 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98157 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MGPLLALAGCLLALLAAPAARALEAPKTCSPKQFACKDQITCISKGWRCD 50
51 GEKDCPDGSDESPDICPQSKVSRCQPNEHNCLGTELCIHMSKLCNGLHDC 100
101 FDGSDEGPHCREQLANCTALGCQHHCVPTLSGPACYCNNSFQLAEDRRSC 150
151 KDFDECTVYGTCSQTCTNTEGSYTCSCVEGYLLQPDNRSCKAKNEPVDRP 200
201 PVLLIANSQNILATYLSGAPVPNITPTSAKQTTAMDFNYIEDTVCWVHVG 250
251 DSASQTILKCAKIPNLKGFVEERSINISLSLHQVEQMAIDWLTGNFYFVD 300
301 DIDDRIFVCNKNGLTCVTLLDLELYNPKGIALDPAMGKVFFTDYGQIPKV 350
351 ERCDMDGQNRTKLVDSKIVFPHGITLDLVSRLVYWADAYLDYIEVVDYEG 400
401 KNRHTIIQGILIEHLYGLTVFENYLYATNSDNANAQQKTSVIRVNRFNST 450
451 EYQVVTRVDKGGALHIYHQRRQPTVRSHACEPDQFGKPGGCSDICLLGNS 500
501 HKSRTCRCRSGFSLGSDGKSCKKPEHELFLVYGKGRPGIIRGMDMGAKVP 550
551 DEHMIPIENLMNPRALDFHAETGFIYFADTTSYLIGRQKIDGTERETILK 600
601 DGIHNVEGIAVDWMGNNLYWTDDGPKKTISVARLEKAAQTRKTLIEGKMT 650
651 HPRAIVVDPLNGWMYWTDWEEDPKDSKRGKIERAWMDGSNRNVFITSKTV 700
701 LWPNGLSLDIPAKILYWVDAFYDRIEMVYLNGTERKIVYEGPELNHAFGL 750
751 CHYSSFLFWTEYRSGSIYRLDQSSKAVSLLRNERPPIFEIRMYDAQQQQV 800
801 GSNKCRVNNGGCSSLCLATPRGRQCACAEDQILGADSVTCEANPSYIPPP 850
851 QCQPGEFACKNNRCIQERWKCDGDNDCLDNSDEAPELCHQHTCPSDRFKC 900
901 KNNRCIPNRWLCDGDNDCGNNEDESNSTCSARTCSPNQFSCASGRCIPIS 950
951 WTCDLDDDCGDRSDESASCAYPTCFPLTQFTCNNGRCININWRCDNDNDC 1000
1001 GDNSDEAGCSHSCSSNQFKCNSGRCIPVHWTCDGDNDCGDYSDETHANCT 1050
1051 NQATRPPGGCHTDEFQCRLDGLCIPMRWRCDGDTDCMDSSDEKNCEGVTH 1100
1101 VCDPNVKFGCKDSARCISKAWVCDGDSDCEDNSDEENCESLVCKPPSHTC 1150
1151 ANNTSICLPPEKLCDGSDDCGDGSDEGELCDQCSLNNGGCSHNCTVAPGE 1200
1201 GIVCSCPLGMELGADNKTCQIQSYCAKHLKCSQKCEQDKYNVKCSCYEGW 1250
1251 MLEPDGESCRSLDPFKPFIIFSNRHEIRRIDLHRGDYSVLVPGLRNTIAL 1300
1301 DFHLNQSSLYWTDVVEDKIYRGKLLENGALTSFEVVIQYGLATPEGLAVD 1350
1351 WIAGNIYWVESNLDQIEVAKLDGTMRTTLLAGDIEHPRAIALDPRYGILF 1400
1401 WTDWDASLPRIEAASMSGAGRRTIHKETGSGGWPNGLTVDYLEKRILWID 1450
1451 ARSDAIYSALYDGTGHIEVLRGHEYLSHPFAVTLYGGEVYWTDWRTNTLA 1500
1501 KANKWTGHNVTVVQRTNTQPFDLQVYHPSRQPLAPNPCEANGGKGPCSHL 1550
1551 CLINYNRTLSCACPHLMKLDKDNTTCYEFKKFLLYARQMEIRGVDIDNPY 1600
1601 YNYIISFTVPDIDNVTVVDYDAVEQRIYWSDVRTQTIKRAFINGTGVETV 1650
1651 VSADLPNAHGLSVDWVSRNLFWTSYDTNKKQINVARLDGSFKNAVIQGLD 1700
1701 KPHCLVVHPLHGKLYWTDGDNISVANMDGSNRTLLFTNQRGPVGLAIDYP 1750
1751 ESKLYWISSGNGTINRCNLDGSDLEVIVAVKSQLSKATALAIMGDKLWWA 1800
1801 DQASERMGTCNKKDGTEVTVLRNSTTLVMLMKVYDESIQQAGSNPCSVNN 1850
1851 GDCSQLCLPTSETSRSCMCTAGYSLKSGQQSCEGVGSFLLYSVHEGIRGI 1900
1901 PLDPNDKSDALVPVSGTSLAVGIDFHAENDTIYWVDMGLSTISRAKRDQT 1950
1951 WREDVVTNGIGRVEGIAVDWIAGNIYWTDQGFDVIEVARLNGSFRYVVIS 2000
2001 QGLDKPRAITVHPEKGYLFWTEWGQYPRIERSRLDGTERMVLVNVSISWP 2050
2051 NGISVDYEDGKLYWCDARTDKIERIDLETGENREVVLSSDNMDMFSVSVF 2100
2101 EDYIYWSDRTHANGSIKRGSKDNATESVSLRTGIGVQLKDIKVFNRARQK 2150
2151 GTNVCAQNNGGCQQLCLFRGGGRRTCACAHGMLSEDGVSCRDYDGYLLYS 2200
2201 ERTILKSIHLSDENNLNAPIKPFEDAEHMKNVIALAFDYRYGTKGSNRIF 2250
2251 YSDIHFGNIQQINDDGTGRRTIVENVGSVEGLAYHRGWDTLYWTSYTTST 2300
2301 ITRHTVDQSRLGAFERETVITMSGDDHPRAFVLDECQNLMFWTNWNEQHP 2350
2351 SIMRATLSGANVLIIIDQDIRTPNGLAIDHRAEKIYFSDATLDKIERCEY 2400
2401 DGSHRHVILKSEPVHPFGLAVYGDYIFWTDWVRRAVQRANKYVGTDMKLL 2450
2451 RVDIPQQPMGIIAVANDTDSCELSPCRVNNGGCQDLCLLTPKGHVNCSCR 2500
2501 GERVLQEDFTCKALNSTCNVHDEFECGNGDCIDFSRTCDGVVHCKDKSDE 2550
2551 KQSYCSSRKCKKGFLHCMNGRCVASRFWCNGVDDCGDNSDEVPCNKTSCA 2600
2601 ATEFRCRDGSCIGNSSRCNQFIDCEDASDEMNCTATDCSSYFKLGVKGTT 2650
2651 FQKCEHTSLCYAPSWVCDGANDCGDYSDERNCPGGRKPKCPANYFACPSG 2700
2701 RCIPMTWTCDKEDDCENGEDETHCSERQDKFCYPVQFECNNHRCISKLWV 2750
2751 CDGADDCGDGSDEDSRCRLTTCSTGSFQCPGTYVCVPERWLCDGDKDCAD 2800
2801 GADETLAAGCLYNNTCDEREFMCGNRQCIPKHFVCDHDDDCGDGSDESPE 2850
2851 CEYPTCGPHEFRCANGRCLSNSQWECDGEFDCHDHSDEAPKNPRCSSPES 2900
2901 KCNDSFFMCKNGKCIPEALLCDNNNDCADGSDELNCFINECLNKKLSGCS 2950
2951 QECEDLKIGYKCRCRPGFRLKDDGKTCIDIDECSTTYPCSQKCINTLGSY 3000
3001 KCLCIEGYKLKPDNPTSCKAVTDEEPFLIFANRYYLRKLNLDGSNYTLLK 3050
3051 QGLNNAVALDFDYREQMIYWTDVTTQGSMIRRMHINGSNVQVLHRTGLSN 3100
3101 PDGLAVDWVGGNLYWCDKGRDTIEVSKLNGAYRTVLVNSGLREPRALVVD 3150
3151 VQNGYLYWTDWGDHSLIGKIGMDGTNRSVIVDTKITWPNGLTLDYINSRI 3200
3201 YWADAREDYIEFASLDGSNRHTVLSQDIPHIFALTLFEDYIYWTDWETKS 3250
3251 INRAHKTTGANKTLLISTLHRPMDIHIYHPYRQPDVPNHPCKTNNAGCSN 3300
3301 LCLLSPGGGHKCACPTNFYLGSDGKTCVSNCTASQFVCKNDKCIPFWWKC 3350
3351 DTEDDCGDRSDEPEDCPEFKCRPGQFQCSTGICTNPAFICDGDNDCQDNS 3400
3401 DEANCDIHVCLPSQFKCTNTNRCIPGIFRCNGQDNCGDGEDEKDCPEVTC 3450
3451 APNQFQCAITKRCIPRVWVCDRDNDCVDGSDEPANCTQMTCGVDEFRCKD 3500
3501 SGRCIPARWKCDGEDDCGDGSDEPKEECDERTCEPYQFRCKNNRCVPGRW 3550
3551 QCDYDNDCGDNSDEESCTPRPCSESEFSCANGRCIAGRWKCDGDHDCADG 3600
3601 SDEKDCIPRCEFDQYQCKNGHCIPMRWRCDADADCMDGTDEEDCGTGVRT 3650
3651 CPLDEFQCNNTLRKPLAWKCDGEDDCGDNSDENPEECLKFQCPPNRPFRC 3700
3701 KNDRVCLWIGRQCDGIDNCGDNTDEKDCESPTAKPKSCSQDKNEFLCENK 3750
3751 KCISANLRCNFFDDCGDGSDEKSCSHEHKSYDCMTNTTMCGDEAQCIQAQ 3800
3801 SSTYCTCRRGFQKVPDKNSCQDVNECLRFGTCSQLCNNTKGSHVCSCAKN 3850
3851 FMKTDNMCKAEGSEHQILYIADDNKIRSMYPFNPNSAYEPAFQGDENVRI 3900
3901 DAMDIYVKGNKIYWTNWHTGRISYCELPASSAASTASNRNRRQIDGGVTH 3950
3951 LNISGLKMPRGIAVDWVAGNIYWTDSGRDVIEVAQMKGENRKTLISGMID 4000
4001 EPHAIVVDPLRGTMYWSDWGNHPKIETAAMDGTLRETLVQDNIQWPTGLA 4050
4051 VDYHNERLYWADAKLSVIGSIRLNGTDPVVAIDNKKGLSHPFSIDIFEDY 4100
4101 IYGVTYINNRIFKIHKFGHKSVTNLTSGLNHATDVVLYHQYKQPEVTNPC 4150
4151 DRKKCEWLCLLSPSGPVCTCPNGKRLDNGTCVLIPSPTASAVVPTTDTCD 4200
4201 LVCLNGGSCFLNARKQAKCRCQPRYNGERCQINQCSDYCQNGGLCTASPS 4250
4251 GMPTCRCPTGFTGSRCDQQVCTNYCHNNGSCTVNQGNQPNCRCPPTFIGD 4300
4301 RCQYQQCFNYCENNGVCQMSRDGVKQCRCPPQFEGAQCQDNKCSRCQEGK 4350
4351 CNINRQSGDVSCICPDGKIAPSCLTCDSYCLNGGTCSISDKTQLPECLCP 4400
4401 LEVTGMRCEEFIVGEQQSGRTASIVIPILLLLLLLAVVAFAWYKWRIKGA 4450
4451 KGFQHQRMTNGAMNVEIGNPTYKMYEGEPDDDVGELLDADFALDPDKPTN 4500
4501 FTNPVYATLYMGAHSSRNSLASTDEKRELLARGADDDLTDPLA 4543
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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