 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P34576 from www.uniprot.org...
The NucPred score for your sequence is 0.82 (see score help below)
1 MQAGISIFFLFLHIPIFFVNCSNSTSCVAREEFQCKMDDSCISMKKWQDG 50
51 VDDCYDGSDEVCLPWQFDCQFGSPRCISKNKLHDKKIDCYSGFDEGCPAH 100
101 YFVCRDRSACIEPSKYLNGVADCKDKSDEPCAQNQFQCSDGTKCIPKAQF 150
151 QDGKEDCDDGSDEECTTSQFACQCGTIKCVSDTFIMDGNWDCEDGSDEFI 200
201 NKTLTANCTRNNKVNIATNSLSLGKLKFCSGKNPCKPELGQVCVIIGGTW 250
251 RCVCKLGTFRPLGSDKCIPIELLTRYRNAPSSKCSNSHEEQFGLLQGFQK 300
301 SLSSRKELERWSNIRRAPPRTLSGTTPSGSDGFLKSGAEQVPFMFQEDKN 350
351 YVSVIPDEIIDSYGTIMTPPEFHFNRDDIRCGNKTCGLHESCQKNSESKY 400
401 ECICREGFTIFEGTCRELIDECAQGKHDCHPEARCVDALIGYECLCREGY 450
451 LDTSIDPKARPGRKCRKLINECTNALMNDCSQNARCLDKPIGYTCRCQDD 500
501 YVDVSREGARKPGRNCTQAINECASNLHNCDTHAICQDQPVGYSCRCPFG 550
551 FIDSSPTALEPGRKCVQANNEAATTSTTTSQCIKEKNGETVCKCLLGYKN 600
601 VGTKTHLNCQMEKRANPCQDYSLHDCDPVAECFSEQPGYFQCQCPKGFTD 650
651 SSADKRFPGRKCVRAVDECALGRHTCDPHADCIDTHQGYTCKCRSGWSDT 700
701 SLDPLRSPGRSCKKADMCSNIDCAAEAECRETPIGPMCQCVSGYVDVSRQ 750
751 HGRPAGRVCRAVVNECAEGRHDCSSHATCIDTADGFTCRCKDSYRDESSD 800
801 TLKHPGKNCVRTVQPDPPECDVSDPMSCDPAKREVCIFVENTYKCRCANG 850
851 YSRLPDGRCVVINECAEPRLNTCGKNAECIDLAEGYTCQCRSGYADISPV 900
901 SQPGRICRARVNECSNKEKYNVDCSENAICADTEHSYSCRCRPGFADVSA 950
951 AFNKLPGRRCIEAVNECASPSLNDCSKNAFCEDAKEGYICTCRPGYVDNS 1000
1001 PNAARHPGRICTKPVEKIKTDLKDTSFSTDDGCDPKNPKCGANEACVQRH 1050
1051 GQHNCECVETAFRYTDGSCRVYSACSKRNTCDKNAICLNRFDSYTCQCRP 1100
1101 GYIDLSADLTNAPGRICKELINECASSDNECSPYARCIDATNGYACQCLD 1150
1151 GFIDVSSRYNKPPGRQCTNSNNECSEKSLNTCDENADCVDTPDGYTCQCY 1200
1201 GGFVDVSSNANLPPGRVCTVQTTCPKQKTDLVFLIDGSGSIGSYVFKNEV 1250
1251 LRFVREFVELFEIGRSKTRVGLIQYSDQIRHEFDLDQYGDRDSLLKGISE 1300
1301 TQYLTGLTRTGAAIQHMVQEGFSERRGARPQQSDIARVAIILTDGRSQDN 1350
1351 VTGPADSARKLSINTFAIGVTDHVLASELESIAGSPNRWFYVDKFKDLDT 1400
1401 RLRSMIQKAACPSPTKQETPSEDVCNPRTQTGCDRSLNEHCAVENGRPRC 1450
1451 VCPEGFTRHPFTRVCGGDLCNPQLITSCIFPEECQITPYKNFRCSCPEGY 1500
1501 NRDYRSGFCVSVKEVQISPQHDANCHNGGVRCSENERCTNDGSDWFCECL 1550
1551 PGFERIRNGQCAYPGSCNPNDPMSCDVRKRQQCLPRGNIYTCQCGRNEKR 1600
1601 HPITDICLKNECLTGEHDCDRSARCIDTDESYICACQSGFIDHSPNPSER 1650
1651 PGRVCVALQNECLDGSNRCSPNALCTDTEEGYVCRCKSGFVDYSPNPQTF 1700
1701 PGMVCKELVNECTNPRLNQCDRNAHCIDTIEGYSCICKPGFVDMDEFGNP 1750
1751 GRRCEQIKTNDKCSPGKNDCDRNARCIQIGDDDYSCACPPGFKDKSPSSS 1800
1801 RPGRLCIPVIPECDNPTLNDCDSPDRAVCTDTDDGYMCRCRQGFLDISPS 1850
1851 ISVKPGRLCKPLQNECALGIDDCARDGGICEDNPDSFTCRCAMNYLDVSF 1900
1901 DRVTRPGRKCKRLINECQTGQNDCSEEATCTDTEDSYICACPQSHIDLSP 1950
1951 DTVNRPGRRCLMRINECTSNRHDCSPNADCIDTPESYKCRCRDDFVDESP 2000
2001 DSSRRPGRICRPALVDECRTGKHDCHVNAICQDLPQGYTCQCSADFVDVS 2050
2051 PHRASHPGRLCQPRPTPPPPECRLDGGNQCKVHLNEVCRLMGGEPKCSCP 2100
2101 VNYQRDSSGSCSIINECLFTQLNDCHTAADCIDQVQGYTCQCRDGFKDIG 2150
2151 DRRRPGRMCKPMVNECQYPHLNDCHQNAACIDLEEGYECKCNQGFMDHSH 2200
2201 GRPGRICKQLTNECLRPSLNSCDRNARCIDKEEGYECECRDGFIDVSPSP 2250
2251 TLKGRACRELVNECANSRLNDCDKNARCKDTMDSYECDCPVNSKDISPSP 2300
2301 SFPGRVCLMFINECESGVHDCDPSATCRDNEQSFTCECPSGFVDRSPNKH 2350
2351 ARPGRVCVKLVDECREGRHTCSSHADCRDLEEGYTCECRDGYVDRSPNLA 2400
2401 SQPGRVCSAPEVCPPNHDCSSAAVCEPLGGMKYQCVCIQGYVDQSPGSQK 2450
2451 GRVCVRNNACHDPRLNTCSRNAICYDEPRGYRCECKRGFMDRSPDSSQRG 2500
2501 RVCEPPPPPSPPPRHPCQDPERNDCHPAGTCRATGAQSYTCECLSGYADR 2550
2551 SPDPRNKPGRLCVLTEPVCLDPEQNDCHAAAICSEVNGPEKYTCKCRDGY 2600
2601 TDESPDPLRRPGRICKGLINECLDRSLNDCHSLAVCKDLPNGYTCQCPIN 2650
2651 AKDQSPDPRKPGRICSLSVNECANPSLNSCSAFADCFDEENGYRCRCRNG 2700
2701 YHDDDPAHPGHRCSFMINECDSSNLNDCDRNANCIDTAGGYDCACKAPYR 2750
2751 DEGPPQSPGRICRLNECLNPNRNTCDRNADCRDLDYGYTCTCRHGFYDQS 2800
2801 PNPQEPGRICIEFQQEEHIERVKVTTVQSEPRREFPCGRDDCIKARGEVC 2850
2851 ISGEYCGCKPGEGRSASTGKCQEVQETPFELRVVTRDQRPLMYSTEFGSQ 2900
2901 KSPSYVEIVELFEKNMARTFGGTSLAPRYVNTKVDYITHPKTKNSSWDQG 2950
2951 LLFKYEVQTTKSQSQPIDECELWKQMQASLDRTNGAIGGGSLRVASDTDL 3000
3001 LNPCKQQEEWGNCGGMSCKEHLKEVCIAGHICGCPDGMKRRDANSECRVV 3050
3051 ESWNVPLWVVRDKEKPIVFSESFDNPQTPVYKDYSKRLEKGIEGCYPHTE 3100
3101 LKNAFVTAEVNDIVNPVLMNASYDTGLLFNTTVHFRKGMVHVPSDAYYQL 3150
3151 IKYVTKENNNEVGDSELYLNPTQPDPFNPCFKNDCDPHGKCIEISKYAYK 3200
3201 CECGVGYRDINPQSPGKKCLPVHGFNECERKEDNECSENARCIDLEHLYK 3250
3251 CECLPSYYDTSPVGSVPGSLCVLDYCSDVNFCPTNTTCKNMEQQAECKCD 3300
3301 AGFMDIRKSEKRTALMLGDDTLCMHVRDVDECALGLNNCSGVAHCIDRAV 3350
3351 GYTCKCPDGYIDGNPDEPGRVCGALLCDLCNAHGDCVHNTATNNITCVCT 3400
3401 DGWTGPQCQVAPSNASLVLLILLALLFLLLTLCCLLYFCTKCHCFKGRRF 3450
3451 AGAGANGFGYRRGGAWPWSTLEGSASSESGAEFSAMSAAGNDYYPDIGIP 3500
3501 RAKLKSGMMASGNTAEVRNMEVARLDQYLDENAVRIPRAHLVDAHGDTSF 3550
3551 DSLSEASSEYTIKEEIERKVITDVTTKEIKTTTTTDEQGNTIVTTTEAVH 3600
3601 PRDTTIVHGGGYTQNESSSSSFSGGERAYQSQSQQQQSMSQGMSQSMSQH 3650
3651 ATSAGYSSSGMESSAHNSGYASIRHTGERERGGSEEEFSIGRARGMAAAS 3700
3701 SGYQHSSSENREVEEYCSEEEDVEHSVGDKRTIVTKNHSYEPFVNGESER 3750
3751 FKTEVVTSQTSTHVTKK 3767
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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