 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q06561 from www.uniprot.org...
The NucPred score for your sequence is 0.50 (see score help below)
1 MKRSSTVLAALLALLLVATNDAARHRKYRQTYQDIDSDDDDTSDVQITVF 50
51 PSEKEVRDGRDVSFECRARTSDNSVYPTVRWARVGGPLPSSAHDSGGRLT 100
101 INPVQLSDAGTYICVSDYNGNTVEARATLSVVSYGPQEVSNGLRQAGQCM 150
151 ADEKACGNNECVKNDYVCDGEPDCRDRSDEANCPAISRTCEPNEFKCNNN 200
201 KCVQKMWLCDGDDDCGDNSDELNCNAKPSSSDCKPTEFQCHDRRQCVPSS 250
251 FHCDGTNDCHDGSDEVGCVQPTVVDPPQTNLQVPRGTTFSLTCKAVAVPE 300
301 PYINWRLNWGPVCEPPRCLQTSEGGYGTLTIHDAQPVDQGAYTCEAINVK 350
351 GRVLATPDCIVRVVDDPRPQPPQPPTAPPQRASCDTRGAVTPYPNNYGTC 400
401 ECKSQVTGPNCDQCKPGAFHLSEKSPEGCLKCFCFGVSNDCRSSGHYRTK 450
451 DRLMFAGDAEGVTISDIEERTIDRNTPFSFFKTGYLTFDGTTDGVAKYWR 500
501 LPQRFLGDKVTAYGGKMEFEIEFSGSGHHSSEPMVVLKGNQNILVHRVRN 550
551 QEHVLRSDSPVRITVETYETNYEQLNGAAATREDLLMVLADLDAFLIRAT 600
601 HVAHQTSTSLGDVSWEIAVDRYTPDGLALEVEQCVCPPGYLGTSCEDCAP 650
651 GYERSGYGPYLGTCVPIQPRHQQCGPGAVAPTAPAQGQCQCKASVIGPNC 700
701 DRCAPNSFGLAPTNPQGCIPCFCSGVTQQCSASSYRRTSVSIDYARGDRD 750
751 QLELTTSDSRQPYSPQTRAELSGQAIEFRSFEEARGQTLYWKLPEKFLGD 800
801 KVTSYGGTLEYTFKFSGNGNSDQSADVILRGNDIALQYKHREPFYADREN 850
851 KVQIKIIETSWQRVDGQQATREHLLMTLADLDTLLIKSTYNDDCTDSQLL 900
901 SANLEFAEPYGQGLTAAEVEQCICPPGYVGTSCEDCAPGYSRTGGGLYLG 950
951 LCEKCECNGHASQCDKEYGYCLDCQHNTEGDQCERCKPGFVGDARRGTPN 1000
1001 DCQPEATRAPCHCNNHSPRGCDSFGRCLLCEHNTEGTHCERCKKGYYGDA 1050
1051 TKGSPYDCTPCPCPGASDCYLDNEGQVACRICPAGLQGRLCNECAPGYTR 1100
1101 SNKPAGRVCEPIGQVTNEDITFVQKPHEVLRVRIMEPKRQIALPGDRVHW 1150
1151 ICQVTGYTTEKIHVEWTKVGEMSLPPNAKAYDGYLVLKGVEAENAGQYRC 1200
1201 TATTITQYATDDALLTISKRISGRPPQPVIDPPHLVVNEGEPAAFRCWVP 1250
1251 GIPDCQITWHREQLGGPLPHGVYQTGNALKIPQSQLHHAGRYICSAANQY 1300
1301 GTGQSPPAVLEVKKPVIPPKVDPIRQTVDRDQPARFKCWVPGNSNVQLRW 1350
1351 SRPGGAPLPSGVQEQQGILHIPRASDQEVGQYVCTATDPSDNTPLQSEPV 1400
1401 QLNIRDPAPPQRGAAPQIDPPNQTVNVNDPAQFRCWVPGQPRAQLKWSRK 1450
1451 DGRPLPNGILERDGFLRIDKSQLHDAGEYECTSTEPDGSTQLSPPARLNV 1500
1501 NQPQAIQPQVDPPVQTVNEGEPSRIRCWVPGHPNIQLQFVKRGRRPLPAH 1550
1551 ARFSQGNLEIPRTLKSDEDEYICIATDPTTNRPVESNPARVIVKSPIRPI 1600
1601 IDPAEQTVPEGSPFKIRCYVPGHPSVQLTFRRVSGQLNEDADENNGLLAV 1650
1651 QRAELTDEGDYICTARDPDTGAPIDSTPATVHVTNAAAPPQVEARPPQHP 1700
1701 VITPQTQTIPEGDPARIQCTVPGNPSAAQHLSFERVDGKGLPFGSSDDRG 1750
1751 VLTIPSTQLQDAGEYVCLYSPENSPPVKTNPSTLNVTPEGTPPRPVATPP 1800
1801 LLSVAPGSPARFNCVAHSDTPARIRWGFREENGPLPEHVNQDGDDIVISE 1850
1851 AGDRNVGEYVCSATNDFGTGVADPVRLEVTEDQEPPTAVVEPRTWNGKPG 1900
1901 ERHQFRCITTGSPTPKITWTGPNGSPLPHDVTPLEPNILDFSNGRSELNG 1950
1951 DYTCTASNPIGEASDHGNVNIGPSLTVKTNPPGPKLIVTVGEPLQVKCEA 2000
2001 FGAPGDPEPEVEWLHDPGPERGDLPDDFKPVTISEQFIRHPNVGLGNAGV 2050
2051 YTCKGSSAHATATKNIYIEVVEPSRIATVSILGGSSQWFDQGEKGELICT 2100
2101 ATGSSLVDRLEWEKVDDQLPTDVEEHNEPGLLHFPSFKNSYAGEYRCNGY 2150
2151 RNNEIIASAAVHVHSSANADDEPKVEIEPPRVRVVSQGDNIVLKCSVQGA 2200
2201 ENGEHFKWALLRGGSLVRQLGTEPTLEITKADPSNDFGVYRCNVEDNNGL 2250
2251 VIGSAFTAVSVGQQDKSHAQIVKFDDKSDASFTCPIYSVPGSKVDWTYEN 2300
2301 GDLPSKAVPNGNKIEIKEFDDASAGTYVCKVSFDGNVVEGFVTAQMFVPD 2350
2351 TIIQVLLEVSSESPQIGDRAWFDCKVTGDPSAVISWTKEGNDDLPPNAQV 2400
2401 TGGRLLFTDLKEDNAGVYRCVAKTKAGPLQTRTVLNVGSGKQDQVTFTVA 2450
2451 DSLPVVYTVGQPAYLSCIGKTETKPNQSVVWTKEEGDLPSGSRVEQGVLM 2500
2501 LPSVHRDDEGSYTCEIVKEENPVFSTVDLQIDDFIPVIDGEPIELPPLSD 2550
2551 EEIVNLDIEITLNTANPKGIIFETKRINSGDLLATPYDTIHHEAKITDYG 2600
2601 TVLYEFDIGNGRQIVETTNPINPNEWNVIKIKNDKNQVTIQLNDESATIR 2650
2651 QHTNPLPSLSTGVNRPVFIGGRHEPTNEANDFRGIISQVVLSGHNVGLGD 2700
2701 ARIPSSVVKYDACASTNLCLNGANCRNANNHHGFSCECAEEFHGEYCQWR 2750
2751 SNSCHDESCNTGICLDNEESWQCVCPLGTTGLRCEEKTEIPQPLGFTSDT 2800
2801 SFLAVKRPVKFESIKMKLRPQADSDEHILMYFASDYGSNTKQYTSLSLIA 2850
2851 NQVVLTVRRPDKEVQKIRSETLEAGELIDVAVRQAGNALVMTVDGNQVST 2900
2901 IETDTLKPGTEIFIGGLPPGLNSPDDVVEQSFQGCVYEILINSQDVDLQN 2950
2951 LSSSGDISSCEESQFPVEEDDTTTTTTTEEPEAVIEEPTTEEPTTTEEPI 3000
3001 TEEPTEEPTTTEEPTTTEEPTTTTEEPTTTTTEEPYHIYETSRDDDPEII 3050
3051 IPVETTTTSTTTTSTTEEPEAEPALVLPTDPVEENDVSDEEEEISTISTV 3100
3101 SPDNGLDSDSDYSEGTLPPDSSSEEIVVGDVYSTQEPNNICANSTCGMNG 3150
3151 QCVPRNMTHYTCECKLYYDGPTCSLFKPIEHAARFDGDAFIELSSDEFPH 3200
3201 LTSEKDEIVAFKFKTEQQNGVLLWQGQRPTVQQMEDYISVGIVNGHLHFS 3250
3251 YELGGGAAHLISEERVDDGKEHSVRFERKGREGQMRIDNYREVDGRSTGI 3300
3301 LAMLNVDGNIFVGGVPDISKATGGLFSNNFVGCIADVELNGVKLDLMATA 3350
3351 IDGKNVKPCDEWMHRKRWLYRRRVR 3375
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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