 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NYC9 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MRLAEERAALAAENADGEPGADRRLRLLGTYVAMSLRPAAGAWERCAGSA 50
51 EAEQLLQAFLGRDAAEGPRPLLVVRPGPRGLAIRPGLEVGPESGLAGAKA 100
101 LFFLRTGPEPPGPDSFRGAVVCGDLPAAPLEHLAALFSEVVLPVLANEKN 150
151 RLNWPHMICEDVRRHAHSLQCDLSVILEQVKGKTLLPLPAGSEKMEFADS 200
201 KSETVLDSIDKSVIYAIESAVIKWSYQVQVVLKRESSQPLLQGENPTPKV 250
251 ELEFWKSRYEDLKYIYNQLRTITVRGMAKLLDKLQSSYFPAFKAMYRDVV 300
301 AALAEAQDIHVHLIPLQRHLEALENAEFPEVKPQLRPLLHVVCLIWATCK 350
351 SYRSPGRLTVLLQEICNLLIQQASNYLSPEDLLRSEVEESQRKLQVVSDT 400
401 LSFFKQEFQDRRENLHTYFKENQEVKEWDFQSSLVFVRLDGFLGQLHVVE 450
451 GLLKTALDFHKLGKVEFSGVRGNALSQQVQQMHEEFQEMYRLLSGSSSDC 500
501 LYLQSTDFENDVSEFNQKVEDLDRRLGTIFIQAFDDAPGLEHAFKLLDIA 550
551 GNLLERPLVARDTSDKYLVLIQMFNKDLDAVRMIYSQHVQEEAELGFSPV 600
601 HKNMPTVAGGLRWAQELRQRIQGPFSNFGRITHPCMESAEGKRMQQKYED 650
651 MLSLLEKYETRLYEDWCRTVSEKSQYNLSQPLLKRDPETKEITINFNPQL 700
701 ISVLKEMSYLEPREMKHMPETAAAMFSSRDFYRQLVANLELMANWYNKVM 750
751 KTLLEVEFPLVEEELQNIDLRLRAAEETLNWKTEGICDYVTEITSSIHDL 800
801 EQRIQKTKDNVEEIQNIMKTWVTPIFKTKDGKRESLLSLDDRHDRMEKYY 850
851 NLIKESGLKIHALVQENLGLFSADPTSNIWKTYVNSIDNLLLNGFFLAIE 900
901 CSLKYLLENTECKAGLTPIFEAQLSLAIPELVFYPSLESGVKGGFCDIVE 950
951 GLITSIFRIPSLVPRLSPQNGSPHYQVDLDGIPDLANMRRTLMERVQRMM 1000
1001 GLCCGYQSTFSQYSYLYVEDRKEVLGQFLLYGHILTPEEIEDHVEDGIPE 1050
1051 NPPLLSQFKVQIDSYETLYEEVCRLEPIKVFDGWMKIDIRPFKASLLNII 1100
1101 KRWSLLFKQHLVDHVTHSLANLDAFIKKSESGLLKKVEKGDFQGLVEIMG 1150
1151 HLMAVKERQSNTDEMFEPLKQTIELLKTYEQELPETVFKQLEELPEKWNN 1200
1201 IKKVAITVKQQVAPLQANEVTLLRQRCTAFDAEQQQFWEQFHKEAPFRFD 1250
1251 SIHPHQMLDARHIEIQQMESTMASISESASLFEVNVPDYKQLRQCRKEVC 1300
1301 QLKELWDTIGMVTSSIHAWETTPWRNINVEAMELECKQFARHIRNLDKEV 1350
1351 RAWDAFTGLESTVWNTLSSLRAVAELQNPAIRERHWRQLMQATGVSFTMD 1400
1401 QDTTLAHLLQLQLHHYEDEVRGIVDKAAKEMGMEKTLKELQTTWAGMEFQ 1450
1451 YEPHPRTNVPLLCSDEDLIEVLEDNQVQLQNLVMSKYVAFFLEEVSGWQK 1500
1501 KLSTVDAVISIWFEVQRTWTHLESIFTGSEDIRAQLPQDSKRFEGIDIDF 1550
1551 KELAYDAQKIPNVVQTTNKPGLYEKLEDIQGRLCLCEKALAEYLDTKRLA 1600
1601 FPRFYFLSSSDLLDILSNGTAPQQVQRHLSKLFDNMAKMRFQLDASGEPT 1650
1651 KTSLGMYSKEEEYVAFSEPCDCSGQVEIWLNHVLGHMKATVRHEMTEGVT 1700
1701 AYEEKPREQWLFDHPAQVALTCTQIWWTTEVGMAFARLEEGYESAMKDYY 1750
1751 KKQVAQLKTLITMLIGQLSKGDRQKIMTICTIDVHARDVVAKMIAQKVDN 1800
1801 AQAFLWLSQLRHRWDDEVKHCFANICDAQFLYSYEYLGNTPRLVITPLTD 1850
1851 RCYITLTQSLHLTMSGAPAGPAGTGKTETTKDLGRALGILVYVFNCSEQM 1900
1901 DYKSCGNIYKGLAQTGAWGCFDEFNRISVEVLSVVAVQVKSIQDAIRDKK 1950
1951 QWFSFLGEEISLNPSVGIFITMNPGYAGRTELPENLKSLFRPCAMVVPDF 2000
2001 ELICEIMLVAEGFIEAQSLARKFITLYQLCKELLSKQDHYDWGLRAIKSV 2050
2051 LVVAGSLKRGDPDRPEDQVLMRSLRDFNIPKIVTDDMPIFMGLIGDLFPA 2100
2101 LDVPRRRDPNFEALVRKAIVDLKLQAEDNFVLKVVQLEELLAVRHSVFVV 2150
2151 GGAGTGKSQVLRSLHKTYQIMKRRPVWTDLNPKAVTNDELFGIINPATGE 2200
2201 WKDGLFSSIMRELANITHDGPKWILLDGDIDPMWIESLNTVMDDNKVLTL 2250
2251 ASNERIPLNPTMKLLFEISHLRTATPATVSRAGILYINPADLGWNPPVSS 2300
2301 WIEKREIQTERANLTILFDKYLPTCLDTLRTRFKKIIPIPEQSMVQMVCH 2350
2351 LLECLLTTEDIPADCPKEIYEHYFVFAAIWAFGGAMVQDQLVDYRAEFSK 2400
2401 WWLTEFKTVKFPSQGTIFDYYIDPETKKFEPWSKLVPQFEFDPEMPLQAC 2450
2451 LVHTSETIRVCYFMERLMARQRPVMLVGTAGTGKSVLVGAKLASLDPEAY 2500
2501 LVKNVPFNYYTTSAMLQAVLEKPLEKKAGRNYGPPGNKKLIYFIDDMNMP 2550
2551 EVDAYGTVQPHTIIRQHLDYGHWYDRSKLSLKEITNVQYVSCMNPTAGSF 2600
2601 TINPRLQRHFSVFVLSFPGADALSSIYSIILTQHLKLGNFPASLQKSIPP 2650
2651 LIDLALAFHQKIATTFLPTGIKFHYIFNLRDFANIFQGILFSSVECVKST 2700
2701 WDLIRLYLHESNRVYRDKMVEEKDFDLFDKIQTEVLKKTFDDIEDPVEQT 2750
2751 QSPNLYCHFANGIGEPKYMPVQSWELLTQTLVEALENHNEVNTVMDLVLF 2800
2801 EDAMRHVCHINRILESPRGNALLVGVGGSGKQSLTRLAAFISSMDVFQIT 2850
2851 LRKGYQIQDFKMDLASLCLKAGVKNLNTVFLMTDAQVADERFLVLINDLL 2900
2901 ASGEIPDLYSDDEVENIISNVRNEVKSQGLVDNRENCWKFFIDRIRRQLK 2950
2951 VTLCFSPVGNKLRVRSRKFPAIVNCTAIHWFHEWPQQALESVSLRFLQNT 3000
3001 EGIEPTVKQSISKFMAFVHTSVNQTSQSYLSNEQRYNYTTPKSFLEFIRL 3050
3051 YQSLLHRHRKELKCKTERLENGLLKLHSTSAQVDDLKAKLAAQEVELKQK 3100
3101 NEDADKLIQVVGVETDKVSREKAMADEEEQKVAVIMLEVKQKQKDCEEDL 3150
3151 AKAEPALTAAQAALNTLNKTNLTELKSFGSPPLAVSNVSAAVMVLMAPRG 3200
3201 RVPKDRSWKAAKVTMAKVDGFLDSLINFNKENIHENCLKAIRPYLQDPEF 3250
3251 NPEFVATKSYAAAGLCSWVINIVRFYEVFCDVEPKRQALNKATADLTAAQ 3300
3301 EKLAAIKAKIAHLNENLAKLTARFEKATADKLKCQQEAEVTAVTISLANR 3350
3351 LVGGLASENVRWADAVQNFKQQERTLCGDILLITAFISYLGFFTKKYRQS 3400
3401 LLDRTWRPYLSQLKTPIPVTPALDPLRMLMDDADVAAWQNEGLPADRMSV 3450
3451 ENATILINCERWPLMVDPQLQGIKWIKNKYGEDLRVTQIGQKGYLQIIEQ 3500
3501 ALEAGAVVLIENLEESIDPVLGPLLGREVIKKGRFIKIGDKECEYNPKFR 3550
3551 LILHTKLANPHYQPELQAQATLINFTVTRDGLEDQLLAAVVSMERPDLEQ 3600
3601 LKSDLTKQQNGFKITLKTLEDSLLSRLSSASGNFLGETVLVENLEITKQT 3650
3651 AAEVEKKVQEAKVTEVKINEAREHYRPAAARASLLYFIMNDLSKIHPMYQ 3700
3701 FSLKAFSIVFQKAVERAAPDESLRERVANLIDSITFSVYQYTIRGLFECD 3750
3751 KLTYLAQLTFQILLMNREVNAVELDFLLRSPVQTGTASPVEFLSHQAWGA 3800
3801 VKVLSSMEEFSNLDRDIEGSAKSWKKFVESECPEKEKLPQEWKNKTALQR 3850
3851 LCMLRAMRPDRMTYALRDFVEEKLGSKYVVGRALDFATSFEESGPATPMF 3900
3901 FILSPGVDPLKDVESQGRKLGYTFNNQNFHNVSLGQGQEVVAEAALDLAA 3950
3951 KKGHWVILQNIHLVAKWLSTLEKKLEEHSENSHPEFRVFMSAEPAPSPEG 4000
4001 HIIPQGILENSIKITNEPPTGMHANLHKALDNFTQDTLEMCSRETEFKSI 4050
4051 LFALCYFHAVVAERRKFGPQGWNRSYPFNTGDLTISVNVLYNFLEANAKV 4100
4101 PYDDLRYLFGEIMYGGHITDDWDRRLCRTYLGEFIRPEMLEGELSLAPGF 4150
4151 PLPGNMDYNGYHQYIDAELPPESPYLYGLHPNAEIGFLTQTSEKLFRTVL 4200
4201 ELQPRDSQARDGAGATREEKVKALLEEILERVTDEFNIPELMAKVEERTP 4250
4251 YIVVAFQECGRMNILTREIQRSLRELELGLKGELTMTSHMENLQNALYFD 4300
4301 MVPESWARRAYPSTAGLAAWFPDLLNRIKELEAWTGDFTMPSTVWLTGFF 4350
4351 NPQSFLTAIMQSTARKNEWPLDQMALQCDMTKKNREEFRSPPREGAYIHG 4400
4401 LFMEGACWDTQAGIITEAKLKDLTPPMPVMFIKAIPADKQDCRSVYSCPV 4450
4451 YKTSQRGPTYVWTFNLKTKENPSKWVLAGVALLLQI 4486
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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