAQERLLFIHELEGGGNAYNIDAAF 2050
2051 ELPPYIDQSRVEEALYTILSRHEALRTFLLRDQATGTFYQKILTTDEAKC 2100
2101 MLIIEKSAVSTIDQIDSIVGRLSQHIFRLDSELPWLAHIVTHKTGNLYLT 2150
2151 LSFHHTCFDAWSLKIFERELRVFCASNEKGGNMPILPMPQVQYKEYAEHH 2200
2201 RRRLGKNQIQKLSDFWLQRLDGLEPLQLLPDYPRPAQFNYDGGDLSVILD 2250
2251 GVVLETLRGIAKDHGVTLYAVLLAVYCLMLSTYTHQVDIAVGVPISHRTH 2300
2301 PLFQSIVGFFVNMVVVRVDVKDFAVHDLIRRVMKALVDAQLHQDMPFQDV 2350
2351 TKLLRVDNDASRHPLVQTVFNFESDMDKEFETTPSIQDTATIAPYQSVQR 2400
2401 IKSVAKFDLNATATESGSALKINFNYATSLFRKETIQGFLETYRHLLLQL 2450
2451 SYLGSQGLKEDTKLLLVRPEEMSGPHLPLAGLSNGAETLEAISLSRAFEF 2500
2501 EAFRVPDRAAVVQGDKSLSYTELNKRANQLARYIQSVAHLRPDDKVLLIL 2550
2551 DKSIDMIICILAIWKTGSAYVPLDPSYPKERVQCISEVVQAKILITESRY 2600
2601 ASAWGSQTSTILAIDSPKVSNMVNNQATHNLPNIAGIKNLAYIIFTSGTS 2650
2651 GKPKGVLVEQGGVLHLRDALRKRYFGIECNEYHAVLFLSNYVFDFSIEQL 2700
2701 VLSIMSGHKLIIPEGEFVADDEFYITANGQRLSYLSGTPSLLQQIDLARL 2750
2751 NHLQVVTAAGEQLHAAQFNKLRSGFRGPIYNAYGITETTVYNIVSEFSAQ 2800
2801 SQFENALRELLPGTRAYLLNHATQPVPMNAVGELYLAGDCVARGYLNQPV 2850
2851 LTGDRFIQNPFQTEQDIASGSYPRLYRTGDLFRCRLDRQHQPYLEYLGRA 2900
2901 DLQVKIRGYRIEPSEVQNVLASCPGVRECAVVAKYENTDAYSRIAKFLVG 2950
2951 YYTPDTETVSDSSILAHMKSKLPAYMVPKYLCRLEGGLPVTINGKLDVRK 3000
3001 LPDIGNPQHQISYNPPRDVLEADLCRLWASALGTERCGIDDDLFRLGGDS 3050
3051 ITALHLAAQIHHQIGRKVTVRDIFDHPTIRGIHDNVMVKLVPHNVPQFQA 3100
3101 EQQTVLGDAPLLPIQTWFLSKSLQHPSHWNHTFYLRTPELDVTTLSTAVA 3150
3151 ELQLYHDAFRMRLRQIDGRTVQCFADDISPVQLRVLNVKDVDGSAAIDQQ 3200
3201 LQKYQSDFDLEKGPICAAAYLHGYEDRSARVWFSVHHIIIDIVSWQILAR 3250
3251 DLQILYEGGTLGRKSSSVRQWAEALQSYQGSASERAYWEGLLAQTAANIS 3300
3301 ALPPVTGTRTRLARTWSDDRTVILLNEASNQNASIQDLLLAAVGLALQQV 3350
3351 TPGSPSMITLEGHGREEIVDPTLDLSRTLGWFTSMYPFEIPPLNVETLSQ 3400
3401 GIASLRECLRQVPARGIGFGSLYGYCKHQMPQVTFNYLGQLTSKQSITDQ 3450
3451 WALAVGDGEMQYGLTTSPADRDQSSFAVDITASCVNGALSVEMNSAWSLE 3500
3501 KSMRFISRIEEVLNMILSGTLAQQATPVLTPQVFNEEMYTPYFEFSKTPR 3550
3551 RGPILFLLPPGEGGAESYFNNIVKHLPTTNMVVFNNYYLHSKSLNTFEKL 3600
3601 AEMYLGHIRQIQPDGPYHFIGWSFGGTIAMEISRQLVGLGSTIGLLGIID 3650
3651 TYFNVPGATRAIGLGDTEVLDPIHHISQPEPADFQCLPASTDYIILFKAT 3700
3701 RVNDKFQSENQRRLYEYYDKTLLNDLDWLLPGASNIHLVRLEEDTHFSWA 3750
3751 TNPRQIAHVCSTIEKFLARY 3770
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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