 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q99PF4 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MRYSLVTCYAVLWLLMLVPGSWGQVNRLPFFTNHFFDTYLLISEDTPVGS 50
51 SVTQLLARDMDNDPLVFGVSGEEASRFFAVEPDTGVVWLRQPLDRETKSE 100
101 FTVEFSVSDHQGVITRKVNIQVGDVNDNAPTFHNQPYSVRIPENTPVGTP 150
151 IFIVNATDPDLGAGGSVLYSFQPPSPFFAIDSARGIVTVIQELDYEVTQA 200
201 YQLTVNATDQDKTRPLSTLANLAIIITDMQDMDPIFINLPYSTNIYEHSP 250
251 PGTTVRVITAVDQDKGRPRGIGYTIVSGNTNSIFALDYISGALTLNGLLD 300
301 RENPLYSHGFILTVKGTELNDDRTPSDATVTTTFNILVIDINDNAPEFNS 350
351 SEYSVAITELAQVGFALPLFIQVVDKDEDLGLNSMFEVYLVGNNSHHFII 400
401 SPTSVQGKADIRIRVAIPLDYETVDRYDFDLFANESVPDHVGYAKVKITL 450
451 INENDNRPIFSQPLYNVSLYENITVGTSVLTVLATDNDVGTFGEVNYFFS 500
501 DDPDRFSLDKDTGLIMLIARLDYELIQRFTLTVIARDGGGEETTGRVRIN 550
551 VLDVNDNVPTFQKDAYVGALRENEPSVTQLVRLRATDEDSPPNNLITYSI 600
601 VNASAFGSYFDISIYEGYGVISVSRPLDYEQIPNGLIYLTVMAKDAGNPP 650
651 LYSTVPVTIEVFDENDNPPTFSKPAYFVSVLENIMAGATVLFLNATDLDR 700
701 SREYGQESIIYSLEGSSQFRINARSGEITTTSLLDRETKSEYILIVRAVD 750
751 GGVGHNQKTGIATVNVTLLDINDNHPTWKDAPYYINLVEMTPPDSDVTTV 800
801 VAVDPDLGENGTLVYSIHPPNKFYSLNSTTGKIRTTHVMLDRENPDPVEA 850
851 ELMRKIIVSVTDCGRPPLKATSSATVFVNLLDLNDNDPTFRNLPFVAEIL 900
901 EGTPAGVSVYQVVAIDLDEGLNGLVSYRMQVGMPRMDFVINSTSGVVTTT 950
951 AELDRERIAEYQLRVVASDAGTPTKSSTSTLTVRVLDVNDETPTFFPAVY 1000
1001 NVSVSEDVPREFRVVWLNCTDNDVGLNAELSYFITAGNVDGKFSVGYRDA 1050
1051 VVRTVVGLDRETTAAYTLVLEAIDNGPVGKRRTGTATVFVTVLDVNDNRP 1100
1101 IFLQSSYEASVPEDIPEGHSIVQLKATDADEGEFGRVWYRILHGNHGNNF 1150
1151 RIHVGSGLLMRGPRPLDRERNSSHVLMVEAYNHDLGPMRSSVRVIVYVED 1200
1201 VNDEAPVFTQQQYNRLGLRETAGIGTSVIVVRATDKDTGDGGLVNYRILS 1250
1251 GAEGKFEIDESTGLIVTVDYLDYETKTSYLMNVSATDGAPPFNQGFCSVY 1300
1301 VTLLNELDEAVQFSNASYEAVIMENLALGTEIVRVQAYSIDNLNQITYRF 1350
1351 DAYTSAQAKALFKIDAITGVITVKGLVDREKGDFYTLTVVADDGGPKVDS 1400
1401 TVKVYITVLDENDNSPRFDFTSDSAISVPEDCPVGQRVATVKARDPDAGS 1450
1451 NGQVVFSLASGNIAGAFEIITSNDSIGEVFVAKPLDREELDHYILKVVAS 1500
1501 DRGTPPRKKDHILQVTILDVNDNPPVIESPFGYNVSVNENVGGGTSVVQV 1550
1551 RATDRDIGINSVLSYYITEGNEDMTFRMDRISGEIATRPAPPDRERQNFY 1600
1601 HLVVTVEDEGTPTLSATTHVYVTIVDENDNAPVFQQPHYEVVLDEGPDTI 1650
1651 NTSLITVQALDLDEGPNGTVTYAIVAGNIINTFRINKHTGVITAAKELDY 1700
1701 EISHGRYTLIVTATDQCPILSHRLTSTTTVLVNVNDINDNVPTFPRDYEG 1750
1751 PFDVTEGQPGPRVWTFLAHDRDSGPNGQVEYSVVDGDPLGEFVISPVEGV 1800
1801 LRVRKDVELDRETIAFYNLTICARDRGVPPLSSTMLVGIRVLDINDNDPV 1850
1851 LLNLPMNVTISENSPVSSFVAHVLASDADSGCNALLTFNITAGNRERAFF 1900
1901 INATTGIVTVNRPLDRERIPEYRLTVSVKDNPENPRIARKDFDLLLVSLA 1950
1951 DENDNHPLFTEGTYQAEVMENSPAGTPLTVLNGPILALDADEDVYAVVTY 2000
2001 QLLGTHSDLFVIDNSTGVVTVRSGIIIDREAFSPPFLELLLLAEDIGQLN 2050
2051 GTAHLFITILDDNDNWPTFSPPTYTVHLLENCPPGFSVLQVTATDEDSGL 2100
2101 NGELVYRIEAGAQDRFLIHPVTGVIRVGNATIDREEQESYRLTVVATDRG 2150
2151 TVPLSGTAIVTILIDDINDSRPEFLNPIQTVSVLESAEPGTIIANVTAID 2200
2201 LDLNPKLEYHIISIVAKDDTDRLVPDQEDAFAVNINTGSVMVKSPLNREL 2250
2251 VATYEVTLSVIDNASDLPEHSVSVPNAKLTVNILDVNDNTPQFKPFGITY 2300
2301 YTERVLEGATPGTTLIAVAAVDPDKGLNGLITYTLLDLTPPGYVQLEDSS 2350
2351 AGKVIANRTVDYEEVHWLNFTVRASDNGSPPRAAEIPVYLEIVDINDNNP 2400
2401 IFDQPSYQEAVFEDIAVGTVILRVTATDADSGNFALIEYSLVDGEGKFAI 2450
2451 NPNTGDISVLSSLDREKKDHYILTALAKDNPGDVASNRRENSVQVVIRVL 2500
2501 DVNDCRPQFSKPQFSTSVYENEPAGTSVITMLATDQDEGSNSQLTYSLEG 2550
2551 PGMEAFSVDMDSGLVTTQRPLQSYERFNLTVVATDGGEPPLWGTTMLLVE 2600
2601 VIDVNDNRPVFVRPPNGTILHIKEEIPLRSNVYEVYATDNDEGLNGAVRY 2650
2651 SFLKTTGNRDWEYFTIDPISGLIQTAQRLDREKQAVYSLILVASDLGQPV 2700
2701 PYETMQPLQVALEDIDDNEPLFVRPPKGSPQYQLLTVPEHSPRGTLVGNV 2750
2751 TGAVDADEGPNAIVYYFIAAGDEDKNFHLQPDGRLLVLRDLDRETEATFS 2800
2801 FIVKASSNRSWTPPRGPSPALDLLTDLTLQEVRVVLEDINDQPPRFTKAE 2850
2851 YTAGVATDAKVGSELIQVLALDADIGNNSLVFYGILAIHYFRALANDSED 2900
2901 VGQVFTMGSVDGILRTFDLFMAYSPGYFVVDIVARDLAGHNDTAIIGIYI 2950
2951 LRDDQRVKIVINEIPDRVRGFEEEFIRLLSNITGAIVNTDDVQFHVDMKG 3000
3001 RVNFAQTELLIHVVNRDTNRILDVDRVIQMIDENKEQLRNLFRNYNVLDV 3050
3051 QPAISVQLPDDMSALQMAIIVLAILLFLAAMLFVLMNWYYRTIHKRKLKA 3100
3101 IVAGSAGNRGFIDIMDMPNTNKYSFDGSNPVWLDPFCRNLELAAQAEHED 3150
3151 DLPENLSEIADLWNSPTRTHGTFGREPAAVKPDDDRYLRAAIQEYDNIAK 3200
3201 LGQIIREGPIKGSLLKVVLEDYLRLKKLFAQRMVQKASSCHSSISELIHT 3250
3251 DLEEEPGDHSPGQGSLRFRHKPPMELKGQDGIHMVHGSTGTLLATDLNSL 3300
3301 PEDDQKGLDRSLETLTASEATAFERNARTESAKSTPLHKLRDVIMESPLE 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.) |
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