 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P23098 from www.uniprot.org...
The NucPred score for your sequence is 0.58 (see score help below)
1 MADVVDPRLEFISEYILKSYKLKPDKWAKCINVEENKILMLEFLEKADNP 50
51 QLVFTVNAAGLITPSYEFPSALKNTKAIYFIKKGREPVGKDNIKTTLVYG 100
101 DLSYTPLEQLSALVDEILVPLLANPRNHEQWPVVVSQDVLRHVHNLKSSV 150
151 YVVAGQVKGKTLLPLPVGSEKVETAAESEEKDDSYDRSLVHAIESVIIDW 200
201 THQIRDVLKRDSAQPLLEGLNPGPMVEINFWKAKCENLDCIFQQLRDPKV 250
251 RKMKELLERTQSSYLPSFNNIERDVEAALTEAQDINCYLKPLIYQVESLD 300
301 ELEFPDMTPRLAPILHTVCLIWSNSDYYNTAPRIIVLLQEICNLLIDLCR 350
351 TFLDPSEIFKLEPEESLEKVRGALAVLKAWRDLYDEHRAKLKDYFKDGRE 400
401 VKGWEFASPLVFTRMDNFTSRIETIQSLFETNVEFSKLEKTEMGSMKGRM 450
451 LSQQVEKIHEEFQECAKVFTERPYDGLDPQCQEFLDDYEEFEKKVFDLDR 500
501 RLGSILCQGFDDCCGLEAVFKMLDCYGPLLDRPVIRNDFECKYPVVLMLY 550
551 DLELDQAKEIYDEHMRVEENDGNAPLNKNMPDVAGQLKWSAQLRDRISKP 600
601 MGSLKHMEHPTGVRRILESEDAKVVFQKYEEMINLLNKYEQKVFENWTKG 650
651 VDEVCKTNLDQSLITRDESTKLIKINFDPKLVAVLREVKYLQIRGEESIP 700
701 ESAASIYEKHETLRKYVANLDLTQAWYNKVRNTVLEVEFPLIEGQLADLD 750
751 TRLKQAESELNWTSDSVWEYIQETRDQVHDLEKRVQQTKDNVDRIKKIMA 800
801 EWTKQPLFERKELKKESLLALDDRQDRLKKRYAEISTAGEKIHSLIKENL 850
851 GLFKADASSDIWKAYVDYVDDMVIDGFFNCIHCTLSYLLENTDPRHCAAP 900
901 LFEARLELQVPDMIFNPSLDYGVADGFYDLVEMLISDTYKMASLVSRLAE 950
951 HNGQEHYQADLEGMDDLSDVRNDLMDRVQTIMTKAQEYRNSFDNYAYLYV 1000
1001 DDRKEFMRQFLLYNHVLTTEEIEAHAEDGVPECPPTLDQFKEQVDTYEKI 1050
1051 YSEADEIEPEQVFDAWFRVDSKPFKAALLNIIKKWSFMFKQHLIDHVTNS 1100
1101 LSELQEFIKVGNSGLTKTVEEGDYNGLVECMGHLMAVKERQAATDEMFEP 1150
1151 IKQTIELLKTYDQEMSEEVHTQLQELPEQWNNTKKIAITVKQQVAPLQAN 1200
1201 EVAIIRRKCTSFDVRQHEFRERFRKEAPFIFTFEGPYQCLDRCHSEIYEM 1250
1251 EEHMANLQESAGLFEVNMPDYKQLKACRREVRLLKALWDLIMIVRTSIED 1300
1301 WKTTPWLEINVEQMEMDCKKFAKDIRSLDKEMRAWDAYNGLDATVKNMLT 1350
1351 SLRAVSELQNPAIRERHWQQLMAATKVKFTMDKETTLSDLLALNLHNFED 1400
1401 EVRNIVDKAVKEMGMEKVLKELNTTWSSMDFEYEPHSRTGISLLKSNEEL 1450
1451 IETLEDNQVQLQNLMTSKHIAHFLEEVSGWQKKLSTTDSVITIWFEVQRT 1500
1501 WSHLESIFIGSEDIRNQLPEDSKRFDGIDTDFKELASEMEKTPNVVEATN 1550
1551 RARLYDRLEAIQGSLVVCEKALAEYLETKRLAFPRFYFVSSADLLDILSQ 1600
1601 GNNPSQVQRHLSKLFDNMAKLKFKQDDEGNDTKLALGMYSKEGEYVDFDK 1650
1651 ECECTGQVEVWLNRVMDAMRSTVRSQFADAVVSYEEKPREQWLYDYPAQV 1700
1701 ALATTQVWWTTEVNISFARLEEGHENSMKDYNKKQIQQLNTLIGLLIGKL 1750
1751 TKGDRQKIMTICTIDVHARDVVAMMVLKKVDNAQAFQWLSQLRHRWADDD 1800
1801 KHCYANICDAQFKYSYEYLGNTPRLVITPLTDRCYITLTQSLHLVMSGAP 1850
1851 AGPAGTGKTETTKDLGRALGIMVYVFNCSEQMDYKSCGNIYKGLSQTGAW 1900
1901 GCFDEFNRISVEVLSVVAVQVKCVQDAIRDKKERFNFMGEEISLIPSVGI 1950
1951 FITMNPGYAGRTELPENLKALFRPCAMVVPDFELICEIMLVAEGFLDARL 2000
2001 LARKFITLYTLCKELLSKQDHYDWGLRAIKSVLVVAGSLKRGDPQRPEDQ 2050
2051 VLMRALRDFNVPKIVSDDTPVFMGLIGDLFPALDVPRRRDMDFEKVVKQS 2100
2101 TLDLKLQAEDSFVLKVVQLEELLAVRHSVFVIGNAGTGKSQVLKVLNKTY 2150
2151 SNMKRKPVLVDLNPKAVTNDELFGIINPATREWKDGLFSVIMRDMSNITH 2200
2201 DGPKWIVLDGDIDPMWIESLNTVMDDNKVLTLASNERIPLTPSMRLLFEI 2250
2251 SHLKTATPATVSRAGILYINPSDLGWNPIVTSWIDTREVQSERANLTILF 2300
2301 DKYLPTLLDTLRVRFKKIIPIPEQSMVQMLCYLLECLLTPENTPADCPKE 2350
2351 LYELYFVFASIWAFGGSMFQDQLVDYRVEFSKWWITEFKTIKFPNQGTVF 2400
2401 DYFIDQESKKFLPWSEKVPVFELDPEIPMQAVLVHTNETTRVRFFMDLLM 2450
2451 ERGRPVMLVGNAGLGKSVLVGDKLSNLGEDSMVANVPFNYYTTSEMLQRV 2500
2501 LEKPLEKKAGRNYGPPGTKKLVYFIDDMNMPEVDTYGTVQPHTLIRQHMD 2550
2551 YKHWYDRQKLTLKEIHKCQYVSCMNPTAGSFTINSRLQRHFCVFALSFPG 2600
2601 QDALSTIYNSILSQHLANISVSNALQKLSPTVVSATLDLHKKVAQSFLPT 2650
2651 AIKFHYVFNLRDLSNVFQGLLYSGPDLLKAPIDFARLWMHECQRVYGDKM 2700
2701 INDQDIEAFEKLVLEYAKKFFEDVDEEALKAKPNIHCHFATGIGDPKYMQ 2750
2751 VPNWPELNKILVEALDTYNEINAVMNLVLFEDAMQHVCRINRILESPRGN 2800
2801 ALLVGVGGSGKQSLARLASYISSLEVFQITLRKGYGIPDLKLDLATVCMK 2850
2851 AGLKNIGTVFLMTDAQVSDEKFLVLINDLLASGEIPDLFADDEVENIIGG 2900
2901 VRNEVKGMGLQDTRENCWKFFIDRLRRQLKTVLCFSPVGTTLRVRSRKFP 2950
2951 AVVNCTSIDWFHEWPQEALVSVSMRFLDEVELLKGDIKKSIAEFMAYVHV 3000
3001 SVNESSKLYLTNERRYNYTTPKSFLEQIKLYESLLSMKSKELTAKMERLE 3050
3051 NGLTKLQSTAQQVDDLKAKLASQEVELAQKNEDADKLIQVVGVETEKVSK 3100
3101 EKAIADDEEKKVAIINEEVSKKAKDCSEDLAKAEPALLAAQEALNTLNKN 3150
3151 NLTELKSFGSPPSAVLKVAAAVMVLLAPNGKIPKDRSWKAAKVVMNKVDA 3200
3201 FLDSLINYDKENIHENCLKSIKEYLNDPEFEPEYIKGKSLAAGGLCSWVV 3250
3251 NIVKFYNVYCDVEPKRIALQKANDELKAAQDKLALIKAKIAELDANLAEL 3300
3301 TAQFEKATSDKLKCQQEAEATSRTITLANRLVGGLASENVRWGEAVANFK 3350
3351 IQEKTLPGDVLLITAFVSYIGCFTKTYRVDLQERMWLPFLKSQKDPIPIT 3400
3401 EGLDVLSMLTDDADIAVWNNEGLPSDRMSTENATILSNCQRWPLMIDPQL 3450
3451 QGIKWIKQKYGDDLRVIRIGQRGYLDTIENAISSGDTVLIENMEESIDPV 3500
3501 LDPVLGRNTIKKGRYIKIGDKEVEYNPDFRLILQTKLANPHYKPEMQAQT 3550
3551 TLINFTVTRDGLEDQLLANVVAQERPDLEKLKSDLTKQQNDFKIILKELE 3600
3601 DNLLSRLSSAEGNFLGDTALVENLETTKRTAAEISVKVEEAKVTEVKINE 3650
3651 ARELYRPAAARASLLYFILNDLNKINPIYQFSLKAFNTVFSLPIARAEPC 3700
3701 EDVKERVVNLIDCITYSVFIYTTRGLFEADKLIFTTQVAFQVLLMKKEIA 3750
3751 QNELDFLLRFPIQVGLTSPVDFLTNSAWGAIKSLSAMEDFRNLDRDIEGS 3800
3801 AKRWKKFVESECPEKEKFPQEWKNKSALQKLCMMRALRADRMSYAVRNFI 3850
3851 EEKLGSKYVEGRQVEFAKSYEETDPATPVFFILSPGVDPLKDVEALGKKL 3900
3901 GFTFDNNNFHNVSLGQGQEIVAEQCMDLAAKEGHWVILQNIHLVAKWLST 3950
3951 LEKKLEQYSVGSHDSYRVYMSAEPAGSPEAHIIPQGILESSIKITNEPPT 4000
4001 GMFANLHKALYNFNQDTLEMCAREAEFKVILFALCYFHAVVCERQKFGPQ 4050
4051 GWNRSYPFNTGDLTISVNVLYNYLEANSKVPWQDLRYLFGEIMYGGHITD 4100
4101 DWDRRLCRTYLEEYMAPEMLDGELYLAPGFPVPPNSDYKGYHQYIDEILP 4150
4151 PESPYLYGLHPNAEIGFLTTESDNLFKIVLELQPRDAGGGGGGGSSREEK 4200
4201 IKSLLDEIVEKLPEEFNMMEIMAKVEDRTPYVVVAFQECERMNMLTSEIR 4250
4251 RSLKELDLGLKGELTITPDMEDLSNALFLDQIPATWVKRAYPSLFGLTSW 4300
4301 YADLLQRIKELEQWTADFALPNVVWLGGFFNPQSFLTAIMQSMARKNEWP 4350
4351 LDKMCLQCDVTKKNKEDFSSAPREGSYVHGLFMEGARWDTQTNMIADARL 4400
4401 KELAPNMPVIFIKAIPVDKQDTRNIYECPVYKTKQRGPTFVWTFNLKSKE 4450
4451 KAAKWTLAGVALLLQV 4466
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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