 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O75962 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MSGSSGGAAAPAASSGPAAAASAAGSGCGGGAGEGAEEAAKDLADIAAFF 50
51 RSGFRKNDEMKAMDVLPILKEKVAYLSGGRDKRGGPILTFPARSNHDRIR 100
101 QEDLRRLISYLACIPSEEVCKRGFTVIVDMRGSKWDSIKPLLKILQESFP 150
151 CCIHVALIIKPDNFWQKQRTNFGSSKFEFETNMVSLEGLTKVVDPSQLTP 200
201 EFDGCLEYNHEEWIEIRVAFEDYISNATHMLSRLEELQDILAKKELPQDL 250
251 EGARNMIEEHSQLKKKVIKAPIEDLDLEGQKLLQRIQSSESFPKKNSGSG 300
301 NADLQNLLPKVSTMLDRLHSTRQHLHQMWHVRKLKLDQCFQLRLFEQDAE 350
351 KMFDWITHNKGLFLNSYTEIGTSHPHAMELQTQHNHFAMNCMNVYVNINR 400
401 IMSVANRLVESGHYASQQIRQIASQLEQEWKAFAAALDERSTLLDMSSIF 450
451 HQKAEKYMSNVDSWCKACGEVDLPSELQDLEDAIHHHQGIYEHITLAYSE 500
501 VSQDGKSLLDKLQRPLTPGSSDSLTASANYSKAVHHVLDVIHEVLHHQRQ 550
551 LENIWQHRKVRLHQRLQLCVFQQDVQQVLDWIENHGEAFLSKHTGVGKSL 600
601 HRARALQKRHEDFEEVAQNTYTNADKLLEAAEQLAQTGECDPEEIYQAAH 650
651 QLEDRIQDFVRRVEQRKILLDMSVSFHTHVKELWTWLEELQKELLDDVYA 700
701 ESVEAVQDLIKRFGQQQQTTLQVTVNVIKEGEDLIQQLRDSAISSNKTPH 750
751 NSSINHIETVLQQLDEAQSQMEELFQERKIKLELFLQLRIFERDAIDIIS 800
801 DLESWNDELSQQMNDFDTEDLTIAEQRLQHHADKALTMNNLTFDVIHQGQ 850
851 DLLQYVNEVQASGVELLCDRDVDMATRVQDLLEFLHEKQQELDLAAEQHR 900
901 KHLEQCVQLRHLQAEVKQVLGWIRNGESMLNAGLITASSLQEAEQLQREH 950
951 EQFQHAIEKTHQSALQVQQKAEAMLQANHYDMDMIRDCAEKVASHWQQLM 1000
1001 LKMEDRLKLVNASVAFYKTSEQVCSVLESLEQEYKREEDWCGGADKLGPN 1050
1051 SETDHVTPMISKHLEQKEAFLKACTLARRNADVFLKYLHRNSVNMPGMVT 1100
1101 HIKAPEQQVKNILNELFQRENRVLHYWTMRKRRLDQCQQYVVFERSAKQA 1150
1151 LEWIHDNGEFYLSTHTSTGSSIQHTQELLKEHEEFQITAKQTKERVKLLI 1200
1201 QLADGFCEKGHAHAAEIKKCVTAVDKRYRDFSLRMEKYRTSLEKALGISS 1250
1251 DSNKSSKSLQLDIIPASIPGSEVKLRDAAHELNEEKRKSARRKEFIMAEL 1300
1301 IQTEKAYVRDLRECMDTYLWEMTSGVEEIPPGIVNKELIIFGNMQEIYEF 1350
1351 HNNIFLKELEKYEQLPEDVGHCFVTWADKFQMYVTYCKNKPDSTQLILEH 1400
1401 AGSYFDEIQQRHGLANSISSYLIKPVQRITKYQLLLKELLTCCEEGKGEI 1450
1451 KDGLEVMLSVPKRANDAMHLSMLEGFDENIESQGELILQESFQVWDPKTL 1500
1501 IRKGRERHLFLFEMSLVFSKEVKDSSGRSKYLYKSKLFTSELGVTEHVEG 1550
1551 DPCKFALWVGRTPTSDNKIVLKASSIENKQDWIKHIREVIQERTIHLKGA 1600
1601 LKEPIHIPKTAPATRQKGRRDGEDLDSQGDGSSQPDTISIASRTSQNTLD 1650
1651 SDKLSGGCELTVVIHDFTACNSNELTIRRGQTVEVLERPHDKPDWCLVRT 1700
1701 TDRSPAAEGLVPCGSLCIAHSRSSMEMEGIFNHKDSLSVSSNDASPPASV 1750
1751 ASLQPHMIGAQSSPGPKRPGNTLRKWLTSPVRRLSSGKADGHVKKLAHKH 1800
1801 KKSREVRKSADAGSQKDSDDSAATPQDETVEERGRNEGLSSGTLSKSSSS 1850
1851 GMQSCGEEEGEEGADAVPLPPPMAIQQHSLLQPDSQDDKASSRLLVRPTS 1900
1901 SETPSAAELVSAIEELVKSKMALEDRPSSLLVDQGDSSSPSFNPSDNSLL 1950
1951 SSSSPIDEMEERKSSSLKRRHYVLQELVETERDYVRDLGYVVEGYMALMK 2000
2001 EDGVPDDMKGKDKIVFGNIHQIYDWHRDFFLGELEKCLEDPEKLGSLFVK 2050
2051 HERRLHMYIAYCQNKPKSEHIVSEYIDTFFEDLKQRLGHRLQLTDLLIKP 2100
2101 VQRIMKYQLLLKDFLKYSKKASLDTSELERAVEVMCIVPRRCNDMMNVGR 2150
2151 LQGFDGKIVAQGKLLLQDTFLVTDQDAGLLPRCRERRIFLFEQIVIFSEP 2200
2201 LDKKKGFSMPGFLFKNSIKVSCLCLEENVENDPCKFALTSRTGDVVETFI 2250
2251 LHSSSPSVRQTWIHEINQILENQRNFLNALTSPIEYQRNHSGGGGGGGSG 2300
2301 GSGGGGGSGGGGAPSGGSGHSGGPSSCGGAPSTSRSRPSRIPQPVRHHPP 2350
2351 VLVSSAASSQAEADKMSGTSTPGPSLPPPGAAPEAGPSAPSRRPPGADAE 2400
2401 GSEREAEPIPKMKVLESPRKGAANASGSSPDAPAKDARASLGTLPLGKPR 2450
2451 AGAASPLNSPLSSAVPSLGKEPFPPSSPLQKGGSFWSSIPASPASRPGSF 2500
2501 TFPGDSDSLQRQTPRHAAPGKDTDRMSTCSSASEQSVQSTQSNGSESSSS 2550
2551 SNISTMLVTHDYTAVKEDEINVYQGEVVQILASNQQNMFLVFRAATDQCP 2600
2601 AAEGWIPGFVLGHTSAVIVENPDGTLKKSTSWHTALRLRKKSEKKDKDGK 2650
2651 REGKLENGYRKSREGLSNKVSVKLLNPNYIYDVPPEFVIPLSEVTCETGE 2700
2701 TVVLRCRVCGRPKASITWKGPEHNTLNNDGHYSISYSDLGEATLKIVGVT 2750
2751 TEDDGIYTCIAVNDMGSASSSASLRVLGPGMDGIMVTWKDNFDSFYSEVA 2800
2801 ELGRGRFSVVKKCDQKGTKRAVATKFVNKKLMKRDQVTHELGILQSLQHP 2850
2851 LLVGLLDTFETPTSYILVLEMADQGRLLDCVVRWGSLTEGKIRAHLGEVL 2900
2901 EAVRYLHNCRIAHLDLKPENILVDESLAKPTIKLADFGDAVQLNTTYYIH 2950
2951 QLLGNPEFAAPEIILGNPVSLTSDTWSVGVLTYVLLSGVSPFLDDSVEET 3000
3001 CLNICRLDFSFPDDYFKGVSQKAKEFVCFLLQEDPAKRPSAALALQEQWL 3050
3051 QAGNGRSTGVLDTSRLTSFIERRKHQNDVRPIRSIKNFLQSRLLPRV 3097
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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