 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q700K0 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MLLPALLFGMLWAPANGHWCEQIETVHVEEEVTPRREDLVPCTSLYHYSR 50
51 LGWKLDLSWGGHVGLTRPPALGLCAIYKPPETRPATWNRTVRACCPGWGG 100
101 IHCTEALAEASPKGHCFVTWHCQPLAGSANSSAGSLEECCAQPWGHSWWN 150
151 SSSQMCLSCSGQHRPGNASSEGLLQPLAGAVGQLWSQRQRPSATCATWSG 200
201 FHYQTFDGQHYHFLGQCTYLLAGAMDSTWAVHLRPSVHCPQPRQCWLVQV 250
251 IMGPEEVLIQDGEVSVKGQPVPVGESQLLHGMSLQWQGDWLVLSGGLGVV 300
301 VRLDRSSSIIISVDHEFWGRTQGLCGLYNGRPEDDFVEPGGGLAMLAATF 350
351 GNSWRLPGYEPGCLDTVEVARGCEGLLEGTLTGLEAGKLQAQAQDLCHQL 400
401 LEDPFSQCHGQVPPDEYHETCLFAYCVGATAGSGPEEQVKAVCATFANYA 450
451 QACARQHIYVHWRKPGFCERLCPGGQLYSDCISSCPPSCSAVAQGEEGSC 500
501 GKECVSGCECPTGLFWDGALCVPAAHCPCYYRRQRYAPGDTVKQQCNPCV 550
551 CQDGRWHCAQAPCPAECAVGGDGHYCTFDGRSFSFRGNPGCQYSLVQDSV 600
601 KGQLLVVLEHGACETGSCLHALSAFLGKTHIQLRYSGAVLVDGQDVDLPW 650
651 IGAEGFNVSHASSTFLLLRWPGAWVLWGVADPAVYITLDPRHAYQVQGLC 700
701 GTFTWKQQDDFLTPAGDIETSVTAFASKFQVSGDGRCPLVDNTPLSSCST 750
751 YSQRLAFAEAACAALHGHAFQECHGLVEREPFRLRCLESMCSCAPGRDCL 800
801 CSVLSAYAHHCAQEGVLLQWRNETLCSVPCPGGQVYQECAPACGHYCGEP 850
851 EDCKELGSCVAGCNCPPGLLWDLEGQCVPPSMCPCQLGGHRYAFNTTTTL 900
901 KDCSHCICQERGLWNCIAHHCPRQWALCPQELIYAPGACLLTCDSLGANH 950
951 SCLAGSTDGCVCPSGTVLLDKHCVSPDLCPCRHNGQWYPPNATIQEDCNI 1000
1001 CVCQNQRWHCTGQRCSGWCQASGAPHYVTFDGLVFTFPGACEYLLVREAG 1050
1051 GRFSVSIQNLPCGASGLTCTKALAVRLDSTVVHMLRGQAVTVNGVSIKLP 1100
1101 KVYTGPGLSLHHAGLFLLLTTRLGLTLLWDGGTRVLVQLSPHFHGRVTGL 1150
1151 CGNFDGDVSNDLRSRQGVLEPTAELTAHSWRLNPLCPEPGDLPHPCSVNA 1200
1201 HRVNWARAHCEVILQPIFAPCHTEVPPQQYYEWCVYDACGCDTGGDCECL 1250
1251 CSAIATYADECARHRHHVRWRSQELCPLQCEGGQVYEPCGSTCPPTCHDH 1300
1301 HPELRWHCQAITCVEGCFCPEGTLLHGGTCVELTDCPCEWQGSFFPPGAV 1350
1351 LQKDCGNCTCQESQWHCNPSGAPCEEMEPGCAEGEALCRESGHCVPLEWL 1400
1401 CDNQDDCGDGSDEEGCDTSVCGEGQMSCQSGRCLPLSLICDGQDDCGDGT 1450
1451 DEQGCLCPQGFLACADGRCLPPALLCDGHPDCLDAADEESCLGWVSCTSG 1500
1501 EVSCVDGPCIRTIQLCDGVWDCPDGADEGPVHCSSPSLPTPPAGIGQNPS 1550
1551 TSSPDTSPSPVGSASPASPCSLSEFQCNSGECTPRGWRCDREEDCTDGSD 1600
1601 ELDCGGPCKLYQMPCAHGPHCLSPGQLCDGVAQCPDGSDEDPDVCEERSA 1650
1651 SGGPNGTAVPCPEFSCPNGTCIDFLLVCDGSPDCELADETEPSLDEQGCG 1700
1701 TWGSWGPWEPCSQTCGPGIQSRNRNCSISSLHVLQNCPGLQHQSQSCFTE 1750
1751 ACPVDGEWSSWSLWSPCSEPCGGTMTRHRQCRPPQNGGQDCALLPGSTHS 1800
1801 THQTSPCPQEGCLNVTCFGELVFRPCAPCPLTCDDISGEAVCSPDRPCSS 1850
1851 PGCWCPEGKVLGTEGRCVRPRQCPCLVDGIRYWPGQRIKMDCQLCFCQDG 1900
1901 QPHRCRPNPECAVDCGWSSWSPWAECLGPCSSQSLQWSFRSPNNPRLSGH 1950
1951 GRQCRGIHRKARRCQTEPCEGCEQWGLTYHVGERWRGGPCTVCECLHRSI 2000
2001 TRCSPYCPIGSCPQSWVLVEGMGESCCHCALPGKNQTMIPVTTPAPVPTP 2050
2051 SPQIGASLVTYVLPPMSDACYSPLGLAGLPTWAPSQPLEHSTRAAPVEAP 2100
2101 TAGPGPREDAYAEWHTQPLYLQLDLLWPRNLTGIMVQRAGSSAAYISNLS 2150
2151 LQFSSDGLQWHSVLNSLSSTLPPPKPSPESSNHMVPEVWTFDQMVQARYI 2200
2201 RVWPHGSHLRDNNQQDIFLWVELLGCKPVPPLAPLCPGTRHRCANGDCAL 2250
2251 KGGPCDGAVDCEDGSDEEGCGPLRASTASRVHSTARTPALSPTQPGKFPF 2300
2301 HPREGLADMEHQQPKQESPMPSAGVSPSASEGLLPVSGQSMQTLTTTSTF 2350
2351 PPGLKSLHPGMAAVTVHSPHSVMPGTPVGQSVSPRPFPLMRCGPGQVPCD 2400
2401 VLGCVEQEQLCDGREDCLDGSDEQHCASPEPFTVPTTALPGLPASRALCS 2450
2451 PSQLSCGSGECLPLEHRCDLQVNCQDGSDEDDCVDCVLAPWSGWSDCSRS 2500
2501 CGLGLIFQHRELLRPPLPGGSCLLDQLRSQPCFVQACPVAGAWAKWGPWE 2550
2551 PCSVSCGGGHQSRQRSCVDPPPKNGGAPCPGPSHEKVLCNLQLCPGDTDC 2600
2601 EPGRVHVNAELCQKGLLPPCPPSCLDPEANRSCSGHCVEGCRCPPGLFLQ 2650
2651 DSHCLPLSECPCLVGQKLMQPGLAFLLDNCSQCICESGILLCKPAACSQS 2700
2701 CGWSAWSPWTACDHSCGSGVRARFRSPTNPPAAFGGSPCEGDRQELQACY 2750
2751 TDCGTDIPGWTPWTSWSSCSQSCLVPGGDPGRRQRSRLCPSSRDTFCPGE 2800
2801 ATQEEPCSPSLCPVPSAWGLWASWSACSASCNGGIQTRGRSCSGSAPGNP 2850
2851 VCLGPHTQTRDCNVHPCTAQCPGNMVFRSAEKCLEEGGPCPQLCLAQDPG 2900
2901 VECTGSCAPGCSCPPGLFLHNASCLPRSQCPCQLHGQLYAPGAVARLDCN 2950
2951 NCTCSSGEMVCTSERCPVACGWSPWTPWSPCSQSCNVGIRRRFRAGTAPP 3000
3001 AAFGGAECRGPNLDAEFCTLRPCQGPGAAWSSWTPCSVPCGGGYRNRTQG 3050
3051 SGRHSPVEFSTCSLQPCAGPVPGVCPKDQQWLDCAQGPASCAHLSTPREA 3100
3101 NQTCHPGCYCLSGMLLLNNVCVPAQDCPCAHRGRLHSPGSAVILPCENCS 3150
3151 CVSGLITNCSSWPCEEGQPAWSSWTPWSVCSASCSPARRHRRRFCVRPST 3200
3201 TAPFSLDLPTTVAAPTMLCPGPEAEEEPCLLPGCNQAGVWGPWSPWSGCS 3250
3251 RSCGGGLRSRTRACDKPPPQGLGDFCEGPQAQGEACQAQPCPVANCSTIE 3300
3301 GAEYSPCGPPCPRSCDDLVHCMWHCQPGCYCPPGKVLSADGAICVQPHHC 3350
3351 SCLDLLTGKRHHPGSQLMRPDGCNHCTCMEGRLNCTDLPCQVSGDWCPWS 3400
3401 EWTACSQPCRGQTRTRSRACVCPAPQHGGAPCPEEAGETGVQHQMEACPN 3450
3451 PTACPVDGAWSPWGSWSPCDACLGQSYRSRMCSHPPPSDGGTPCLGGHQQ 3500
3501 SRPCRNSSTPCTDCGGGQDLLPCGQPCPHSCQDLSLGSTCQPGSSGCQSG 3550
3551 CGCPPGQLSQDGLCVFPADCHCHFQPKAMGIPENQSRSVGSALSSWESLE 3600
3601 PGEVVTGPCDNCTCVAGVLQCHEVPSCPGPGIWSSWGPWEKCSVPCGGGE 3650
3651 QLRSRQCARPPCPGLAQQSRTCHIHVCRETGCPAGRLYRECQPSEGCPFS 3700
3701 CAHVTGQVACFSESCEEGCHCPEGTFQHHSACVQECPCVLTVSLLQELGV 3750
3751 ASTALRSYPVLLGDEGQPLGPGDELDPGQMLQTVCGNCSCVHGKLSCSME 3800
3801 ECSRVRGYFGPWGMWSLCSHSCGGLGTRTRTRQCVLPTLAPAGLSCRGPL 3850
3851 QDLEYCFSPECPGTAGSTVEPVTGLAGGWGPWSPWSPCSHSCTDLTHPAW 3900
3901 RSRTRLCLANCTVGDSSQERPCNLPSCTTLPLCPGPGCGSENCFWTSWAP 3950
3951 WEPCSRSCGVGQQRRLRAYHPPGPGGHWCPDILTAYQERRFCNLRACPVP 4000
4001 GGWSHWSPWSWCDRSCGGGRSLRSRSCSSPPPKNGGASCVGERHHVRSCN 4050
4051 PMPCEKDCPAGMEMVSCANRCPYSCSDLQEAVMCQEDQACQLGCRCSEGF 4100
4101 LEQDGGCVPVGHCECTDAQGRSWAPGSQHQDACNNCSCQAGQLSCTAQPC 4150
4151 PPPAHCAWSHWSAWSACSHSCGPHGQQSRFRSSTSGSWALECQKEQSQSQ 4200
4201 PCPEDPCPPLCLHEAHLHVLGDNWLHGECQQCSCTPEGVICKDTDCAVPG 4250
4251 GWTLWSSWSYCSVSCGGGSQVRTRSCMVSAPQHGSPSCQGPDTQTQHCGQ 4300
4301 QLCLQLLEICSWGPWGPCSRSCGTGLASRSGSCPCLLTKEDSECNDTFSG 4350
4351 LDTQACYPGPCQEDCMWSDWSSWTRCSCKILVQQRYRHQVPAPGQAGEGT 4400
4401 LCTGLDGHFRPCAIGNCSEDSCLPPFEFQSCGSPCAGLCATHLSHQLCQD 4450
4451 LPPCQPGCYCPMGLLEQDGGCILPEQCNCWHTSGEGARVTLAPGHRLQLG 4500
4501 CKECVCQSGELQCSSQGCEGLLPLTGWSEWSPCGPCLPQSALAPDSRTAL 4550
4551 EVHWPLNTSVTLLASEQYRHRLCLDPETGRPWAGDPALCTVPLSQQRLCS 4600
4601 DPGACHDTCQWGPWGPWSPCQVPCSGGFKLRWREASDNSVGECRGPWAQT 4650
4651 ESCNMGSCPGESCETRDTVFTLDCANQCPRSCADLWEGVQCLQGPCSPGC 4700
4701 RCPPGQLVQDGHCVPISSCRCGLPSANASWELAPTQVVQLDCHNCTCING 4750
4751 TLMCPYPECPVLGPWSPWSECSAVCGGGTMVRYRSCEEHPDSAPCQALDM 4800
4801 EQRVECNLQTCPECPPGQVLSTCATLCPSFCSHLWPGTICVREPCQLGCG 4850
4851 CPGGQLLHSGTCIPPEACPCTRLSLPWGLTLPLEEQAQELPSGTVLTWNC 4900
4901 THCTCQGGVFTCSHTDCQECPPGEILQLGELRPCEKTCLEMNKTQAWSNC 4950
4951 TEAQVPGCVCQLGHFRSHTGLCVPEDHCECWHHGSPHLPGSEWQEACESC 5000
5001 RCLHGKSVCTQHCPELSCAQGEVVVQEPGSCCPICQQDTLEEEPVSCRHL 5050
5051 TELRNLTKGPCHLDQVEVSYCSGHCRSSTNVMTEEPYLQSQCDCCSYRLD 5100
5101 PDSPVRILNLLCPDGRTEPVLLPVIHNCHCSACQGGEFSKH 5141
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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