 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8R0W0 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MNGQAPPHDVVVANGTEKFIVPKIKKNQLGASTPSRPQAEAALPTTARSI 50
51 AGVYVEASGQTQSIYAAIKQGLLPTGLGLTLLEAQAATGGLVDLAQGQLL 100
101 PVSEALRRGLVGLELKEKLLAAERAVTGYPDPYGGEKLSLFQAIKKEVVD 150
151 RTLGWRLLEAQLATGGLVDPTQGVQVAPELACQQGLLDKETWLSLVESEP 200
201 SMGTPGFSDPNTLEQLPYSVLLGRCVQDPSSGLPLLPLKTTFHTLAGAAS 250
251 ASMLLEAGVLNEEMVRDLQEGMLVVSDVGTRPEVRRYLEGTGGLAGVVLL 300
301 PGGHKKSFFQATVEHLVSKGIALQLLEAQAATRTLVHPTTGQRLWVEEAV 350
351 KAGLVGPELHEQLLVAEQAVTGYYDPFSSSRIPVFQAMKKGLVDQPLALR 400
401 LLDAQLATGGLICPARRFRLPLEAALRFGCLDEETRQRLSQAMGFSDPTT 450
451 HDRLGYEQLLALSVTDPETGLAFLPLPGMSHANEPQGPTFIDHCTRQALS 500
501 KATTSISVGRYQGRPVSLWELLFSESVPVKKRAMLAQRHQEGALSVEELA 550
551 AELKNIVEQAAATAKVTFAGLRDTVTPGELLKAEIINQDLFEQLERGQTS 600
601 AQDVGSLDSVQRYLQGTGSIAGLLLPDSQERLSIYEARSKGLLRPGTALI 650
651 LLEAQAATGFIIDPKENKRYSVEEALRAGVIGPDVYAKLLSAEHAVTGYT 700
701 DPYSGEQISLFQAMQRDLIVRDHGIRLLEAQIATGGVIDPVHSHRVPVDV 750
751 AYQRGYFDQILNSILLDPSDDTKGFFDPNTHENLTYLQLLERCVHDSETG 800
801 LHLLPLSSTRPQLVDSSTRQAFQKLLLSVKYGRFRGQRVSAWELVNSEYF 850
851 TEDRRRQLLQRYRQRKITLEQVTQLLEKEMRRWTDITLPALQGQVTAYQL 900
901 LEAHIINQELLDQVLTGTISPDALLQVGDVHRYLRGSGTVGGVLLKPSNQ 950
951 RISLYQAMKQKLLPPSTALALLEAQAATGTITDPCSMETLSVDEAVCRGV 1000
1001 VGAEVYGKLKRAEHSITGYRDPFSGKKVSLFRAMKKGLVPVEQATRLLEA 1050
1051 QVSTGGVVDPTTHLHLPMPVAVQRGCIDREMEAALSRSPETFPTPDGRGH 1100
1101 TSYAQLLEHCLQDKASGLHLLPLTEDAPNVPTDTQIQETLQASAGTEDGL 1150
1151 SLWDLLTSCHFTEEQRRGYLEDVKVGKISVPQLQNTVRSWVHSAKLLARA 1200
1201 RITVPGPRGEVPATWLRDAGIITQETLEALAQGMQSPDEVAKQPTVKVCL 1250
1251 WGTGCVAGVLLQPSGTKLSIAQAVRDGLLPTGLGQQLLEAQVASGFLVNP 1300
1301 LTNQRLSVEGAVKAGLVGMEQSEHLRQVEKAVTGYSDPFSGGSLSLWQAM 1350
1351 EKGLVTQSEAFPLLQVQLATGGVVDPVHGVHLPQEVAYKLGLLDEQTSRV 1400
1401 LTATGKENKLFFDPNSREKVTYQQLRELCVLDADTGLWLLPLPQGTVLEV 1450
1451 DDHTAVALRAMKVPINMGRFQGHSVSLWDLLHSEYVGAEKRRELVALCCS 1500
1501 GRAAALRQVIGMLTTLVEAAEKQPSQATFKGLRKQVSAGDLFRSQLITKQ 1550
1551 TLDELNQGKRTVQEVTEMDSVRRSLEGGNFIAGVLIQDTKEKMSIPEALR 1600
1601 RHILRPGTALVLLEAQAATGFIIDPVENRKLTVEQAFQAGMFGKETYMKL 1650
1651 LSAERAVTGYTDPYTGEQISLFQAMQRDLIVRDHGIRLLEAQIATGGIID 1700
1701 PVHSHRVPVDVAYQRGYFNEEMNRILSDPSDDTKGFFDPNTHENLTYLQL 1750
1751 LERCVEDPETGLYMLEIVKKGETYTYIDEATRQALTSRTVKMYVGKFAGQ 1800
1801 TVSVWDLLSSQYFTEGRRRKLLREYRAQNIGLENLLEVITSTVEETEKQS 1850
1851 QIFKVPGIHGDVTAAELFNSGILNKKTLDALRSGDRGFQDLRWLEDVRVY 1900
1901 LEGSNYIAGVIAPLTQKVMSFYEASREELIPAGFAAQMLEAQAATGYLMD 1950
1951 PCTNQRLCVDEAIAAGLVGEDLRERLVNAEMAAKGYKDPATGETIPLYQA 2000
2001 MERKLVGREEALRLLEVQVATGGVIDPRHHHRVPLDTACQRGCMCDDSLV 2050
2051 LIADQKHMRKRFVDPNTQEKVTYQELQDRCQREEKSGWALFPVVKDKKDI 2100
2101 EYVDEATKRALEAEQVEVTVGRYRGQRRSVWELLNSEYVSEEKKMELVRL 2150
2151 YKEDTTRALQKVVELILQMIADKERRSRQLWFRGLRTQVTAEELLRSEVI 2200
2201 TKQTLEDLEEGRTTVDQIERKEDVKRYLKGTSCIAGVLVPVQGEPGRQEK 2250
2251 MSIYQAMWKGVLRPGTALVLLEAQAATGFIIDPVNNRRLSVEEAVAAGVV 2300
2301 GGEIQEKLLSAERAVTGYTDPYTGDQISLFQAMQRDLIVRDHGIRLLEAQ 2350
2351 IATGGVIDPVHSHRVPVDVAYQRGYFDEDMNSILADPGDDTKGFFDPNTH 2400
2401 ENLTYLQLLRRCVRDPETGFYMLQLAGKGSSVHHLSEELRRALREARVTP 2450
2451 GTGDFQGQSISVWELLFYREVPESLRQDLLRCYQAGGLTVHDVTTTLTSL 2500
2501 LARAKDGSPRGDPQGALGKATMEVKRGHLRGHEVPVWDILTSNYVSRDTR 2550
2551 KELLAQFSSGSLTLPMLKRRLTTIIEEAEETQESKPKPRDASLKQQDTGA 2600
2601 RGSGTSPDEGDAQDSSESARQQQEQTLRATTMQVHRGQFRDQQVSVWKVL 2650
2651 FSSYLSETRREELLAQHLAGKLGVMELVSLLTQIIEETEERLSKVSFPGL 2700
2701 RRQVSASELCTSGILDRDTMRELAQGTKTIHEVTEMDSVKRYLGGSSCIA 2750
2751 GVLVPVQGEPGRQEKMSIYQAMWKGVLRPGTALVLLEAQAATGFIIDPVN 2800
2801 NRRLSVEEAVAAGVVGGEIQEKLLSAERAVTGYTDPYTGDQISLFQAMQR 2850
2851 DLIVRDHGIRLLEAQIATGGVIDPVHSHRVPVDVAYQRGYFDEDMNSILA 2900
2901 DPGDDTKGFFDPNTHENLTYLQLLRRCVRDPETGFYMLQLAGKGSSVHHL 2950
2951 SEELRRALREARVTPGTGDFQGQSISVWELLFYREVPESLRQDLLRRYQA 3000
3001 GGLTVHDVTTTLTSLLARAKDGSPRXDPQGALGKATMEVKRGHLRGHXVP 3050
3051 VWDILTSNYVSRDTRKELLAQFSSGSLTLPMLKRRLTTIIEEAEETQESK 3100
3101 PKPRDASLKQQDTGARGSGTSPDEGDAQDSSESARQQQEQTLRATTMQVH 3150
3151 RGQFRDQQVSVWKVLFSSYLSETRREELLAQHLAGKLGVMELVSLLTQII 3200
3201 EETEERLSKVSFPGLRRQVSASELCTSGILDRDTMRELAQGTKTIHEVTE 3250
3251 MDSVKRYLGGSSCIAGVLVPVQGEPGRQEKMSIYQAMWKGVLRPGTALVL 3300
3301 LEAQAATGFIIDPVNNRRLSVEEAVAAGVVGGEIQEKLLSAERAVTGYTD 3350
3351 PYTGDQISLFQAMQRDLIVRDHGIRLLEAQIATGGVIDPVHSHRVPVDVA 3400
3401 YQRGYFDEDMNSILADPGDDTKGFFDPNTHENLTYLQLLRRCVRDPETGF 3450
3451 YMLQLAGKGSSVHHLSEELRRALREARVTPGTGDFQGQSISVWELLFYRE 3500
3501 VPESLRQDLLRRYQAGGLTVHDVTTTLTSLLARAKDGSPRXDPQGALGKA 3550
3551 TMEVKRGHLRGHXVPVWDILTSNYVSRDTRKELLAQFSSGSLTLPMLKRR 3600
3601 LTTIIEEAEETQESKPKPRDASLKQQDTGARGSGTSPDEGDAQDSSESAR 3650
3651 QQQEQTLRATTMQVHRGQFRDQQVSVWKVLFSSYLSETRREELLAQHLAG 3700
3701 KLGVMELVSLLTQIIEETEERLSKVSFPGLRRQVSASELCTSGILDRDTM 3750
3751 RELAQGTKTIHEVTEMDSVKRYLGGSSCIAGVLVPVQGEPGRQEKMSIYQ 3800
3801 AMWKGVLRPGTALVLLEAQAATGFIIDPVNNRRLSVEEAVAAGVVGGEIQ 3850
3851 EKLLSAERAVTGYTDPYTGDQISLFQAMQRDLIVRDHGIRLLEAQIATGG 3900
3901 VIDPVHSHRVPVDVAYQRGYFDEDMNSILADPGDDTKGFFDPNTHENLTY 3950
3951 LQLLRRCVRDPETGFYMLQLAGKGSSVHHLSEELRRALREARVTPGTGDF 4000
4001 QGQSISVWELLFYREVPESLRQDLLRRYQAGGLTVHDVTTTLTSLLARAK 4050
4051 DGSPRXDPQGALGKATMEVKRGHLRGHXVPVWDILTSNYVSRDTRKELLA 4100
4101 QFSSGSLTLPMLKRRLTTIIEEAEETQESKPKPRDASLKQQDTGARGSGT 4150
4151 SPDEGDAQDSSESARQQQEQTLRATTMQVHRGQFRDQQVSVWKVLFSSYL 4200
4201 SETRREELLAQHLAGKLGVMELVSLLTQIIEETEERLSKVSFPGLRRQVS 4250
4251 ASELCTSGILDRDTMRELAQGTKTIHEVTEMDSVKRYLGGSSCIAGVLVP 4300
4301 VQGEPGRQEKMSIYQAMWKGVLRPGTALVLLEAQAATGFIIDPVNNRRLS 4350
4351 VEEAVAAGVVGGEIQEKLLSAERAVTGYTDPYTGDQISLFQAMQRDLIVR 4400
4401 DHGIRLLEAQIATGGVIDPVHSHRVPVDVAYQRGYFDEDMNSILADPGDD 4450
4451 TKGFFDPNTHENLTYLQLLRRCVRDPETGFYMLQLAGKGSSVHHLSEELR 4500
4501 RALREARVTPGTGDFQGQSISVWELLFYREVPESLRQDLLRRYQAGGLTV 4550
4551 HDVTTTLTSLLARAKDGSPRXDPQGALGKATMEVKRGHLRGHXVPVWDIL 4600
4601 TSNYVSRDTRKELLAQFSSGSLTLPMLKRRLTTIIEEAEETQESKPKPRD 4650
4651 ASLKQQDTGARGSGTSPDEGDAQDSSESARQQQEQTLRATTMQVHRGQFR 4700
4701 DQQVSVWKVLFSSYLSETRREELLAQHLAGKLGVMELVSLLTQIIEETEE 4750
4751 RLSKVSFPGLRRQVSASELCTSGILDRDTMRELAQGTKTIHEVTEMDSVK 4800
4801 RYLGGSSCIAGVLVPVQGEPGRQEKMSIYQAMWKGVLRPGTALVLLEAQA 4850
4851 ATGFIIDPVNNRRLSVEEAVAAGVVGGEIQEKLLSAERAVTGYTDPYTGD 4900
4901 QISLFQAMQRDLIVRDHGIRLLEAQIATGGVIDPVHSHRVPVDVAYQRGY 4950
4951 FDEDMNSILADPGDDTKGFFDPNTHENLTYLQLLRRCVRDPETGFYMLQL 5000
5001 AGKGSSVHHLSEELRRALREARVTPGTGDFQGQSISVWELLFYREVPESL 5050
5051 RQDLLRRYQAGGLTVHDVTTTLTSLLARAKDGSPRXDPQGALGKATMEVK 5100
5101 RGHLRGHXVPVWDILTSNYVSRDTRKELLAQFSSGSLTLPMLKRRLTTII 5150
5151 EEAEETQESKPKPRDASLKQQDTGARGSGTSPDEGDAQDSSESARQQQEQ 5200
5201 TLRATTMQVHRGQFRDQQVSVWKVLFSSYLSETRREELLAQHLAGKLGVM 5250
5251 ELVSLLTQIIEETEERLSKVSFPGLRRQVSASELCTSGILDRDTMRELAQ 5300
5301 GTKTIHEVTEMDSVKRYLGGSSCIAGVLVPVQGEPGRQEKMSIYQAMWKG 5350
5351 VLRPGTALVLLEAQAATGFIIDPVNNRRLSVEEAVAAGVVGGEIQEKLLS 5400
5401 AERAVTGYTDPYTGDQISLFQAMQRDLIVRDHGIRLLEAQIATGGVIDPV 5450
5451 HSHRVPVDVAYQRGYFDEDMNSILADPGDDTKGFFDPNTHENLTYLQLLR 5500
5501 RCVRDPETGFYMLQLAGKGSSVHHLSEELRRALREARVTPGTGDFQGQSI 5550
5551 SVWELLFYREVPESLRQDLLRRYQAGGLTVHDVTTTLTSLLARAKDGSPR 5600
5601 XDPQGALGKATMEVKRGHLRGHXVPVWDILTSNYVSRDTRKELLAQFSSG 5650
5651 SLTLPMLKRRLTTIIEEAEETQESKPKPRDASLKQQDTGARGSGTSPDEG 5700
5701 DAQDSSESARQQQEQTLRATTMQVHRGQFRDQQVSVWKVLFSSYLSETRR 5750
5751 EELLAQHLAGKLGVMELVSLLTQIIEETEERLSKVSFPGLRRQVSASELC 5800
5801 TSGILDRDTMRELAQGTKTIHEVTEMDSVKRYLGGSSCIAGVLVPVQGEP 5850
5851 GRQEKMSIYQAMWKGVLRPGTALVLLEAQAATGFIIDPVNNRRLSVEEAV 5900
5901 AAGVVGGEIQEKLLSAERAVTGYTDPYTGDQISLFQAMQRDLIVRDHGIR 5950
5951 LLEAQIATGGVIDPVHSHRVPVDVAYQRGYFDEDMNSILADPGDDTKGFF 6000
6001 DPNTHENLTYLQLLRRCVRDPETGFYMLQLAGKGSSVHHLSEELRRALRE 6050
6051 ARVTPGTGDFQGQSISVWELLFYREVPESLRQDLLRRYQAGGLTVHDVTT 6100
6101 TLTSLLARAKDGSPREDPQGALGKATMEVKRGHLRGHVVPVWDILTSNYV 6150
6151 SRDTRKELLAQFSSGSLTLPMLKRRLTTIIEEAEETQESKPKPRDASLKQ 6200
6201 QDTGARGSGTSPDEGDAQDSSESARQQQEQTLRATTMQVHRGQFRDQQVS 6250
6251 VWKVLFSSYLSETRREELLAQHLAGKLGVMELVSLLTQIIEETEERLSKV 6300
6301 SFPGLRRQVSASELCTSGILDRDTMRELAQGTKTIHEVTEMDSVKRYLGG 6350
6351 SSCIAGVLVPVQGEPGRQEKMSIYQAMWKGVLRPGTALVLLEAQAATGFI 6400
6401 IDPVNNRRLSVEEAVAAGVVGGEIQEKLLSAERAVTGYTDPYTGDQISLF 6450
6451 QAMQRDLIVKNHGIRLLEAQIATGGVIDPVHSHRVPVDVAYQRGYFDQEM 6500
6501 NSILADPGDDTKGFFDPNTHENLTYLQLLQRATIDPETGLLFLSLSKG 6548
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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