 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P45443 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MMDSVPSPPPQPSPDANGVATTPFAAVDPVKVVDHLVLLLEATLGAKRDE 50
51 LEAPGSLLSKVRYSDTVQRCSRFALDTQVALYIQKDLAPTTTLDGDNGAE 100
101 AEEPEPTHVYTISSDLTSSPTTVAYLVLLKRPQPLDPIVPLTSQIQMLNL 150
151 PGPAYLSTSGSEQGPTSSPYEILQLYLHNGLAPYFDASTKSQQLLNGARG 200
201 RPDVDAKTGIPVTKKRWTELELSLSHLQQNVEIPEVSLPFHPLVQSTLEE 250
251 AATKNVKPSIDLLPATVLADSTFLNNLQATVNNWIKSIQVITKMTRDPTT 300
301 GTANQEINFWLSMEAALEGIENQLRSEGVMLTLDILKHAKRFQATVSFTA 350
351 DTGLKEAMEKVQKYNQLMRDFPLDELLSATTLTKVQESIGQIFGHLNKKL 400
401 RICPYPIRRALPLVEAISGDLDEVLHRLLPGTELVKLDYEEFKGVMKQAG 450
451 SIFRAWDESIKEFTNVAREVTRRRNEKFIPIKINPRHAELQSRLDYVHNF 500
501 RDNHEQLQRTIINVLGPKATVNGIVTASGANGVAVVEEIGDVDAVDEVKQ 550
551 AWEALKDVDLLDCTREGTEKWVRAENIYNERTARVENSIIARLRDRLATA 600
601 KNANEMFRVFSKFNALFVRPKIRGAIAEYQTQLIDNVKQAISSLHERFKQ 650
651 QYGHSEAHAMAQLHDLPPVSGAIIWARQIERQLDQYMKKVEQVLGSDWAL 700
701 HTEGQKLQNESDLFRKKLDTRPIFEAWLHDVQRKQISISGLLFTINRIRS 750
751 AGNILELAVNFDAQVIALFKETRNLLWLNYPVPHSVNNVAKEAKRVYPFA 800
801 VSLMESVRTFAQTNRQISDMSEVAVLLSGHRNDVYTLISKGIPLRWETFV 850
851 NTYEVHFKPTFNPNTPLGQTGSKVSETKHVMFIREFAASVSLLQSKTLLL 900
901 ANIYVTVQKALNELKTCPYEASAFQSRLETIQHAVDQLNLEQYVNLGYWV 950
951 ERMNRQIKDVLYTRLQVAIQAWIQAFEDEDVERPSERKRLLEIASPDAAK 1000
1001 SIGPVIKSLVHEITMRNQVIYLDPPLEYARASWFAQLQDWIGVICNLKKI 1050
1051 KATRYTMSLSTEVVDEPRFNDLPGDCTEELLRVQTSVEKKIREIGAYVDK 1100
1101 WLQFQSLWDLQSEHVYDVLGDQLSRWLQLLQEIRKTRQTFDTTEVSRSFG 1150
1151 HITIDYDQVQTKVNAKYDQWQQDILIKFASRLGNRMREVYAELEKARKDL 1200
1201 EGQAMTANSTAEAVRFITIVQSCTRQVKLWAPEIETFRQGESTLVRQRYH 1250
1251 FQNDWLHAEQVDGMWDMLNELLARKSKIVTDQSDALRAKITAEDKVVNDK 1300
1301 IAEIAHQWNEEKPVSGTIAPDVASATLTHFEQRITKLQEESAMVAKAKEA 1350
1351 LDLAPTPDTSLGVILEEVQDFKSVWASLSTIWKNLNELRETLWNSVQPRK 1400
1401 IRASIDNLIKMTKEMPSRMRQYAAFEHIQNVLRQLMKVNSILGELKSEAV 1450
1451 RDRHWTKIYKQIKPGKRYSPVSMTLGDVWDLNLVATEVIVKDIIIQAQGE 1500
1501 MALEEFLKQVRETWTNYGLELVQYQQKCRLIRGWDDLFAKCSENLNSLQA 1550
1551 MKHSPYYKEFEEEASSWEEKLNRVHVLFDIWIDVQRQWVYLEGVFHGNAD 1600
1601 IKHLLPIESSRFQNINSEFLAVMKKVYKQPNVLDVLNIPNVQKSLERLAE 1650
1651 LLNKIQKALGEYLEKERVSFPRFYFVGDEDLLEMIGNSNDTMRIAKHFKK 1700
1701 MFAGLNGLVMDDEGVISGFTSKEGETVRLKKEINLVKTPRINDWLALLEN 1750
1751 GMKVTLAELLAEAVDEFTPIFSSENVDRDALIKFMNTYPSQIVVLATQVV 1800
1801 WTTAVDQALADGGKDLQLLFDREVQVLRMLADTVLGDLEVLLRKKCEQLI 1850
1851 TECVHQRDVIEKLVKLNANSNTHYMWLLQMRYVYNPEGDFLQRLHIKMAN 1900
1901 AKLNYGFEYLGVPDRLVRTPLTDRCFLTLTQALCQRLGGSPYGPAGTGKT 1950
1951 ESVKALGLQLGRFTLVFCCDDTFDNQAMGRIFLGICQVGAWGCFDEFNRL 2000
2001 EEKILSAVSQQIQDIQLGLKMGAEDEKAQIELDGRQIHVNANAGIFITMN 2050
2051 PGYAGRSNLPDNLKKLFRSVAMSKPDKELIAEVMLYSQGFNQAKQLSKHT 2100
2101 VPFFDQCSEKLSKQAHYDFGLRALKSVLVSSGGLKRARLLETGDAESLGP 2150
2151 EDVVEPEIIVQSIRETIAPKLIKSDVEIMMEIESVCFPGVKYVPASLEKL 2200
2201 QEAIRRLAAERQLVVNDIWMTKVLQLYQIQKIHHGVMMVGNSGSGKSAAW 2250
2251 RLLLDALQQTENVEGVSHVIDSKVMSKEALYGNLDSTTREWTDGLFTSIL 2300
2301 RKIVDNLRGEDAKRHWIVFDGDVDPEWVENLNSVLDDNKLLTLPNGERLN 2350
2351 LPPNVRIMFEVENLKYATLATVSRCGMVWFSEDTVTPDMMVSNYIETLRT 2400
2401 VAFEDLDEDAVATGQSSAKALAVQSQAADLLQEFLTRDNLINEVLKEAAN 2450
2451 YEHIMEFTVARVLSTLFSLLNKAVRDIIEYNSAHVDFPMDPEQVEGYIAK 2500
2501 KVLLALVWALTGDCPLKDRKAFGDKVAGLASFGSPPLDGTSSLIDFTVTM 2550
2551 PQGEWQTWQQHVPTIEVNTHSVTQTDVVIPTLDTIRHEDVLYSWLAEHKP 2600
2601 LLLCGPPGSGKTMTLFSALRKLPNMEVVGLNFSSATTPDLLIKTFEQYCE 2650
2651 YKKTLNGVMLSPTQIGRWLVIFCDEINLPAPDKYGTQRAISFLRQLVEHN 2700
2701 GFWRTSDKAWVTLDRIQFVGACNPPTDAGRTPMGARFLRHAPLIMVDYPG 2750
2751 ELSLMQIYGSFNAAVLKVIPSLRGYAEALTQAMVRFYLESQERFTPKIQP 2800
2801 HYVYSPRELTRWVRGVYEAIRPLETLSVEGLIRIWAHEALRLFQDRLVDE 2850
2851 EERKWTDDAVRRIAMEYFPTIDEHKALGGPILFSNWLSKNYVPVDREQLR 2900
2901 DFVKARLKTFCEEEVDVPLILFNDVLEHVLRIDRVFRQPQGHLILIGVSG 2950
2951 SGKTTLSRFVAWMNGLKVFQIKVHGKYSAEDFDEDLREVLRRCGCKGEKI 3000
3001 CFIMDESNVLDSGFLERMNTLLANAEVPGLFEGDDLAALMTACKEGAQRQ 3050
3051 GLLLDSQEELYKWFTGQIVKNLHVVFTMNPPGEDGLSSKAATSPALFNRC 3100
3101 VLNWFGDWSDQALFQVAHELTHSVDLDRPNWTAPDTIPVAYRGLNLPPSH 3150
3151 REAVVNAMVYIHYSLQRFNAKLLKQQGKITFLTPRHFLDFVAQYVKLYNE 3200
3201 KREDLEEQQRHLNVGLEKLRDTVDKVRDLRVTLSEKKAQLEQKDAEANEK 3250
3251 LQRMVADQREAEQRKNISLEIQAALEKQEAEVASRKKVVLEDLARAEPAV 3300
3301 EEAKASVSSIKRQHLTEVRSMPTPPSGVKLALESVCTLIGHKANDWKTIQ 3350
3351 GIVRRDDFIASIVNFNNEKQMTKSLRVKMRNEFLANPEFTFEKVNRASKA 3400
3401 CGPLVQWVEAQVNYAEILDRVGPLREEVMLLEEQALQTKAEAKAVEQTIS 3450
3451 TLENSIARYKTEYAALISETQAIKAEMSRVQFKVDRSVKLLDSLSSERTR 3500
3501 WEEGSRSFETQISTLVGDVLVAAAFLAYSGLYDQTFRKSMMEDWLHQLHL 3550
3551 SGVQFKQHNPMTEYLSTADERLSWQENTLPVDDLCTENAIILKRFNRYPL 3600
3601 IIDPSGRATEFLNRESKDRKLTVTSFLDDSFTKVLESSLRFGNPILIQDA 3650
3651 EHLDPVLNHVLNKEYQKTGGRVLIQLGKQQIDFSPAFKLYLSTRDPSATF 3700
3701 APDICSRTTFVNFTVTQSSLQTQSLNEVLKSERPDVDERRSNLIKLQGEF 3750
3751 KVHLRQLEKKLLQALNESRGNILDDDHVIETLETLKTEAAEISAKMSNTE 3800
3801 GVMAEVEQITLQYNIIARSCSAVFAVLEQLHYLNHFYRFSLQYFLDIFHS 3850
3851 VLRGNPHLANETNHNVRRDIIVKDLFVATFKRTALGLLQKDRITLAMLLA 3900
3901 QASPYKMDKGLLDIILDERIEGKDVSIDQNTREEAFARAKKIPALKNKID 3950
3951 AVPEADWEKFFTEELAEDFVPKIWNDETEPNDRALMSLLLVKLFRLDRFV 4000
4001 PAAERFVTLVFGSDLFDIVEDLKQTVDQVSAILPIALVSSPGFDASYKVD 4050
4051 GLVERMRVRCTNIAMGSAEAEGSADKAIANAAQTGSWVLIKNVHLAPGWL 4100
4101 QGVEKKMETLNPNPEFRLFLSMESSPKIPVNLLRASRVLMYEQPAGVRAN 4150
4151 MKDSMSSISTRSLKSPVERTRLYLLLSFLHAVVQERLRYAPNLGWKGFWE 4200
4201 FNDADYECSAHVIDTWIDTAAHGRTNIAPSNIPWEMIRYLIVETYGGKID 4250
4251 DENDFKMLNQLVHTFLTPSAFDIGHKLVEVSHDAEDEQKDAATGGDLVVP 4300
4301 SGTSLQEFMSWIQKLPEREPPTYLGLPANAEKLLLVGLGKSLIGNLKKVT 4350
4351 DLLDEGEAIMAEASEAA 4367
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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