 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10584 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MWTILALLTATLLFEGAFSVDTVVRKNVDSLSSDEVLALEKALDDLQQDD 50
51 SNQGYQAIAGYHGVPTMCVDKHEKNVACCLHGMPSFPLWHRLYVVQLERA 100
101 LIRKKATISIPYWDWTSELTHLPELVSHPLFVGTEGGKAHDNSWYRADIT 150
151 FLNKKTSRAVDDRLFEKVQPGHHTRLMEGILDALEQDEFCKFEIQFELAH 200
201 NAIHYLVGGRHTYSMSHLEYTSYDPLFFLHHSNTDRIFAIWQRLQQLRGK 250
251 DPNSADCAHNLIHTPMEPFDRDTNPLDLTREHAKPADSFDYGRLGYQYDD 300
301 LSLNGMSPEELNVYLGERAAKERTFASFILSGFGGSANVVVYVCRPAHDE 350
351 ISDDQCIKAGDFFLLGGPTEMKWGFYRAYHFDVTDSVASIDDDGHGHYYV 400
401 KSELFSVNGSALSNDILRQPTLVHRPAKGHFDKPPVPVAQANLAVRKNIN 450
451 DLTAEETYSLRKAMERFQNDKSVDGYQATVEFHALPARCPRPDAKDRFAC 500
501 CVHGMATFPHWHRLFVTQVEDALLRRGSTIGLPNWDWTMPMDHLPELATS 550
551 ETYLDPVTGETKNNPFHHAQVAFENGVTSRNPDAKLFMKPTYGDHTYLFD 600
601 SMIYAFEQEDFCDFEVQYELTHNAIHAWVGGSEKYSMSSLHYTAFDPIFY 650
651 LHHSNVDRLWAIWQALQIRRGKSYKAHCASSQEREPLKPFAFSSPLNNNE 700
701 KTYHNSVPTNVYDYVGVLHYRYDDLQFGGMTMSELEEYIHKQTQHDRTFA 750
751 GFFLSYIGTSASVDIFINREGHDKYKVGSFVVLGGSKEMKWGFDRMYKYE 800
801 ITEALKTLNVAVDDGFSITVEITDVDGSPPSADLIPPPAIIFERADAKDF 850
851 GHSRKIRKAVDSLTVEEQTSLRRAMADLQDDKTSGGFQQIAAFHGEPKWC 900
901 PSPEAEKKFACCVHGMAVFPHWHRLLTVQGENALRKHGFTGGLPYWDWTR 950
951 SMSALPHFVADPTYNDAISSQEEDNPWHHGHIDSVGHDTTRDVRDDLYQS 1000
1001 PGFGHYTDIAKQVLLAFEQDDFCDFEVQFEIAHNFIHALVGGNEPYSMSS 1050
1051 LRYTTYDPIFFLHRSNTDRLWAIWQALQKYRGKPYNTANCAIASMRKPLQ 1100
1101 PFGLDSVINPDDETREHSVPFRVFDYKNNFDYEYESLAFNGLSIAQLDRE 1150
1151 LQRRKSHDRVFAGFLLHEIGQSALVKFYVCKHNVSDCDHYAGEFYILGDE 1200
1201 AEMPWRYDRVYKYEITQQLHDLDLHVGDNFFLKYEAFDLNGGSLGGSIFS 1250
1251 QPSVIFEPAAGSHQADEYREAVTSASHIRKNIRDLSEGEIESIRSAFLQI 1300
1301 QKEGIYENIAKFHGKPGLCEHDGHPVACCVHGMPTFPHWHRLYVLQVENA 1350
1351 LLERGSAVAVPYWDWTEKADSLPSLINDATYFNSRSQTFDPNPFFRGHIA 1400
1401 FENAVTSRDPQPELWDNKDFYENVMLALEQDNFCDFEIQLELIHNALHSR 1450
1451 LGGRAKYSLSSLDYTAFDPVFFLHHANVDRIWAIWQDLQRYRKKPYNEAD 1500
1501 CAVNEMRKPLQPFNNPELNSDSMTLKHNLPQDSFDYQNRFRYQYDNLQFN 1550
1551 HFSIQKLDQTIQARKQHDRVFAGFILHNIGTSAVVDIYICVEQGGEQNCK 1600
1601 TKAGSFTILGGETEMPFHFDRLYKFDITSALHKLGVPLDGHGFDIKVDVR 1650
1651 AVNGSHLDQHILNEPSLLFVPGERKNIYYDGLSQHNLVRKEVSSLTTLEK 1700
1701 HFLRKALKNMQADDSPDGYQAIASFHALPPLCPSPSAAHRHACCLHGMAT 1750
1751 FPQWHRLYTVQFEDSLKRHGSIVGLPYWDWLKPQSALPDLVTQETYEHLF 1800
1801 SHKTFPNPFLKANIEFEGEGVTTERDVDAEHLFAKGNLVYNNWFCNQALY 1850
1851 ALEQENYCDFEIQFEILHNGIHSWVGGSKTHSIGHLHYASYDPLFYIHHS 1900
1901 QTDRIWAIWQALQEHRGLSGKEAHCALEQMKDPLKPFSFGSPYNLNKRTQ 1950
1951 EFSKPEDTFDYHRFGYEYDSLEFVGMSVSSLHNYIKQQQEADRVFAGFLL 2000
2001 KGFGQSASVSFDICRPDQSCQEAGYFSVLGGSSEMPWQFDRLYKYDITKT 2050
2051 LKDMKLRYDDTFTIKVHIKDIAGAELDSDLIPTPSVLLEEGKHGINVRHV 2100
2101 GRNRIRMELSELTERDLASLKSAMRSLQADDGVNGYQAIASFHGLPASCH 2150
2151 DDEGHEIACCIHGMPVFPHWHRLYTLQMDMALLSHGSAVAIPYWDWTKPI 2200
2201 SKLPDLFTSPEYYDPWRDAVVNNPFAKGYIKSEDAYTVRDPQDILYHLQD 2250
2251 ETGTSVLLDQTLLALEQTDFCDFEVQFEVVHNAIHYLVGGRQVYALSSQH 2300
2301 YASYDPAFFIHHSFVDKIWAVWQALQKKRKRPYHKADCALNMMTKPMRPF 2350
2351 AHDFNHNGFTKMHAVPNTLFDFQDLFYTYDNLEIAGMNVNQLEAEINRRK 2400
2401 SQTRVFAGFLLHGIGRSADVRFWICKTADDCHASGMIFILGGSKEMHWAY 2450
2451 DRNFKYDITQALKAQSIHPEDVFDTDAPFFIKVEVHGVNKTALPSSAIPA 2500
2501 PTIIYSAGEGHTDDHGSDHIAGSGVRKDVTSLTASEIENLRHALQSVMDD 2550
2551 DGPNGFQAIAAYHGSPPMCHMXDGRDVACCTHGMASFPHWHRLFVKQMED 2600
2601 ALAAHGAHIGIPYWDWTSAFSHLPALVTDHEHNPFHHGHIAHRNVDTSRS 2650
2651 PRDMLFNDPEHGSESFFYRQVLLALEQTDFCQFEVQFEITHNAIHSWTGG 2700
2701 HTPYGMSSLEYTAYDPLFYLHHSNTDRIWAIWQALQKYRGFQYNAAHCDI 2750
2751 QVLKQPLKPFSESRNPNPVTRANSRAVDSFDYERLNYQYDTLTFHGHSIS 2800
2801 ELDAMLQERKKEERTFAAFLLHGFGASADVSFDVCTPDGHCAFAGTFAVL 2850
2851 GGELEMPWSFERLFRYDITKVLKQMNLHYDSEFHFELKIVGTDGTELPSD 2900
2901 RIKSPTIEHHGGDHHGGDTSGHDHSERHDGFFRKEVGSLSLDEANDLKNA 2950
2951 LYKLQNDQGPNGYESIAGYHGYPFLCPEHGEDQYACCVHGMPVFPHWHRL 3000
3001 HTIQFERALKEHGSHLGIPYWDWTKSMIALPAFFADSSNSNPFYKYHIMK 3050
3051 AGHDTARSPSDLLFNQPQLHGYDYLYYLALSTLEEDNYCDFEVHYEILHN 3100
3101 AVHLWLGGTETYSMSSLAFSAYDPVFMILHSGLDRLWIIWQELQKLRKKP 3150
3151 YNAAKCAGHMMDEPLHPFNYESANHDSFTRANAKPSTVFDSHKFNYHYDN 3200
3201 PDVRGNSIQEISAIIHDLRNQPRVFAGFVLSGIYTSANVKIYLVREGHDD 3250
3251 ENVGSFVVLGGPKEMPWAYERIFKYDITEVANRLNMHHDDTFNFRLEVQS 3300
3301 YTGEMVTHHLPEPLIIYRPAKQEYDVLVIPLGSGHKLPPKVIVKRGTRIM 3350
3351 FHPVDDTVNRPVVDLGSHTALYNCVVPPFTYNGYELDHAYSLRDGHYYIA 3400
3401 GPTKDLCTSGNVRIHIHIEDE 3421
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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