 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9H251 from www.uniprot.org...
The NucPred score for your sequence is 0.24 (see score help below)
1 MGRHVATSCHVAWLLVLISGCWGQVNRLPFFTNHFFDTYLLISEDTPVGS 50
51 SVTQLLAQDMDNDPLVFGVSGEEASRFFAVEPDTGVVWLRQPLDRETKSE 100
101 FTVEFSVSDHQGVITRKVNIQVGDVNDNAPTFHNQPYSVRIPENTPVGTP 150
151 IFIVNATDPDLGAGGSVLYSFQPPSQFFAIDSARGIVTVIRELDYETTQA 200
201 YQLTVNATDQDKTRPLSTLANLAIIITDVQDMDPIFINLPYSTNIYEHSP 250
251 PGTTVRIITAIDQDKGRPRGIGYTIVSGNTNSIFALDYISGVLTLNGLLD 300
301 RENPLYSHGFILTVKGTELNDDRTPSDATVTTTFNILVIDINDNAPEFNS 350
351 SEYSVAITELAQVGFALPLFIQVVDKDENLGLNSMFEVYLVGNNSHHFII 400
401 SPTSVQGKADIRIRVAIPLDYETVDRYDFDLFANESVPDHVGYAKVKITL 450
451 INENDNRPIFSQPLYNISLYENVTVGTSVLTVLATDNDAGTFGEVSYFFS 500
501 DDPDRFSLDKDTGLIMLIARLDYELIQRFTLTIIARDGGGEETTGRVRIN 550
551 VLDVNDNVPTFQKDAYVGALRENEPSVTQLVRLRATDEDSPPNNQITYSI 600
601 VSASAFGSYFDISLYEGYGVISVSRPLDYEQISNGLIYLTVMAMDAGNPP 650
651 LNSTVPVTIEVFDENDNPPTFSKPAYFVSVVENIMAGATVLFLNATDLDR 700
701 SREYGQESIIYSLEGSTQFRINARSGEITTTSLLDRETKSEYILIVRAVD 750
751 GGVGHNQKTGIATVNITLLDINDNHPTWKDAPYYINLVEMTPPDSDVTTV 800
801 VAVDPDLGENGTLVYSIQPPNKFYSLNSTTGKIRTTHAMLDRENPDPHEA 850
851 ELMRKIVVSVTDCGRPPLKATSSATVFVNLLDLNDNDPTFQNLPFVAEVL 900
901 EGIPAGVSIYQVVAIDLDEGLNGLVSYRMPVGMPRMDFLINSSSGVVVTT 950
951 TELDRERIAEYQLRVVASDAGTPTKSSTSTLTIHVLDVNDETPTFFPAVY 1000
1001 NVSVSEDVPREFRVVWLNCTDNDVGLNAELSYFITGGNVDGKFSVGYRDA 1050
1051 VVRTVVGLDRETTAAYMLILEAIDNGPVGKRHTGTATVFVTVLDVNDNRP 1100
1101 IFLQSSYEASVPEDIPEGHSILQLKATDADEGEFGRVWYRILHGNHGNNF 1150
1151 RIHVSNGLLMRGPRPLDRERNSSHVLIVEAYNHDLGPMRSSVRVIVYVED 1200
1201 INDEAPVFTQQQYSRLGLRETAGIGTSVIVVQATDRDSGDGGLVNYRILS 1250
1251 GAEGKFEIDESTGLIITVNYLDYETKTSYMMNVSATDQAPPFNQGFCSVY 1300
1301 ITLLNELDEAVQFSNASYEAAILENLALGTEIVRVQAYSIDNLNQITYRF 1350
1351 NAYTSTQAKALFKIDAITGVITVQGLVDREKGDFYTLTVVADDGGPKVDS 1400
1401 TVKVYITVLDENDNSPRFDFTSDSAVSIPEDCPVGQRVATVKAWDPDAGS 1450
1451 NGQVVFSLASGNIAGAFEIVTTNDSIGEVFVARPLDREELDHYILQVVAS 1500
1501 DRGTPPRKKDHILQVTILDINDNPPVIESPFGYNVSVNENVGGGTAVVQV 1550
1551 RATDRDIGINSVLSYYITEGNKDMAFRMDRISGEIATRPAPPDRERQSFY 1600
1601 HLVATVEDEGTPTLSATTHVYVTIVDENDNAPMFQQPHYEVLLDEGPDTL 1650
1651 NTSLITIQALDLDEGPNGTVTYAIVAGNIVNTFRIDRHMGVITAAKELDY 1700
1701 EISHGRYTLIVTATDQCPILSHRLTSTTTVLVNVNDINDNVPTFPRDYEG 1750
1751 PFEVTEGQPGPRVWTFLAHDRDSGPNGQVEYSIMDGDPLGEFVISPVEGV 1800
1801 LRVRKDVELDRETIAFYNLTICARDRGMPPLSSTMLVGIRVLDINDNDPV 1850
1851 LLNLPMNITISENSPVSSFVAHVLASDADSGCNARLTFNITAGNRERAFF 1900
1901 INATTGIVTVNRPLDRERIPEYKLTISVKDNPENPRIARRDYDLLLIFLS 1950
1951 DENDNHPLFTKSTYQAEVMENSPAGTPLTVLNGPILALDADQDIYAVVTY 2000
2001 QLLGAQSGLFDINSSTGVVTVRSGVIIDREAFSPPILELLLLAEDIGLLN 2050
2051 STAHLLITILDDNDNRPTFSPATLTVHLLENCPPGFSVLQVTATDEDSGL 2100
2101 NGELVYRIEAGAQDRFLIHLVTGVIRVGNATIDREEQESYRLTVVATDRG 2150
2151 TVPLSGTAIVTILIDDINDSRPEFLNPIQTVSVLESAEPGTVIANITAID 2200
2201 HDLNPKLEYHIVGIVAKDDTDRLVPNQEDAFAVNINTGSVMVKSPMNREL 2250
2251 VATYEVTLSVIDNASDLPERSVSVPNAKLTVNVLDVNDNTPQFKPFGITY 2300
2301 YMERILEGATPGTTLIAVAAVDPDKGLNGLVTYTLLDLVPPGYVQLEDSS 2350
2351 AGKVIANRTVDYEEVHWLNFTVRASDNGSPPRAAEIPVYLEIVDINDNNP 2400
2401 IFDQPSYQEAVFEDVPVGTIILTVTATDADSGNFALIEYSLGDGESKFAI 2450
2451 NPTTGDIYVLSSLDREKKDHYILTALAKDNPGDVASNRRENSVQVVIQVL 2500
2501 DVNDCRPQFSKPQFSTSVYENEPAGTSVITMMATDQDEGPNGELTYSLEG 2550
2551 PGVEAFHVDMDSGLVTTQRPLQSYEKFSLTVVATDGGEPPLWGTTMLLVE 2600
2601 VIDVNDNRPVFVRPPNGTILHIREEIPLRSNVYEVYATDKDEGLNGAVRY 2650
2651 SFLKTAGNRDWEFFIIDPISGLIQTAQRLDRESQAVYSLILVASDLGQPV 2700
2701 PYETMQPLQVALEDIDDNEPLFVRPPKGSPQYQLLTVPEHSPRGTLVGNV 2750
2751 TGAVDADEGPNAIVYYFIAAGNEEKNFHLQPDGCLLVLRDLDREREAIFS 2800
2801 FIVKASSNRSWTPPRGPSPTLDLVADLTLQEVRVVLEDINDQPPRFTKAE 2850
2851 YTAGVATDAKVGSELIQVLALDADIGNNSLVFYSILAIHYFRALANDSED 2900
2901 VGQVFTMGSMDGILRTFDLFMAYSPGYFVVDIVARDLAGHNDTAIIGIYI 2950
2951 LRDDQRVKIVINEIPDRVRGFEEEFIHLLSNITGAIVNTDNVQFHVDKKG 3000
3001 RVNFAQTELLIHVVNRDTNRILDVDRVIQMIDENKEQLRNLFRNYNVLDV 3050
3051 QPAISVRLPDDMSALQMAIIVLAILLFLAAMLFVLMNWYYRTVHKRKLKA 3100
3101 IVAGSAGNRGFIDIMDMPNTNKYSFDGANPVWLDPFCRNLELAAQAEHED 3150
3151 DLPENLSEIADLWNSPTRTHGTFGREPAAVKPDDDRYLRAAIQEYDNIAK 3200
3201 LGQIIREGPIKGSLLKVVLEDYLRLKKLFAQRMVQKASSCHSSISELIQT 3250
3251 ELDEEPGDHSPGQGSLRFRHKPPVELKGPDGIHVVHGSTGTLLATDLNSL 3300
3301 PEEDQKGLGRSLETLTAAEATAFERNARTESAKSTPLHKLRDVIMETPLE 3350
3351 ITEL 3354
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.) |
Go back to the NucPred Home Page.