 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UPN3 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MSSSDEETLSERSCRSERSCRSERSYRSERSGSLSPCPPGDTLPWNLPLH 50
51 EQKKRKSQDSVLDPAERAVVRVADERDRVQKKTFTKWVNKHLMKVRKHIN 100
101 DLYEDLRDGHNLISLLEVLSGIKLPREKGRMRFHRLQNVQIALDFLKQRQ 150
151 VKLVNIRNDDITDGNPKLTLGLIWTIILHFQISDIYISGESGDMSAKEKL 200
201 LLWTQKVTAGYTGIKCTNFSSCWSDGKMFNALIHRYRPDLVDMERVQIQS 250
251 NRENLEQAFEVAERLGVTRLLDAEDVDVPSPDEKSVITYVSSIYDAFPKV 300
301 PEGGEGISATEVDSRWQEYQSRVDSLIPWIKQHTILMSDKTFPQNPVELK 350
351 ALYNQYIHFKETEILAKEREKGRIEELYKLLEVWIEFGRIKLPQGYHPND 400
401 VEEEWGKLIIEMLEREKSLRPAVERLELLLQIANKIQNGALNCEEKLTLA 450
451 KNTLQADAAHLESGQPVQCESDVIMYIQECEGLIRQLQVDLQILRDENYY 500
501 QLEELAFRVMRLQDELVTLRLECTNLYRKGHFTSLELVPPSTLTTTHLKA 550
551 EPLTKATHSSSTSWFRKPMTRAELVAISSSEDEGNLRFVYELLSWVEEMQ 600
601 MKLERAEWGNDLPSVELQLETQQHIHTSVEELGSSVKEARLYEGKMSQNF 650
651 HTSYAETLGKLETQYCKLKETSSFRMRHLQSLHKFVSRATAELIWLNEKE 700
701 EEELAYDWSDNNSNISAKRNYFSELTMELEEKQDVFRSLQDTAELLSLEN 750
751 HPAKQTVEAYSAAVQSQLQWMKQLCLCVEQHVKENTAYFQFFSDARELES 800
801 FLRNLQDSIKRKYSCDHNTSLSRLEDLLQDSMDEKEQLIQSKSSVASLVG 850
851 RSKTIVQLKPRSPDHVLKNTISVKAVCDYRQIEITICKNDECVLEDNSQR 900
901 TKWKVISPTGNEAMVPSVCFLIPPPNKDAIEMASRVEQSYQKVMALWHQL 950
951 HVNTKSLISWNYLRKDLDLVQTWNLEKLRSSAPGECHQIMKNLQAHYEDF 1000
1001 LQDSRDSVLFSVADRLRLEEEVEACKARFQHLMKSMENEDKEETVAKMYI 1050
1051 SELKNIRLRLEEYEQRVVKRIQSLASSRTDRDAWQDNALRIAEQEHTQED 1100
1101 LQQLRSDLDAVSMKCDSFLHQSPSSSSVPTLRSELNLLVEKMDHVYGLST 1150
1151 VYLNKLKTVDVIVRSIQDAELLVKGYEIKLSQEEVVLADLSALEAHWSTL 1200
1201 RHWLSDVKDKNSVFSVLDEEIAKAKVVAEQMSRLTPERNLDLERYQEKGS 1250
1251 QLQERWHRVIAQLEIRQSELESIQEVLGDYRACHGTLIKWIEETTAQQEM 1300
1301 MKPGQAEDSRVLSEQLSQQTALFAEIERNQTKLDQCQKFSQQYSTIVKDY 1350
1351 ELQLMTYKAFVESQQKSPGKRRRMLSSSDAITQEFMDLRTRYTALVTLTT 1400
1401 QHVKYISDALRRLEEEEKVVEEEKQEHVEKVKELLGWVSTLARNTQGKAT 1450
1451 SSETKESTDIEKAILEQQVLSEELTTKKEQVSEAIKTSQIFLAKHGHKLS 1500
1501 EKEKKQISEQLNALNKAYHDLCDGSANQLQQLQSQLAHQTEQKECRAVAG 1550
1551 VIDLGTVEIFPIFKAMQKGLLDQDTGLVLLESQVIMSGLIAPETGENLSL 1600
1601 EEGIARNLINPQMYQQLRELQDALALISRLTESRGPLSVVEAIEKRIISE 1650
1651 TVGLKILEAHLATGGFSLSPSENCINLEEAFHQGLISAWLHSVLESYLRT 1700
1701 SKNLIDPNTAEKIGLLDLMQRCIVHQESGFKLLPVKQLAGGMVSLKSGRK 1750
1751 VSIFRAVQEGLIDRQVTVRLLEAQLFAGGIVDPRTGHRLTVEEAVRHNLI 1800
1801 DQDMACAILIRQLQTGGIIDTVTGQRLTIDEAVSNDLVAAKIALVILESL 1850
1851 WSFMGLLWPESGEILPITDALEQGIVSTELAHKILSNRQHIKALFLPATT 1900
1901 EILSWKKAIESGILDRDLANNLKSICIPDVMPHMQLADSAEQNINPGAAV 1950
1951 LPCSKSHPKATASQSENLLFQLMTHSYINVQNGQRLLLLDKELMETLTSR 2000
2001 DEYQTSPPKVVEIGHQRQKTPEGLQESANVKISGTFSSGWTVRLPEFQFS 2050
2051 SQNKEYPDREDCTTEKGKKTTVETEDSSVENPEQDLFVEQKERNPNIDAL 2100
2101 KVINKVKLEVQRQLIGTQREDQTAVSVRENASRGHLLTIPPAEAEGVPLV 2150
2151 VDKDVFSVETPKKEHQPLRNTSFTCQNEQAHTLETEYIHDETGGSHIKPQ 2200
2201 SKKLQVQVKKTLGIKLELKSETDGNVHPLDKKEMLKKTFLAKDDHKESQE 2250
2251 AQNIAGGSMMMSEKTDEEDSGREIFLSCSHPLELLEEATLNVLSAQLLDG 2300
2301 GIFHEQTGQKLLLNEAISRGIVPSHTAVKLMEKLNMFQGFFDSQTCESLT 2350
2351 TEEVINEGLMDEKLLHNVLMADKAISGVLDPRTQTLCSVKDAVTVGLLDK 2400
2401 ETATRILERQVVTGGIIDLKRGKKVSVTLASTLGLVDVADQPELINLEKA 2450
2451 SKGRDAEKTVRERLISLQMETTGLIDPDSKAPLTVVQSIDRGLLEREEAV 2500
2501 RLLTKQVVDGGIIHHISGMRLSVDNAFRHGLIGEDLAEKLKRVENLNIHQ 2550
2551 IFNPETKENISLPKAIKLDLITSDLKREIQEVQAFTGNFVDLISGQRLTL 2600
2601 AEAKKEGLLTNEAVLSPGMMHGIVDPENCRIVPYSELVKKCKIDIESGQR 2650
2651 YLEVIPFSDIKDGVSDKVLTLSQAIQLGKVDFASTLKVLEAQANTGGIID 2700
2701 TATGKRLTLASALEEKLVDENMVRIIASHQVLNGGIVDIFSDQRVTLVEA 2750
2751 IEKRLISPELANMIQIDSSEFSDHRAQIEKQEGIEVCALQNEFLGKDMLI 2800
2801 ACNQTAEMSCNKVEESERLFQVENQSAQEKVKVRVSDGEQAKKSREISLK 2850
2851 EFGCKDQRKPRMSSDAKEFISIINPHNLKGKSLGQVSLTHPYSECDFKLK 2900
2901 EVARNNMGNDTNEEQEKAVTKIEIISHMKQSTSCLDSEEIRENQGEVILE 2950
2951 VQETYCETSGKLPSEQVLQQPMNARVKSKREKREVIVEESIRTCKPAFLS 3000
3001 EEKLYQETAIRDEHDSHIKSQPREMTSSEKGKEADTEMGFSITFKIEESS 3050
3051 SQVVPQGISVKHLDALTLFSSKQANEGKVNNLSLCLTLKPEENLSREIAC 3100
3101 GAQSEPFPCMTPRPEGLHYQESDGKAQVTGPSQISKTDKSFQGTTRQETN 3150
3151 YQDSWVTSKTKETKHQISSSNECKEKSYQEVSFDPARGLKLEEITVSRPD 3200
3201 SKEVRYLEFSDRKDLHHQGSKSDDKLCGTLKSEIATQELTGEKFLEMANP 3250
3251 NVAGLEAGSIEDIVTQRGSRVLGSFLPEKLFKGVSQKENTGQQNAIISPT 3300
3301 VLETSEEKTVSLTVCSAVKTEKTPQEKLRESPGSEQTPFMTAPEGKGNGG 3350
3351 VNPEPFRATQNVFTRQLCLEHDEKLVSYLSLLRNIEMRTKQIQPLELNLA 3400
3401 ELQDLLCQAKVLERELKDLTTLVSQELECVNQIIISQPQEVPAQLLKALE 3450
3451 KDAKNLQKSLSSVSDTWNSRLLHFQNAVEIEKTKVLNQHTQLEGRLQDLR 3500
3501 AWVGNKNLILNSKGSNSEIDVDSLNLCLQQYEDLKQPMAERKAQLDALAF 3550
3551 DIQFFISEHAQDLSPQQNRQMLRLLNELQRSFQDILEQTAAQVDALQGHL 3600
3601 QQMEQEALVKTLQKQQNTCHQQLEDLCSWVGQAERALAGHQGRTTQQDLS 3650
3651 ALQKNQSDLKDLQDDIQNRATSFATVVKDIEGFMEENQTKLSPRELTALR 3700
3701 EKLHQAKEQYEALQEETRVAQKELEEAVTSALQQETEKSKAAKELAENKK 3750
3751 KIDALLDWVTSVGSSGGQLLTNLPGMEQLSGASLEKGALDTTDGYMGVNQ 3800
3801 APEKLDKQCEMMKARHQELLSQQQNFILATQSAQAFLDQHGHNLTPEEQQ 3850
3851 MLQQKLGELKEQYSTSLAQSEAELKQVQTLQDELQKFLQDHKEFESWLER 3900
3901 SEKELENMHKGGSSPETLPSLLKRQGSFSEDVISHKGDLRFVTISGQKVL 3950
3951 DMENSFKEGKEPSEIGNLVKDKLKDATERYTALHSKCTRLGSHLNMLLGQ 4000
4001 YHQFQNSADSLQAWMQACEANVEKLLSDTVASDPGVLQEQLATTKQLQEE 4050
4051 LAEHQVPVEKLQKVARDIMEIEGEPAPDHRHVQETTDSILSHFQSLSYSL 4100
4101 AERSSLLQKAIAQSQSVQESLESLLQSIGEVEQNLEGKQVSSLSSGVIQE 4150
4151 ALATNMKLKQDIARQKSSLEATREMVTRFMETADSTTAAVLQGKLAEVSQ 4200
4201 RFEQLCLQQQEKESSLKKLLPQAEMFEHLSGKLQQFMENKSRMLASGNQP 4250
4251 DQDITHFFQQIQELNLEMEDQQENLDTLEHLVTELSSCGFALDLCQHQDR 4300
4301 VQNLRKDFTELQKTVKEREKDASSCQEQLDEFRKLVRTFQKWLKETEGSI 4350
4351 PPTETSMSAKELEKQIEHLKSLLDDWASKGTLVEEINCKGTSLENLIMEI 4400
4401 TAPDSQGKTGSILPSVGSSVGSVNGYHTCKDLTEIQCDMSDVNLKYEKLG 4450
4451 GVLHERQESLQAILNRMEEVHKEANSVLQWLESKEEVLKSMDAMSSPTKT 4500
4501 ETVKAQAESNKAFLAELEQNSPKIQKVKEALAGLLVTYPNSQEAENWKKI 4550
4551 QEELNSRWERATEVTVARQRQLEESASHLACFQAAESQLRPWLMEKELMM 4600
4601 GVLGPLSIDPNMLNAQKQQVQFMLKEFEARRQQHEQLNEAAQGILTGPGD 4650
4651 VSLSTSQVQKELQSINQKWVELTDKLNSRSSQIDQAIVKSTQYQELLQDL 4700
4701 SEKVRAVGQRLSVQSAISTQPEAVKQQLEETSEIRSDLEQLDHEVKEAQT 4750
4751 LCDELSVLIGEQYLKDELKKRLETVALPLQGLEDLAADRINRLQAALAST 4800
4801 QQFQQMFDELRTWLDDKQSQQAKNCPISAKLERLQSQLQENEEFQKSLNQ 4850
4851 HSGSYEVIVAEGESLLLSVPPGEEKRTLQNQLVELKNHWEELSKKTADRQ 4900
4901 SRLKDCMQKAQKYQWHVEDLVPWIEDCKAKMSELRVTLDPVQLESSLLRS 4950
4951 KAMLNEVEKRRSLLEILNSAADILINSSEADEDGIRDEKAGINQNMDAVT 5000
5001 EELQAKTGSLEEMTQRLREFQESFKNIEKKVEGAKHQLEIFDALGSQACS 5050
5051 NKNLEKLRAQQEVLQALEPQVDYLRNFTQGLVEDAPDGSDASQLLHQAEV 5100
5101 AQQEFLEVKQRVNSGCVMMENKLEGIGQFHCRVREMFSQLADLDDELDGM 5150
5151 GAIGRDTDSLQSQIEDVRLFLNKIHVLKLDIEASEAECRHMLEEEGTLDL 5200
5201 LGLKRELEALNKQCGKLTERGKARQEQLELTLGRVEDFYRKLKGLNDATT 5250
5251 AAEEAEALQWVVGTEVEIINQQLADFKMFQKEQVDPLQMKLQQVNGLGQG 5300
5301 LIQSAGKDCDVQGLEHDMEEINARWNTLNKKVAQRIAQLQEALLHCGKFQ 5350
5351 DALEPLLSWLADTEELIANQKPPSAEYKVVKAQIQEQKLLQRLLDDRKAT 5400
5401 VDMLQAEGGRIAQSAELADREKITGQLESLESRWTELLSKAAARQKQLED 5450
5451 ILVLAKQFHETAEPISDFLSVTEKKLANSEPVGTQTAKIQQQIIRHKALN 5500
5501 EEIVNRKKNVDQAIKNGQALLKQTTGEEVLLIQEKLDGIKTRYADITVTS 5550
5551 SKALRTLEQARQLATKFQSTYEELTGWLREVEEELATSGGQSPTGEQIPQ 5600
5601 FQQRQKELKKEVMEHRLVLDTVNEVSRALLELVPWRAREGLDKLVSDANE 5650
5651 QYKLVSDTIGQRVDEIDAAIQRSQQYEQAADAELAWVAETKRKLMALGPI 5700
5701 RLEQDQTTAQLQVQKAFSIDIIRHKDSMDELFSHRSEIFGTCGEEQKTVL 5750
5751 QEKTESLIQQYEAISLLNSERYARLERAQVLVNQFWETYEELSPWIEETR 5800
5801 ALIAQLPSPAIDHEQLRQQQEEMRQLRESIAEHKPHIDKLLKIGPQLKEL 5850
5851 NPEEGEMVEEKYQKAENMYAQIKEEVRQRALALDEAVSQSTQITEFHDKI 5900
5901 EPMLETLENLSSRLRMPPLIPAEVDKIRECISDNKSATVELEKLQPSFEA 5950
5951 LKRRGEELIGRSQGADKDLAAKEIQDKLDQMVFFWEDIKARAEEREIKFL 6000
6001 DVLELAEKFWYDMAALLTTIKDTQDIVHDLESPGIDPSIIKQQVEAAETI 6050
6051 KEETDGLHEELEFIRILGADLIFACGETEKPEVRKSIDEMNNAWENLNKT 6100
6101 WKERLEKLEDAMQAAVQYQDTLQAMFDWLDNTVIKLCTMPPVGTDLNTVK 6150
6151 DQLNEMKEFKVEVYQQQIEMEKLNHQGELMLKKATDETDRDIIREPLTEL 6200
6201 KHLWENLGEKIAHRQHKLEGALLALGQFQHALEELMSWLTHTEELLDAQR 6250
6251 PISGDPKVIEVELAKHHVLKNDVLAHQATVETVNKAGNELLESSAGDDAS 6300
6301 SLRSRLEAMNQCWESVLQKTEEREQQLQSTLQQAQGFHSEIEDFLLELTR 6350
6351 MESQLSASKPTGGLPETAREQLDTHMELYSQLKAKEETYNQLLDKGRLML 6400
6401 LSRDDSGSGSKTEQSVALLEQKWHVVSSKMEERKSKLEEALNLATEFQNS 6450
6451 LQEFINWLTLAEQSLNIASPPSLILNTVLSQIEEHKVFANEVNAHRDQII 6500
6501 ELDQTGNQLKFLSQKQDVVLIKNLLVSVQSRWEKVVQRSIERGRSLDDAR 6550
6551 KRAKQFHEAWKKLIDWLEDAESHLDSELEISNDPDKIKLQLSKHKEFQKT 6600
6601 LGGKQPVYDTTIRTGRALKEKTLLPEDSQKLDNFLGEVRDKWDTVCGKSV 6650
6651 ERQHKLEEALLFSGQFMDALQALVDWLYKVEPQLAEDQPVHGDLDLVMNL 6700
6701 MDAHKVFQKELGKRTGTVQVLKRSGRELIENSRDDTTWVKGQLQELSTRW 6750
6751 DTVCKLSVSKQSRLEQALKQAEVFRDTVHMLLEWLSEAEQTLRFRGALPD 6800
6801 DTEALQSLIDTHKEFMKKVEEKRVDVNSAVAMGEVILAVCHPDCITTIKH 6850
6851 WITIIRARFEEVLTWAKQHQQRLETALSELVANAELLEELLAWIQWAETT 6900
6901 LIQRDQEPIPQNIDRVKALIAEHQTFMEEMTRKQPDVDRVTKTYKRKNIE 6950
6951 PTHAPFIEKSRSGGRKSLSQPTPPPMPILSQSEAKNPRINQLSARWQQVW 7000
7001 LLALERQRKLNDALDRLEELKEFANFDFDVWRKKYMRWMNHKKSRVMDFF 7050
7051 RRIDKDQDGKITRQEFIDGILASKFPTTKLEMTAVADIFDRDGDGYIDYY 7100
7101 EFVAALHPNKDAYRPTTDADKIEDEVTRQVAQCKCAKRFQVEQIGENKYR 7150
7151 FGDSQQLRLVRILRSTVMVRVGGGWMALDEFLVKNDPCRARGRTNIELRE 7200
7201 KFILPEGASQGMTPFRSRGRRSKPSSRAASPTRSSSSASQSNHSCTSMPS 7250
7251 SPATPASGTKVIPSSGSKLKRPTPTFHSSRTSLAGDTSNSSSPASTGAKT 7300
7301 NRADPKKSASRPGSRAGSRAGSRASSRRGSDASDFDLLETQSACSDTSES 7350
7351 SAAGGQGNSRRGLNKPSKIPTMSKKTTTASPRTPGPKR 7388
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.