 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O93574 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 TNGLNTTTASVLQFSLGSGSCRFSYSDPSITVSYSKNSSADWTQLEKISA 50
51 PSNVSTIIHILYLPEDAKGENVHFQWKQDYLHAGEVYEACWALDNILIIN 100
101 AAHRKVVLEDNLDPVDTGNWLFFPGATVKHSCQSDGNSIYFHGTEGSEFN 150
151 FATTRDVDLSTEDAQEQWAEEFESQPKGWDILGAVIGTECGTLESGSSMV 200
201 FLRDGERKICTPYMDTTGYGNLRFYFSMGGNCDSGESHENDVILYAKIEG 250
251 RREHIALDTLTYAAYKVPSLVSVVISPDLQTPATKFCLKQKSHQGHNRNV 300
301 WAVDYFHVLPVLPSTVTHMIQFSINLGCGTYQPGNSVSLEFSTNHGRSWS 350
351 LLHTECLPEICAGPHLPHSTVYASENYSGWNRITTPVPNAALTSDTRIRW 400
401 RQTGPIHGNMWAIDNIYIGPSCLKFCSGRGQCTRNGCKCDPGFSGPACET 450
451 ASQTFPMFISESFASSRLSSYHNFYSIRGAEVSFGCGVLASGKALVFNKD 500
501 GRRQLITAFLDSSQSRFLQFTLRLGSKSVLSTCKAPDQPGEGVLLHYSYD 550
551 NGITWKLLEHYSYLNYHEPRIISVELPEDARQIGIQFRWWQPYHSSQGED 600
601 VWAIDEIVMTSVLFNSISLDFTNLVEVTQSLGFYLGNVQPYCGHDWTLCF 650
651 TGDSKLTSSMRYVETQSMQIGASYMIQFNLVMGCGQKFTPHMDNQVKLEY 700
701 STNHGLTWHLVQEECLPSMPSCQEFTSASIYHSNEFTQWRRITVLLPQKT 750
751 WSSATRFRWSQCYYTAPDEWALDNIYIGQQCPNMCSGHGWCDHGVCRCDS 800
801 GFRGTECQPENPLPSTVMSDFENPDVLKTEWQEIIGGEIVKPEEGCGVIS 850
851 SGSSLYFNKAGKRQLVSWDLDTTWVDFVQFYIQIGGESSSCNRPDSREEG 900
901 VLLQYSNNGGINWQLLAEMYFSDFSKPRFVYLELPAAAKTPCTRFRWWQP 950
951 VFSGEGYDQWAIDDIIILSEKQKHIIPVVNPTLPQNFYEKPAFDYPMNQL 1000
1001 SVWLILANEGMTKNESFCSATPSAMLFGKSDGDRFAVTRDLTLKPGYVLQ 1050
1051 FKLNIGCTNQYSSSAPVLLQYSHDAGLFWSLVKEGCYPASPGTKGCEGSS 1100
1101 RELSEPTVYHTGDFEDWTRITIVIPRSLAASKTRFRWIQESSSHKSVPPF 1150
1151 GLDGVYISEPCPNYCNGHGDCVSGVCFCDLGYTASHGTCVSNVPNHSEMF 1200
1201 DRFERKLSPLWYKITGGQVGTGCGVLSDGKSLYFNGPGKREARTVPLDTT 1250
1251 NIRLVQFYVQIGSKATGNSCNRPRSRNEGLIVQYTNDNGITWHLLRELDF 1300
1301 MSYLEPQVVSIDLPREAKTSATAFRWWQPQHGKHSAQWALDDVLIGMNDS 1350
1351 SQTGFQDKFDGTVDLQASWYRIQGGQVDIDCLSMDTALMFSENIEKPRYA 1400
1401 ETWDFHVSASTFLQFELSMGCSKPYSNSHSIHLQYSLNNGRDWHLVTEEC 1450
1451 VPPTIGCQHYTESSIYTSERFQNWKRITAYLPPITNSPRTRFRWIQYNYA 1500
1501 SGVDSWAIDNVVLATGCPWMCSGHGICDAGHCVCDRGFGGPYCVHVNPLP 1550
1551 SVLKDDFNGNLHPDLWPEVYGAERGNLNGDTIKSGTALIFKGEGLRMLVS 1600
1601 RDLDCTNTVYIQFSFKFIAKGTPERSHSILLQYSVNGGITWHLIDEFYFT 1650
1651 QTTDVLFINVPLPYTAQSNATRFRLWQPYNSGKKEEIWIIDDFIIDGNNL 1700
1701 KNPIILLDTFDFGPKEDNWFFYPGGNIGLYCPYSSKGAPEEDSAMVFVSN 1750
1751 EVGEHSITTRDLSVNENTIIQFEINIGCTTDSSSADPVKLEFSRDLGATW 1800
1801 HLLLPLCYSSSSHLSSLCSTEHHPSSTYYTGTTQGWRREVIHFGKLHLCG 1850
1851 LTRFRWYQGFYPAGSQPVTWAIDNVYIGPQCEEMCNGHGSCINGTKCICD 1900
1901 PGYSGPTCKISTKNSDSLKDDFEGQLESDRFLLVSGGKPSRKCGIMSGGN 1950
1951 NLFFNEEGLRMLMTRDLDLSQARFVQFFMRLGCGKGVPDPRSQLSXLQYS 2000
2001 LNGGLTWSLLQEFLFSNSSNVGRYIALEIPMKARSSSTRLRWWQPSENGH 2050
2051 FYSPWVIDQILIGGNISGSTVLEDDFTTLDSRKWLLHPGGTKMPVCGSTG 2100
2101 DALVFIEKASTRYVVTTDIVVNEDSFLQIDFAASCSVTGSCYAIELEYSV 2150
2151 DLGITWHPILRDCLPTNVECNRYHLQRILISDTFNKWTRITLPLPPYTRS 2200
2201 QATRFRWHQPAPFDKQQTWAIDNVYIGDGCIDMCSGHGKCTQDNCVCDEH 2250
2251 WGGLYCDEPETPLPTQLKDNFNRSPSNQNWLTVNGGKLSTVCGAVASGMA 2300
2301 LHFSGGCSRMLVTVDLNLTNAEFIQFYFMYGCLITPNNRNQGVLLEYSVN 2350
2351 GGITWSPLMEIFYDQFSKPGFVNILLPYDAKTIGTRFRWWQPKHDGLDQN 2400
2401 DWAIDNVLISGSTDQRTVMLDTFSSAPLPQHERSPADAGPTGRIAFDMFM 2450
2451 EDKTTVNEHWLFHDDCSIERFCDSPDGVMICGSHDGREVYAVTHDLTPTE 2500
2501 GWIMQFKVSVGCKTSEKLAQNQVHVQYSTDFGVSWSYLVPQCLPADPKCS 2550
2551 GSVSQPSVFFPTKGWKRVTYSLPENLVGNPVRFRFYQKYSDVQWAIDNFY 2600
2601 LGPGCLENCRGHGDCLKEQCICDPGYSGPNCYLTQTLKTFLKERFDNEEI 2650
2651 KPDLWMSLEGGNTCTECGILAEDTTLYFGGQTVRQAVTQDLDLRGAKFLQ 2700
2701 YWGRIGSENNMTTCHRPTCRKEGVLLDYSIDGGITWTLLHEMDYQKYISV 2750
2751 RHDYILLPEHALTNTTRLRWWQPFTISNGIVVSGPDRAQWALDNILIGGA 2800
2801 EINPSQLVDTFDDEGTSHEENWSSYPNAVRTAGFCGNPSFHLYWPNKKKD 2850
2851 KTHNILSSRELIIQPGYMMQFKIVVGCEASSCGDLHSVMLEYTKDARTDS 2900
2901 WQLVQTHCLPSSSNSIGCSPFQFHEATIYNSVNSSMWRRITIQLPDHVSS 2950
2951 SATQFRWIQKGEELEKQSWAIDHVYIGEACPKLCSGRGYCSTGAICICDE 3000
3001 GYQGDDCSVFSHDLPSYIKDNFESERVTEINWETIQGGVIGNGCGQLAPY 3050
3051 AHGDSLYFNGCQVRQAVTKPLDLTRASKIMFVLQIGSISQTDSCNTNLID 3100
3101 PNTVDKAVLLQYSVNNGITWQVIAQHQPKDFIQAQRVSYNVPLEARMKGV 3150
3151 LLRWWQPRHNGTGHDQWALDHVEVVLISTRKQNYMMNFSRQHGLRHFYNR 3200
3201 RRRSLRRYP 3209
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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