 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q29L39 from www.uniprot.org...
The NucPred score for your sequence is 0.93 (see score help below)
1 MSSHSGGTDWNSVIKALLLSRTGALNKNEVVNLLKAITRCEHEFFDDEGS 50
51 FVPFYTAFAALAADKLMQIKSICQTQICQLHDATAVLIRFILFRLPKVSV 100
101 FESKWLLAALKMLCEGRENASSAVAQFDYSAVAGVVKSSKHPESSNKSIM 150
151 SMAGSTTGGAGAGGSNDKESPKLEIKRSRSDLSSVILQQLLAPLEPGKMT 200
201 WVPLSEEITDCTEQLLAANVEVFQEANGVDTLLDICVGLPILSRYRMKYM 250
251 ETINAGKPLYLPLTQAEATTVKSSMNHMLTDLSIVGQAYALIVMQPLTPS 300
301 RIEKLSMCGISALYNAVLTSIATSVLSMGQASSAQKQQQQQTAAAASTSQ 350
351 GSGGGLSSVQTSKDHDEFEEQASGIVNKALEIYSSIGEMFKSSARMYIYQ 400
401 NHLCYGSWLLISGIQGAMGASGSASAETAAKAAAATKVASKTESITAPST 450
451 PIARVNLFKVQQGFGELNAAIANHSIKLLSELIEDLKIESACGQSSVTDP 500
501 ETLPEPAQFDILQNYTSLERIVRVLNTATLHQLFTFLATVAYRKACTLKR 550
551 ASTKDRTECDPISYSDSTTCLNESMSYSDNSEEDDSESYLGHWFKETLSP 600
601 ETHDDNANTSSQERSGEQKSALVPKLDEPHEYLELSADIFCFLDQFLANR 650
651 HAYMQRYVKAGVSDQQMLLMANIIKDFDRDVMRNDSEQQSPPSTAAAVAA 700
701 ATGAGSSGSSAKWQASMIRFSGAAGRYIHNLISTGLLSEQLQSNLLQHLA 750
751 ISPWSTDTNTWPLQVYPSTLSVLVQILLLKPIQEKEAACLSVWHRLINTL 800
801 VDGVCSSSSAGDSDYEDINIEHAQLLLFLFHSLNLMQKKSILLLTAGGVI 850
851 RCAEVCRGISAERVVRNSQMMLLSRLLVFLEYLMKHLYHAPPELLDQVRW 900
901 ILFSVSSMPETQKVSDLINSRTKLNSYCRQDIEEKFRKSSGEYGSNIRPT 950
951 FYSLVVKEPEKAYWVSEFKLDGLAWNFILCTPDKLKYPLLVDALVDILNI 1000
1001 ADMSSFSRKENSESNSIHNLCAMQYCFTNTWKLLLGLPPSTSHVEALRIE 1050
1051 RAPNLHSLVWSTRLPLATSHYLIVNSLIKQGMYTQYAESLYTQVGDTTAD 1100
1101 IRYNLKQTILGVEAFNQQMSNKGIPRLSELILFDALVAHMQAVAWAEKEG 1150
1151 FKLLRKDCEDSSGGDQSSCTSAASSSGGNATDHDHPEVYSSNESIDEEKS 1200
1201 KHEDDQGLSTETLQRNQMINELLIKLMDSYRYLSEIVREQMLKQLSSTTP 1250
1251 EHVLNLIVPIVSDKPAIMLELHAAFLKLLPNEDKQLIANEWPKCLMVNDS 1300
1301 AFDGKQHPVEPYTLNVIDAHITELTRGSGGVAYSTLHTLKHCLKTILHLM 1350
1351 ELLLPYASNCTEMDAQLKPLLIASMLDMRTDYLQGQSEQCLREILSGLTL 1400
1401 EAQKLLLYEHMIGYCYRMLIEFAAELRQPAAGGTPLDQERALFNESMLFA 1450
1451 VLKTFIKMLEKPTAVQAMRQFFHDQKTGSLTTLLLSFTGTTLPLSYARKM 1500
1501 LQFVERLFEQSTRADSQFQHEDLVDCFSDLATVDVARLKLWLAHIIYGPN 1550
1551 MTAGDVSNSETMDPTCRLLTSIMQPSSSSSSNAQTPTNMATVSAMPSISD 1600
1601 QLDAMDIDYDCGAAAGAAGAAPNTSQILSLWQAAQPNPSEESSQACDHSE 1650
1651 GGEQRQSERNGGLLLSITKYLVRDQSKAGPIAAPLFQALLQLGQTLISPP 1700
1701 HDGCDFADVLQIMITLADASPARGHVALFNTTLLWLELAKLQLPDKHLRH 1750
1751 AENVSALLRYLSELLQSIGYRGSRQHMPPWDDELQTDIDDLYDELAEEEQ 1800
1801 DSLLDDSDEDTLNNKLCTFSQTQKEFMNQHWYHCHTCNMINTVGVCSVCA 1850
1851 RVCHKGHDVSYAKYGNFFCDCGAKEDGSCQALSRRLGSGEVRESVGPGGS 1900
1901 SSCSYLPSHMSLLASKKRSNTAPGATQQQHGAPARKDSISSERIQLLGKL 1950
1951 LEPYRETLQHQEQWMLVVRCILEYFDVLLPSIRENCTLYSIVGCHRRATA 2000
2001 ALQRLHQLEQSFQITDQLMFATLGSQEGAFENVRMNYSGDQGQTIKHLLT 2050
2051 SGTIRRVAFCCLSSPHGRRQQLAVSHEKGKVTILQLSALLKQADASKRKL 2100
2101 TLTQLSSAPIACTVLSLAANPCNEDCLAVCGLKECHVLTFSSSGSTNEHI 2150
2151 VITPQLENGNFLKKAMWLPGSQTLLAIVTTDYVKIYDLSVDTISPKYYYL 2200
2201 VAVGKIKDCTFMYHQQDGSGSYFMLSFTSSGYIYTQQLDQQSLAVHGDFY 2250
2251 VTNTLELSHQHIKDINGQVCGGGVSIYYSHALQLLFYSYTCGRSFCSPLT 2300
2301 NVNEGVKGIYHLDINNTAASTASKSSSASKVPLQSLVGWTEVAGHPGLIY 2350
2351 ASMHTSNNPVILMITPERIYMQEIKAQSAKSRIMDVVGIRHSVAGTEKTT 2400
2401 LLLLCEDGSLRIFSAQPEHTSFWLSPQVQPFGNQLYSSTLLAKNTTNNPQ 2450
2451 GKSKGGGSAAAGKLLHRKASSQQHQKQLTSGGQPIFPIDFFEHCNMLADV 2500
2501 EFGGNDLLQIYNKQKLKTRLFSTGMFVASTKATGFTLEVVNNDPNVVMVG 2550
2551 FRVMLGTQDIQRAPVSVTILGRTIPTTMRKARWFDIPLTREEMFQSDKML 2600
2601 KVVFAKSQDPEHVTLLDSIEVYGKSKELVGWPDDSEDVPAPSSGPTPVTA 2650
2651 TQQSAAANFGEGFNCITQLDRMVNHLLEVLDCALHLLGGSAVAAPLRSKV 2700
2701 VKTASGLLLLPTPNPVQTQARYVLATLYGSRAAYHSFKDGVILHFVHGEL 2750
2751 QAMQPKLQQLESLQEIDPEAFYRLILLVRGVANSRPQSLAKICLENSYDL 2800
2801 VPDLMRIVLELHKITPDLDEPVNIVRRGLCQPETIVHCLVEIMYGFALAD 2850
2851 PGQVGRMTQYFIDLLKHDATVISHSAKEALILLLSPRMKRRKVAAIAVIT 2900
2901 PPACSTPTPQMQALQAVAASAANDIIEEAAGVAAGQDQDNAAAALLEAVE 2950
2951 GGLPGQQANHQLLNLEAFMGGGFPRLLGLPEDGDDEAIMDIAIALSLQQH 3000
3001 GGADANALHSLQQGLANIQGIRQAAANAAAASVSVSVSAGGSDDEGSNVA 3050
3051 TDGSTLRTSPAEPAGSGGSESGGSGVESIGGTSARSSNFGDHPNTTPPRQ 3100
3101 SCSSVKDGEPGEEQQPGPSGSGGSASVPGGGLSAMSSTEDNNEINEDEKL 3150
3151 QKLHDLRIAVLESIIQHLGTFDLCNGLQAIPLIQVIHMLTTDLNGNNERD 3200
3201 QQVLQELLQALVEYVEIGKRGAASRMENKCPGNEVRLALLSLFGVMMGKT 3250
3251 KSKQTGTTSPPHQFKDNSSFVASTTANVLSKSGAFVYALEALNTLLVHWK 3300
3301 TVLGDPYAPGQAAAPIASGGATSGPGVQLLKPVKHGPKPDISILIPQNYL 3350
3351 KNYPDIFESYDGLLTEIIVRLPYQILRLSSAHPDNYDSSFCEAMTFTLCE 3400
3401 YMMLNLNTLLRRQVRKLLMYICGSKEKFRMYRDGHSLDAHFRVVKRVCSI 3450
3451 VSSKTGAPYNANPPMLSYDSLVDLTEHLRTCQEISQMRTGNWQKFCVVHE 3500
3501 DALAMLMEIACYQLDDGVSPIIIQLLQAAVCNMPANKQQQQQQQPPPAVV 3550
3551 SASSKLRSDREKSEDTDAYYSKFDPAQCGTFVHQIFRYACDALIIRFVRI 3600
3601 FLLENNISQLRWQAHSFMTGLFEHANERQREKLLTIFWNLWPLVPTYGRR 3650
3651 TAQFVDLLGYLTLTTRSITERLPEFVSRAVEVLRTQNELLCKHPNAPVYT 3700
3701 TLESILQMNGYYLESEPCLVCNNPEVPMANIKLPSVKSDSKYTTTTMIYK 3750
3751 LVQCHTISKLIVRIADLKRTKMVRTINVYYNNRSVQAVVELKNRPALWHK 3800
3801 ARSVSLQSGQTELKIDFPLPITACNLMIEFADFFETVSGSSENLQCPRCS 3850
3851 AAVPAYPGVCGNCGENVFQCHKCRAINYDEKDPFLCHSCGFCKYAKFDFS 3900
3901 MYARVCCAVDPIESAEDRAKTVLMIHTSLERADRIYHQLLANKQLLELLI 3950
3951 QKVAEHRINDRLVEDNMASVHSTSQVNKIIQLLAQKYCVESRASFEELSK 4000
4001 IVQKVKACRSELVAYDRQQQDLPPSNLALVLGAENPTTNRCYGCALASTE 4050
4051 QCLTLLRAMAYNYDCRMGLYSQGLVSELAEHNLRRGTPQIQNEVRNLLVV 4100
4101 LTKDNAEACMHLLQLVTSRVKSALMGSIPLISLEAAVHQEMTLLEVLLSQ 4150
4151 DDLCWEYKLKVIFELFISNCKLPRGPVASVLHPCLRILQSLINPTISGSG 4200
4201 SGGKPVSAIELSNIKLPEGNTIDYRAWLNSDQNHEYLAWSSRMPISHHQQ 4250
4251 DAPAGTKPKSSKQQQSAGTETPPRKSKEAARAAYLGEKFGKRWRSNVLDK 4300
4301 QRVTKPLVFNAEWIQPLLFNENSRFGRQLACTLLGGLARTHERKQQALNL 4350
4351 LTSFLYHVGDAGEASNEYLALYRSIATESPWLQYLVLRGVLCKISSLLAT 4400
4401 EIAKVHCMEEHSLSSDLTLGYALRRYVELLWLFLECPNIRRTYKTRLLGP 4450
4451 VLESYLALRSLVVQRTRHIDEAQEKLLEMLEEMTSGTEEETRAFMEILID 4500
4501 TVDKTRMNDIKTPVFIFERLYSIIHPEEHDESEFYMTLEKDPQQEDFLQG 4550
4551 RMLGNPYPSGEMGLGPLMRDVKNKICTDCELIALLEDDNGMELLVNNKII 4600
4601 SLDLPVKDVYKKVWLAEGGDRDAMRIVYRMRGLLGDATEEFVETLNNKSQ 4650
4651 EAVDTEQLYRMANVLADCNGLRVMLDRIGSLQRISRQRELIQVLLKLFLI 4700
4701 CVKVRRCQEVLCQPEIGAINTLLKVLQMCLQSENDSIQSAVTEQLLEIME 4750
4751 TLLSKAASDTLDSFLQFSLTFGGPEYVSALISCTDCPNVRNNPSVLRHLI 4800
4801 RVLAALVYGNEVKMALLCEHFKDTLNFKRFDNERTPEEEFKLELFCVLTN 4850
4851 QIEHNCIGGTLKDYIVSLGIVERSLAYITEHAPCVKPTLLRTDSDELKEF 4900
4901 ISRPSLKYILRFLTGLSNHHEATQVAISKDIIPIIHRLEQVSSDEHVGSL 4950
4951 AENLLEALSTDAATAARVQQVRDFTRAEKKRLAMATREKQLDALGMRTNE 5000
5001 KGQVTAKGSILQKIEKLRDETGLTCFICREGYACQPEKVLGIYTFTKRCN 5050
5051 VEEFELKSRKTIGYTTVTHFNVVHVDCHTSAIRLTRGRDEWERASLQNAN 5100
5101 TRCNGLLPLWGPSVLETTFSASMTRHSSYMQESTQRCDISYTSSIHDLKL 5150
5151 LLVRFAWERSFHDDAGGGGPQSNMHFVPYLLFYSIYMLLSSRSAARDSKT 5200
5201 VLAYLTAPPSEKWLECGFEVEGPLYMITISVTLHSRELWNKHKVAHLKRM 5250
5251 LAVAQARHVSPSVLCKALLAPADRQVKDYSVYKPYLMMWAMIDMIYNILF 5300
5301 KLVTMPKEEAWPVSLFDYIRKNDEAMLKSTDGILHILTEELLPCTSFGEF 5350
5351 CDVAGLLTLIEQPDSFIEDLLASLPSTTSSS 5381
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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