 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9QXZ0 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 LLWTQKVTAGYTGVKCTNFSSCWSDGKMFNALIHRYRPDLVDMERVQVQS 250
251 NRENLEQAFEVAERLGVTRLLDAEDVDVPSPDEKSVITYVSSIYDAFPKV 300
301 PEGGEGISATEVDSRWQEYQSRVDSLIPWIRQHTILMSDKSFPQNPVELK 350
351 ALYNQYIHFKETEILAKEREKGRIKELYKLLEVWIEFGRIKLPQGYHPNH 400
401 VEEEWGKLIVEMLEREKSLRPAVERLELLLQIANKIQNGALNCEEKLTLA 450
451 KNTLQADAAHLESGQPVQCESDVIMYIQECEGLIRQLQVDLQILRDEKYY 500
501 QLEELAFRVMRLQDELVTLRLECTNLYRKGHFSSLELVPPSTLTTTHLKA 550
551 EPLNKTTHSSSTSWFRKPMTRTELVSISSSEDEGNLRFVYELLSWVEEMQ 600
601 MKLERAEWGNDLPSVELQLETQQHIHTSVEELGSSVKEARLYEGKMSQNF 650
651 HTSYVETLGKLETQYCKLKETSSFRMRHLQSLHKFVSRATAELIWLNGKE 700
701 EEELACDWSDSNPNISAKKTYFSELTMELEGKQDVFRSLQDTAEVLSLEN 750
751 HPAKQTVEAYSAAVQSQLQWMKQLCLCVEQHVKENAAYFQFFSDARDLES 800
801 FLRNLQDSIKRKYTCDRSTSLSRLEDLLQDSMDEKEQLIQSKSSVASLVG 850
851 RSKTIVQLKPRNPDHVLKSTLSVKAICDYRQIEITICKNDECVLEDNSQR 900
901 TKWKVISPTGNEAMVPSVCFLIPPPNKEAIEMASRVEQSYQKVMALWHQL 950
951 HINTKSLISWNYLRKDLDTVQTWSLEKLRSLAPGECHQVMKNLQAHYEDF 1000
1001 LQDSHDSALFSVADRLRIEEEVEACKAHFQHLMKSLENEDKEETLAKVYI 1050
1051 SELKNIRLLLEECEQRLLKQIQSPASSKTDRDARQDITLRIAEQEHTQED 1100
1101 LQHLRSDLDAISMKCNVFLQQSPSGSSATTLRSELNLMVEKMDHVYGLST 1150
1151 VYLNKLKTIDVIVRSMQDAELLVKGYEIKLSQEEAVPADLSALESHRTTL 1200
1201 QHWLSDVKDKNSVFSVLDEEITKAKKVAEQLRHPASEPNLDLERYQEKGS 1250
1251 QLQERWHRVIAQLETRQSEVESIQEVLRDYRACHGTLIKWIEETTAQQEM 1300
1301 MKPGQAEDSRVLSEQLSQQTELFAEIERNQTKLDQCQKFSQQYSTIVKDY 1350
1351 ELQLMTYKAFVESQQKSPGKRRRMISSSDAITQEFMDLRTRYTALVTLTT 1400
1401 QHVKYISDALRRLEEEEKVVEEEKQEHVEKVKDLLGWVSTLARNTQGTTT 1450
1451 SSHTSASADIEKAILEQQVLAEELTTKKEQVSEAIKTSQIFLAKHGHKLS 1500
1501 EGEKEQISEQLRVLNKTYHDLCDGSANQLQQLQSELAQQTEQKGCRAVAG 1550
1551 VIDLGTVEIFPIFRAMQKGLIDQDTGLVLLESQIIMSGLIDPENSEKLSL 1600
1601 EEGLTRNFINLPIYQQLLGLRDSLSLVSRLTGTLGSLSVVEAIEKKIISE 1650
1651 RLGLKVLEVHLATGGFSLPPSENCINLEEAFHQGFIASSLHSELQSHLRS 1700
1701 SKNLIDPNTAEKVGLLDLMQRCIIHQESGLKLLPVKQLAGGMVSLKSGRK 1750
1751 VSIFRAVQEGLIDRQVTVRLLEAQLFAGGIVDPRTGHRLTVEEAVRHNLI 1800
1801 DQDMACAILIRQLQTGGIIDTVTGDRMTIDEAVTNNLVAAKIALVILESL 1850
1851 WSFMGLLLPESGEILPITDALEQGIVSTELAHKILHNRQQIEALFLPTLT 1900
1901 EIWSWEKATESGILDKDLVNNLRSVCIPDMMPHIQLADSAEQSKVGFAAG 1950
1951 KPPVSGPREEGSSHGEKLLFQLMTHSYIHAHTGQRLLLLDQELVEMLTSR 2000
2001 DDCQVILPEVFEIQHQRLNTSEALQELYTGTISQISSAKHPRKPCESQFL 2050
2051 SQNKDYPSQENCTEAKGERSVVGIECSPAESPERELFLKEQEAIIENVGS 2100
2101 LKVINKVKLKLQRPLLGSRKEEQAETLREENISGDPLLVECPEESEGKDL 2150
2151 STEKSKCQTPTKCSFTCHKEQVKTIKDIPSETGTSLIKSQNQMSQFQVDT 2200
2201 SVGLRSEFKSEHDMNVNSLEKELKEELLVKDGHKQSQEGQSVADGQTVAL 2250
2251 EKTDTEDNADEPALLHSSPFEDATLSTLSAQLQDGGIFNEETGQKLLLNE 2300
2301 AIAQGLVSSHTAVKLMGKLNMFRGFFDSQTCESLTTEEVIDEGLMDEKLL 2350
2351 YNVLMSDKAISGILDPRTHSLCSVKEAVAAGLLDKETATRILEGQVITGG 2400
2401 IVDLKRGKKLSVTLASNLGLVDTADQTELINLEKATKGRGAEKAVKERLI 2450
2451 ELQMETAGLMDPESKAPLTVLQSIDRGILEREAAVYLLTKQVLDGGIIHH 2500
2501 ISGLRLSVDNAFKHGLIGEDMARQLRKVENFIHYQFFHPQTKEALSFSEA 2550
2551 IKLDLVSPDLKREIQEIQDFSGNLGDFIYGQKLTLAKTNKEESLANKTEL 2600
2601 PSGVMHGVIDPENCTIIPYSELVKKCRIDTESGWRYLEVIPFSDIKDEAG 2650
2651 NNVLTPPEAIQLGKVDFASALKVLEAQANTGGIIDMATGKRVTLASALEK 2700
2701 KLLDENMARIIASHQMLSGGIIDIYSDQRVTLNDAVEKRLISPELAAMIQ 2750
2751 VDPLAEQGGTGVCELKGDFLRKELLSESSKTPRESYSKEKHEAVLQAGSL 2800
2801 CAPEKAGIRGSNGEKAEKGRKISVEMEGQRQDEKASSDSKVSASILSPFG 2850
2851 FEGESSYQVSVTHPCSESCDLKPREETRSCMKKCAVVERDKVVTQIKMVS 2900
2901 HVKQSTSGLDAEEARERQGRMVSKEQGSHYETAGNLLSERSVRVDRRVRR 2950
2951 EMGGEQSVQMSREAAVLSEEESDQEVTIGDEPDSFVKSQSMKMIGNDKGK 3000
3001 EAGIEKDISVVCKIEGFPSQMTSKDASLTNQDALPFYTEGETKTVNLCSI 3050
3051 LKPGEKLSQETASTVQKEPLSSEIPRPERLNSQESDEEPQISDVPHISKG 3100
3101 DMAAQITTRQETTDVQDLYITSKSSETKDKIFPSKNYIEKLHQEIPMDPT 3150
3151 RSHKLKEATISTLETEGISYLDSSDIKSLCEDSKADHKSCGHQKSKVTTT 3200
3201 QAKKSLEVVDLLVRDTEEGSSEDRVGQRGPRVLASLLPEKLPTRTVQSEN 3250
3251 IRQHDAVIPAISEIREEMALSLPCSVVKVDGKIPKEKHKEILGDEQGPFM 3300
3301 AIPSGKGIEGVNPEPCRATQNVFTRRLCLEHDEKLVSYLSLLRDIEMRTK 3350
3351 QIQPLELNVAELQDLLGQAKELDRELKDLSTVVSQELECVDRIVISQPQE 3400
3401 VPAQLLKALEKDAKNLQKSLDSVSDSWSSRFLHLQSAVEVKKATVLNRHK 3450
3451 ELQGKLQDLRAWVGRASLTLNSKGCDTETDADSLSHTLQPYKDMKQSMAE 3500
3501 RKSQLDALALDIQLFISEHPQDLSLQQNQEMLQFLSELQRSFQGLVEHTA 3550
3551 AQKDVVQGHLQQVQQEVQVKTLQKQQDTCHKKLEDLCNWVGQAERALERH 3600
3601 QGGSSSGKSSLLCAEPKVDLKDLQGDIQSHSTSFATAVKDIEGFLEENQT 3650
3651 KLSPQELTALREKLHQAKEQYEVLQERTRVAQKELEEAVTSALQQETEKS 3700
3701 KAATELAENKRKIDALLDWVGLLWGHLRESHRLAFQEWSSSLELAMEKQT 3750
3751 LAATDGHVDVNQVPETLDRQYELMKARHQELLSQQQNFIVATQSVQSFLD 3800
3801 QHSHNLTPEERQKLQEKLGELKEQYAASLARSEAELKQTQALRDELQKFL 3850
3851 QDHKEFENWLQQSENELDSMHKGGSSPEALNSLLKRQGSFSEDVISHKGD 3900
3901 LRFVTISGQKVLETENNFEEGQEPSATRNLVNEKLKDATERYTTLHSKCI 3950
3951 RLGSHLSMLLGQYQQFQSSADSLQAWVLTCEASVGKLLSDTVASDPGVLQ 4000
4001 QQLATTKQLQEELAEHQVPVEKLQKAAHDLLDIEGEPALDCRPIQETTDS 4050
4051 ISSRFQNLSCSLDERSALLQKAIAQSQSVQESMESLLQSIREVEQNLERD 4100
4101 QVASLSSGVIQEALANNMKLKQDIARQKSSLEATHDMVTRFMETADSNSA 4150
4151 SVLQGKLAELSQRFQQLQLQQQEKESNLKKLLPQAEMFEQLSNKLQQFME 4200
4201 NKSRLLASGNQPDQDIAHFSQQIQELTLAMEDQKENLDTLEHLVTTLGSC 4250
4251 GFALDLSQHQDKIQNLKKDFTELQKTVQEREKDASTCQEQLDEFRKLIRT 4300
4301 FQKWLKETEGNVPPAKTFVSAKELEKQIEHLKDLISDWESKGALLGEINA 4350
4351 KGTALESLIMDITAPDSQAKTGSILPPVGSSVGSVNGYHTCKDLTEIQCD 4400
4401 MFDVNSKYEKLWEVLRERQESLQTVFSRMEEVQKEASSVLQWLESKEEVL 4450
4451 KAMDATLSPTKTETVKAQAESNKAFLAELEQNSPKIQKVKEALAGLLKTY 4500
4501 PNSQEAENWKKMQEDLNSRWEKATEVTVARQKQLEESASHLACFQAAESQ 4550
4551 LRPWLMEKELMMGVLGPLSIDPNMLKQQVQFMLKEFEARRQQHEQLNEAA 4600
4601 QGILTGPGDMSPSASQVHKDLQSISQKWVELTDKLNSRSSQIDQAIVKST 4650
4651 QYQDLLQDLSEKVKAIGQRLSGQSAISTQPEAVKQQLEETSEIRSDLGQL 4700
4701 DNEIKEAQTLCQELSLLIGEQYLKDELKKRLETVALPLQGLEDLAADRMN 4750
4751 RLQAALASTQQFQQMFDELRTWLDEKQSQQAKNCPISAKLERLQCQLQEN 4800
4801 EEFQKNLNQHSGSYEVIVAEGEALLLSVPPGEEKKTLQNQLVELRSHWED 4850
4851 LSKKTANRQSRLKDCMQKAQKYQGHVEDLVPWIDECKSKMSELQVTLDPV 4900
4901 QLESSLLRSKAMLNEAEKRRSLLEILNSAADILINSSEIDEDEIRDEKAG 4950
4951 LNQNMDAITEELQAKTSSLEEMTQRLKEFQESFKNIEKKVEGAKHQLEIF 5000
5001 DALGSQACSNKNLEKLKAQQEVLQALEPQVDYLRNFTQGLVEDAPDGSDA 5050
5051 SPLVHQAEVAQQEFLEVKQRVSSSCLTMENKLEGIGQFHCRVREMFSQLA 5100
5101 DLDDELDGMGAIGRDTDSLQSQIEDVRLFLNKIQALRFDIEDSEAECRKM 5150
5151 LEEEGTLDLLGLKRELEALNKQCGKLTERGKVRQEQLELTLGRVEDFYRK 5200
5201 LKALNDAATAAEEGEALQWIVGTEVDVINQQLADFKLFQKDQVDPLQVKL 5250
5251 QQVNGLGQGLIQSAGKNCDVQGLEHDMDEINTRWNTLNKKVAQRIAQLQE 5300
5301 ALLHCGKFQDALEPLLSWLTDTEELIANQKPPSAEYKVVKAQIQEQKLLQ 5350
5351 RLLDDRKATVDMLQAEGGRIAQSAELADREKITGQLESLERRWTDLLSKA 5400
5401 AARQKQLEDILVLAKQFHETAEPISDFLSVTANKLANSEPVGTQTAKIHQ 5450
5451 QIIRHKALNEEIINRKKNVDQAIKNGQALLKQTTGEEVLLIQEKLDGIKT 5500
5501 RYADITLTSSKALRTLEQARQLATKFHSTYEELTGWLREAEEELAASGGQ 5550
5551 SPTGEQIPQFQQRQKELKKEVMEHRLVLDTVNEVSHALLELVPWRAREGL 5600
5601 DKLVSDANEQYKLISDTVGQRVDEIDAAIQRSQQYEQAADAELAWVAETK 5650
5651 RKLMALGPIRLEQDQTTAQLQVQKAFSIDIIRHKDSMDELFSHRGEIFST 5700
5701 CGEEQKAVLQEKTECLIQQYEAVSLLNSERYARLERAQVLVNQFWETYEE 5750
5751 LSPWAEETLALIAQLPPPAVDHEQLRQQQEEMRQLRESIAEHKPHIDKIL 5800
5801 KIGPQLKELNPEEGKMVEEKYQKAENMYAQIKDEVRQRALALDEAVSQSA 5850
5851 QFHDKIEPMLETLENLSSRLRMPPLIPAEVDKIRECISDNKSATVELEKL 5900
5901 QPSFEALKRRGEELIGRSQGADKDLAAKEIQDKLDQMVFFWEDIKARSEE 5950
5951 REIKFLDVLELAEKFWYDMAALLTTIKDTQDIVHDLESPGIDPSIIKQQV 6000
6001 EAAETIKEETDGLHEELEFIRILGADLIFACGETEKPEVKKSIDEMNNAW 6050
6051 ENLNKTWKERLEKLEDAMQAAVQYQDTLQAMFDWLDNTVIKLCTMPPVGT 6100
6101 DLNTVKDQLNEMKEFKVEVYQQQIEMEKLNHQGELMLKKATDETDRDIIR 6150
6151 EPLTELKHLWENLGEKIAHRQHKLEGALLALGQFQHALEELMSWLTHTEE 6200
6201 LLDAQRPISGDPKVIEVELAKHHVLKNDVLAHQATVATVNKAGSELLESS 6250
6251 AGDDASSLRSRLETMNQCWESVLQKTEEREQQLQSTLQQAQGFHSEIEDF 6300
6301 LLELNRMENQLSASKPTGGLPETAREQLDTHMELHSQLRAKEEIYNQLLD 6350
6351 KGRLMLLSRGDSGSGSKTEQSVALLEQKWHAVSSKVEERKSKLEEALSLA 6400
6401 TEFQNSLQEFINWLTLAEQSLNIASPPSLILNTVLSQIEEHKVFANEVND 6450
6451 HRDQIIELDQTGNQLKFLSQKQDVVLIKNLLVSVQSRWEKVVQRSIERGR 6500
6501 SLDDARKRAKQFHEAWKKLIDWLEDAESHLDSELEISNDPDKIKLQLSKH 6550
6551 KEFQKTLGGKQPVYDTTIRTGRALKEKTLLAGDTQKLDNLLGEVRDKWDT 6600
6601 VCGKSVERQHKLEEALLFSGQFMDALQALVDWLYKVEPQLAEDQPVHGDL 6650
6651 DLVMNLMDAHKVFQKELGKRTGTVQVLKRSGRELIEGSRDDTTWVKGQLQ 6700
6701 ELSTRWDTVCKLSVSKQSRLEQALKQAEEFRDTVHMLLEWLSEAEQTLRF 6750
6751 RGALPDDTEALQSLIDTHKEFMKKVEEKRVDVNTAVAMGEAILAVCHPDC 6800
6801 ITTIKHWITIIRARFEEVLTWAKQHQQRLETALSELVANAELLEELLAWI 6850
6851 QWAETTLIQRDQEPIPQNIDRVKALITEHQSFMEEMTRKQPDVDRVTKTY 6900
6901 KRKSVEPTHAPFMEKSRSGSRKSLNQPTPPPMPILSQSEAKNPRINQLSA 6950
6951 RWQQVWLLALERQRKLNDALDRLEELCPELKEFANFDFDVWRKKYMRWMN 7000
7001 HKKSRVMDFFRRIDKDQDGKITRQEFIDGILASKFPTTKLEMTAVADIFD 7050
7051 RDGDGYIDYYEFVAALHPNKDAYRPTTDADKIEDEVTRQVAQCKCAKRFQ 7100
7101 VEQIGENKYRFFLGNQFGDSQQLRLVRILRSTVMVRVGGGWMALDEFLVK 7150
7151 NDPCRARGRTNIELREKFILPEGASQGMTPFRSRGRRSKPSSRAASPTRS 7200
7201 SSSASQSNHSCTSMPSSPATPASGTKVISSSGSKLKRPTPAFHSSRTSLA 7250
7251 GDTSNSSSPASTGAKANRADPKKSASRPGSRAGSRAGSRASSRRGSDASD 7300
7301 FDLLETQSACSDTSESSAAGGQGSSRRGLTKPSKIPTMSKKTTTASPRTP 7350
7351 GPKR 7354
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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