 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P58365 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MRHPPVTWCAMLWLLMLVSGSWGQVNRLPFFTNHFFDTYLLISEDTPVGS 50
51 SVTQLLARDMDNDPLVFGVSGEEASRFFAVEPDTGVVWLRQPLDRETKSE 100
101 FTVEFSVSDHQGVITRKVNIQVGDVNDNAPTFHNQPYSVRIPENTPVGTP 150
151 IFIVNATDPDLGAGGSVLYSFQPPSQFFAIDSARGIVTVIRELDYEVTQA 200
201 YQLTVNATDQDKTRPLSTLANLAIIITDVQDMDPIFINLPYSTNIYEHSP 250
251 PGTTVRVITAVDQDKGRPRGIGYTIVSGNTNSIFALDYISGALTLNGLLD 300
301 RENPLYSHGFILTVKGTELNDDRSPSDATVTTTFNILVIDINDNAPEFNS 350
351 SEYSVAITELAQVGFALPLFIQVVDKDEGLNSMFEVYLVGNNSHHFIISP 400
401 TSVQGKADIRIRVAIPLDYETVDRYDFDLFANESVPDHVGYAKVKITLIN 450
451 ENDNRPIFSQPLYNVSLYENITVGTSVLTVLATDNDVGTFGEVNYFFSDD 500
501 PDRFSLDKDTGLIMLIARLDYELIQRFTLTVIARDGGGEETTGRVRINVL 550
551 DVNDNVPTFQKDAYVGALRENEPSVTQLVRLRATDEDSPPNNLITYSIVN 600
601 ASAFGSYFDISVYEGYGVISVSRPLDYEQIPNGLIYLTVMAKDAGNPPLY 650
651 STVPVTIEVFDENDNPPTFSKPAYFVSVVENIMAGATVLFLNATDLDRSR 700
701 EYGQESIIYSLEGSSQFRINARSGEITTTSLLDRETKAEYILIVRAVDGG 750
751 VGHNQKTGIATVNVTLLDINDNHPTWKDAPYYINLVEMTPPDSDVTTVVA 800
801 VDPDLGKNGTLVYSIQPPNKFYSLNSTTGKIRTTHVMLDRENPDPVEAEL 850
851 MRKIIVSVTDCGRPPLKATSSATVFVNLLDLNDNDPTFQNLPFVAEVLEG 900
901 TPAGVSVYQVVAIDLDEGLNGLVSYRMQVGMPRMDFVINSTSGVVTTTAE 950
951 LDRERIAEYQLRVVASDAGTPTKSSTSTLTIRVLDVNDETPTFFPAVYNV 1000
1001 SVSEDVPREFRVVWLNCTDNDVGLNAELSYFITAGNVDGKFSVGYRDAVV 1050
1051 RTVVGLDRETTAAYTLVLEAIDNVPVGKRRTGTATVFVTVLDVNDNRPIF 1100
1101 LQSSYEASVPEDIPEGHSIVQLKATDADEGEFGRVWYRILHGNHGNNFRL 1150
1151 HVSSGLLVRGPRPLDRERNSSHVLMAEAYNHDLGPMRSSVRVIVYVEDVN 1200
1201 DEAPVFTQQQYNRLGLRETAGIGTSVIVVRATDRDTGDGGLVNYRILSGA 1250
1251 EGKFEIDESTGLIVTVDYLDYETKTSYLMNVSATDGAPPFNQGFCSVYVT 1300
1301 LLNELDEAVQFSNASYEAVIMENLALGTEIVRVQAYSIDNLNQITYRFDA 1350
1351 YTSAQAKALFKIDAITGVITVKGLVDREKGDFYTLTVVADDGGPKVDSTV 1400
1401 KVYVTVLDENDNSPRFDFTSDSAISVPEDCPVGQRVATVKARDPDAGSNG 1450
1451 QVVFSLASGNIAGAFEIITSNDSIGEVFVAKPLDREELDHYILKIVASDR 1500
1501 GTPPRKKDHILQVTILDVNDNPPVIESPFGYNVSVNENVGGGTSVVQVRA 1550
1551 TDRDIGINSVLSYYITEGNEDMTFRMDRISGEIATRPAPPDRERQNFYHL 1600
1601 VVTVEDEGTPTLSATTHVYVTIVDENDNAPVFQQPHYEVVLDEGPDTVNT 1650
1651 SLITVQALDLDEGPNGTVTYAIVAGNIINTFRINRRTGVITAAKELDYEI 1700
1701 SHGRYTLIVTATDQCPILSHRLTSTTTVLVNVNDINDNVPTFPRDYEGPF 1750
1751 DVTEGQPGPRVWTFLAHDRDSGPNGQVEYSVVDGDPLGEFVISPVEGVLR 1800
1801 VRKDVELDRETIAFYNLTICARDRGVPPLSSTMLVGIRVLDINDNDPVLL 1850
1851 NLPMNITISENSPVSSFVAHVLASDADSGCNALLTFNITAGNRERAFFIN 1900
1901 ATTGIVTVNRPLDRERIPEYRLTVSVKDNPENPRIARKDFDLLLVSLADE 1950
1951 NDNHPLFTEGTYQAEVMENSPAGTPLTVLNGPILALDADEDVYAVVTYQL 2000
2001 LGTHSDLFVIDNSTGVVTVRSGVIIDREAFSPPFLELLLLAEDVGQLNGT 2050
2051 AYLFITILDDNDNWPTFSPPAYTVHLLENCPPGFSVLQITATDEDSGLNG 2100
2101 ELVYRIEAGAQDRFLIHPVTGVIRVGNATIDREEQESYRLTVVATDRGTV 2150
2151 PLSGTATVTILIDDINDSRPEFLNPIQTVSVLESTEPGTVIANVTAIDLD 2200
2201 LNPKLEYHILSIVAKDDTDRLVPDQEDAFAVNINTGSVIVKSPLNRELVA 2250
2251 TYEVTLSVIDNASDLPERSVSVPNAKLTVNILDVNDNTPQFKPFGITYYT 2300
2301 ERVLEGATPGTTLIAVAAVDPDKGLNGLITYTLLDLIPPGYVQLEDSSAG 2350
2351 KVIANRTVDYEEVHWLNFTVRASDNGSPPRAAEIPVYLEIVDINDNNPIF 2400
2401 DQLSYQEAVFEDVAVGTVILRVTATDADSGNFALIEYSLVDGEGKFAINP 2450
2451 NTGDIYVLSSLDREKKDHYILTALAKDNPGDVASNRRENSVQVVIRVLDV 2500
2501 NDCRPQFSKPQFSTSVYENEPAGTSVITMLATDQDEGSNGQLTYSLEGPG 2550
2551 MEAFSVDMDSGLVTTQRPLQSYERFNLTVVATDGGEPPLWGTTMLLVEVI 2600
2601 DVNDNRPVFVRPPNGTILHIKEEIPLRSNVYEVYATDKDEGLNGAVRYSF 2650
2651 LKSTGNRDWEYFTIDPISGLIQTAQRLDREKQAVYSLILVASDLGQPVPY 2700
2701 ETMQPLQVALEDIDDNEPLFVRPPKGSPQYQLLTVPEHSPRGTLVGNVTG 2750
2751 AVDADEGPNAIVYYFIAAGNEDKNFHLQPDGRLLVLRDLDRETEAIFSFI 2800
2801 VKASSNRSWTPPRGPSPALDLLTDLTLQEVRVVLEDINDQPPRFTKAEYT 2850
2851 AGVATDAKVGSELIQVLALDADIGNNSLVFYGILAIHYFRALANDSEDVG 2900
2901 QVFTMGSVDGILRTFDLFMAYSPGYFVVDIVARDLAGHNDTAIIGIYILR 2950
2951 DDQRVKIVINEIPDRVRGFEEEFIRLLSNITGAIVNTDDVQFHVDMKGRV 3000
3001 NFAQTELLIHVVNRDTNRILDVDRVIQMIDENKEQLRNLFRNYNVLDVQP 3050
3051 AISVQLPDDMSALQMAIIVLAILLFLAAMLFVLMNWYYRTIHKRKLKAIV 3100
3101 AGSAGNRGFIDIMDMPNTNKYSFDGANPVWLDPFCRNLELAAQAEHEDDL 3150
3151 PENLSEIADLWNSPTRTHGTFGREPAAVKPEDDRYLRAAIQEYDNIAKLG 3200
3201 QIIREGPIKLIHTDLEEEPGDHSPGQGSLRFRHKPPTELKGPDGIHIVHG 3250
3251 STGTLLATDLNSLPEDDQKGLDRSLETLTASEATAFERNARTESAKSTPL 3300
3301 HKLRDVIMESPLEITEL 3317
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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