 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96JG9 from www.uniprot.org...
The NucPred score for your sequence is 0.93 (see score help below)
1 MPGERPRGAPPPTMTGDLQPRQVASSPGHPSQPPLEDNTPATRTTKGARE 50
51 AGGQAQAMELPEAQPRQARDGELKPPSLRGQAPSSTPGKRGSPQTPPGRS 100
101 PLQAPSRLAGRAEGSPPQRYILGIASSRTKPTLDETPENPQLEAAQLPEV 150
151 DTPQGPGTGAPLRPGLPRTEAQPAAEELGFHRCFQEPPSSFTSTNYTSPS 200
201 ATPRPPAPGPPQSRGTSPLQPGSYPEYQASGADSWPPAAENSFPGANFGV 250
251 PPAEPEPIPKGSRPGGSPRGVSFQFPFPALHGASTKPFPADVAGHAFTNG 300
301 PLVFAFHQPQGAWPEEAVGTGPAYPLPTQPAPSPLPCYQGQPGGLNRHSD 350
351 LSGALSSPGAAHSAPRPFSDSLHKSLTKILPERPPSAQDGLGSTRGPPSS 400
401 LPQRHFPGQAYRASGVDTSPGPPDTELAAPGPPPARLPQLWDPTAAPYPT 450
451 PPGGPLAATRSMFFNGQPSPGQRLCLPQSAPLPWPQVLPTARPSPHGMEM 500
501 LSRLPFPAGGPEWQGGSQGALGTAGKTPGPREKLPAVRSSQGGSPALFTY 550
551 NGMTDPGAQPLFFGVAQPQVSPHGTPSLPPPRVVGASPSESPLPSPATNT 600
601 AGSTCSSLSPMSSSPANPSSEESQLPGPLGPSAFFHPPTHPQETGSPFPS 650
651 PEPPHSLPTHYQPEPAKAFPFPADGLGAEGAFQCLEETPFPHEGPEVGRG 700
701 GLQGFPRAPPPYPTHHFSLSSASLDQLDVLLTCRQCDRNYSSLAAFLAHR 750
751 QFCGLLLARAKDGHQRSPGPPGLPSPPAAPRVPADAHAGLLSHAKTFLLA 800
801 GDAQAEGKDDPLRTGFLPSLAATPFPLPASDLDMEDDAKLDSLITEALNG 850
851 MEYQSDNPEIDSSFIDVFADEEPSGPRGPSSGHPLKSKAGVTPESKAPPP 900
901 LPAATPDPQTPRPGDRGCPARGRPKTRSLGLAPTEADAPSQGRQQRRGKQ 950
951 LKLFRKDLDSGGAAEGSGSGGGGRASGLRPRRNDGLGERPPPRPRRPRTQ 1000
1001 APGSRADPAPRVPRAAALPEETRSSRRRRLPPRKDPRKRKARGGAWGKEL 1050
1051 ILKIVQQKNRRHRRLGRRAGRCGSLAAGRPRPGAEDRRLREYDFASESEE 1100
1101 DEQPPPRGPGFRGRRGRGEKRKEVELTQGPREDEPQKPRKAARQEAGGDG 1150
1151 APANPEEPGGSRPGPGRSPQARGPSRSLETGAAAREGGPKCADRPSVAPK 1200
1201 DPLQVPTNTETSEETRPSLDFPQEAKEPETAEESAPDSTEFTEALRSPPA 1250
1251 ACAGEMGASPGLLIPEQPPPSRHDTGTPKPSGSLANTAPHGSSPTPGVGS 1300
1301 LLGGPGGTQAPVSHNSKDPPARQPGEFLAPVANPSSTACPKPSVLSSKIS 1350
1351 SFGCDPAGFNRDPLGVPVAKKGPQPYSSPHSELFLGPKDLAGCFLEELHP 1400
1401 KPSARDAPPASSSCLCQDGEDAGSLEPQLPRSPPGTAETEPGRAASPPTL 1450
1451 ESSSLFPDLPVDRFDPPLYGSLSANRDSGLPFACADPPQKTVPSDPPYPS 1500
1501 FLLLEEVSPMLPSHFPDLSGGKVLSKTCPPERTVVPGAAPSLPGKGSGCS 1550
1551 VALMSHLSEDELEIQKLVTELESQLQRSKDTRGAPRELAEAESVGRVELG 1600
1601 TGTEPPSQRRTCQATVPHEDTFSAADLTRVGESTAHREGAESAVATVEAV 1650
1651 QGRPGGTWPCPASFHPGHAALLPCAQEDLVSGAPFSPRGANFHFQPVQKA 1700
1701 GASKTGLCQAEGDSRPPQDVCLPEPSKQPGPQLDAGSLAKCSPDQELSFP 1750
1751 KNKEAASSQESEDSLRLLPCEQRGGFLPEPGTADQPHRGAPAPEAFGSPA 1800
1801 VHLAPDLAFQGDGAPPLDATWPFGASPSHAAQGHSAGRAGGHLHPTAGRP 1850
1851 GFEGNEFAPAGASSLTAPRGREAWLVPVPSPACVSNTHPSRRSQDPALSP 1900
1901 PIRQLQLPGPGVAKSKDGILGLQELTPAAQSPPRVNPSGLEGGTVEGGKV 1950
1951 ACGPAQGSPGGVQVTTLPAVAGHQLGLEADGHWGLLGQAEKTQGQGTANQ 2000
2001 LQPENGVSPGGTDNHASVNASPKTALTGPTEGAVLLEKCKGSRAAMSLQE 2050
2051 EAEPTPSPPSPNRESLALALTAAHSRSGSEGRTPERASSPGLNKPLLATG 2100
2101 DSPAPSVGDLAACAPSPTSAAHMPCSLGPLPREDPLTSPSRAQGGLGGQL 2150
2151 PASPSCRDPPGPQQLLACSPAWAPLEEADGVQATTDTGAEDSPVAPPSLT 2200
2201 TSPCDPKEALAGCLLQGEGSPLEDPSSWPPGSVSAVTCTHSGDTPKDSTL 2250
2251 RIPEDSRKEKLWESPGRATSPPLAGAVSPSVAVRATGLSSTPTGDEAQAG 2300
2301 RGLPGPDPQSRGAPPHTNPDRMPRGHSSYSPSNTARLGHREGQAVTAVPT 2350
2351 EPPTLQGAGPDSPACLEGEMGTSSKEPEDPGTPETGRSGATKMPRVTCPS 2400
2401 TGLGLGRTTAPSSTASDFQSDSPQSHRNASHQTPQGDPLGPQDLKQRSRG 2450
2451 YKKKPASTENGQWKGQAPHGPVTCEVCAASFRSGPGLSRHKARKHRPHPG 2500
2501 APAEPSPAALPAQQPLEPLAQKCQPPRKKSHRVSGKERPNHSRGDPSHVT 2550
2551 QPPPAQGSKEVLRAPGSPHSQQLHPPSPTEHEVDVKTPASKPRPDQARED 2600
2601 ELHPKQAEKREGRRWRREPTVDSPSHSEGKSNKKRGKLRGRRLREESILP 2650
2651 VSADVISDGRGSRPSPAMASYAASPSHCLSVEGGPEADGEQPPRLATLGP 2700
2701 GVMEGAAETDQEALCAGETGAQKPPGDRMLCPGRMDGAALGEQPTGQKGA 2750
2751 SARGFWGPRETKALGVCKESGSEPAEDSSRAHSRSEEGVWEENTPPLGPL 2800
2801 GFPETSSSPADSTTSSCLQGLPDNPDTQGGVQGPEGPTPDASGSSAKDPP 2850
2851 SLFDDEVSFSQLFPPGGRLTRKRNPHVYGKRCEKPVLPLPTQPSFEEGGD 2900
2901 PTLGPARLPTDLSDSSSLCLCHEDPWEDEDPAGLPESFLLDGFLNSRVPG 2950
2951 IDPWAPGLSLWALEPSREAGAEKLPSHCPEDDRPEAIPELHMVPAAWRGL 3000
3001 EMPAPADDSSSSLGDVSPEPPSLERERCDGGLPGNTHLLPLRATDFEVLS 3050
3051 TKFEMQDLCFLGPFEDPVGLPGPSFLDFEGTASSQGPQSRRTEEAAGAGR 3100
3101 AQGRGRPAKGRRASYKCKVCFQRFRSLGELDLHKLAHTPAPPPTCYMCVE 3150
3151 RRFGSRELLRGHLQERHAQSKAGPWACGMCLKEVADVWMYNEHLREHAVR 3200
3201 FARRGQARRSLGDLPGGLEGSSAVAHLLNSITEPAPKHHRGKRSAGKAAG 3250
3251 SPGDPWGQEGEAKKDSPGERAKPRARSTPSNPDGAATPDSASATALADAG 3300
3301 SPGPPRTTPSPSPDPWAGGEPLLQATPVHEACKDPSRDCHHCGKRFPKPF 3350
3351 KLQRHLAVHSPQRVYLCPRCPRVYPEHGELLAHLGGAHGLLERPELQHTP 3400
3401 LYACELCATVMRIIKKSFACSSCNYTFAKKEQFDRHMNKHLRGGRQPFAF 3450
3451 RGVRRPGAPGQKARALEGTLPSKRRRVAMPGSAPGPGEDRPPPRGSSPIL 3500
3501 SEGSLPALLHLCSEVAPSTTKGWPETLERPVDPVTHPIRGCELPSNHQEC 3550
3551 PPPSLSPFPAALADGRGDCALDGALERPENEASPGSPGPLLQQALPLGAS 3600
3601 LPRPGARGQDAEGKRAPLVFSGKRRAPGARGRCAPDHFQEDHLLQKEKEV 3650
3651 SSSHMVSEGGPRGAFHKGSATKPAGCQSSSKDRSAASTPSKALKFPVHPR 3700
3701 KAVGSLAPGELARGTENGMKPATPKAKPGPSSQGSGSPRPGTKTGGGSQP 3750
3751 QPASGQLQSETATTPAKPSFPSRSPAPERLPARAQAKSCTKGPREAGEQG 3800
3801 PHGSLGPKEKGESSTKRKKGQVPGPARSESVGSFGRAPSAPDKPPRTPRK 3850
3851 QATPSRVLPTKPKPNSQNKPRPPPSEQRKAEPGHTQRKDRLGKAFPQGRP 3900
3901 LLRPPKRGTAVHGAEPAEPHTHRTAEAQSDLLSQLFGQRLTGFKIPLKKD 3950
3951 ASE 3953
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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