 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9ERU9 from www.uniprot.org...
The NucPred score for your sequence is 0.78 (see score help below)
1 MRRSKADVERYIASVQGSAPSPREKSMKGFYFAKLYYEAKEYDLAKKYIS 50
51 TYINVQERDPKAHRFLGLLYEVEENIDKAVECYKRSVELNPTQKDLVLKI 100
101 AELLCKNDVTDGRAKYWVERAAKLFPGSPAIYKLKEQLLDCKGEDGWNKL 150
151 FDLIQSELYARPDDIHVNIRLVELYRSNKRLKDAVAHCHEADRNTALRSS 200
201 LEWNLCVVQTLKEYLESLQCLDSDKSTWRATNKDLLLAYANLMLLTLSTR 250
251 DVQEGRELLESFDSALQSVKSSVGGNDELSATFLETKGHFYMHVGSLLLK 300
301 MGQQSDIQWRALSELAALCYLVAFQVPRPKVKLIKGEAGQNLLETMAHDR 350
351 LSQSGHMLLNLSRGKQDFLKEVVESFANKSGQSALCDALFSSQSSKERSF 400
401 LGNDDIGNLDGQVPDPDDLARYDTGAVRAHNGSLQHLTWLGLQWNSLSTL 450
451 PAIRKWLKQLFHHLPQETSRLETNAPESICILDLEVFLLGVIYTSHLQLK 500
501 EKCNSHHTSYQPLCLPLPVCRQLCTERQKTWWDAVCTLIHRKALPGTSAK 550
551 LRLLVQREINSLRGQEKHGLQPALLVHWAQSLQKTGSSLNSFYDQREYIG 600
601 RSVHYWRKVLPLLKMIRKKNSIPEPIDPLFKHFHSVDIQASEIGEYEEDA 650
651 HITFAILDAVNGNIEDAMTAFESIKNVVSYWNLALIFHRKAEDIENDALS 700
701 PEEQEECKNYLRKTRDYLIRILDDSDSNTSVVQKLPVPLESVKEMLNSVM 750
751 QELEDYSEGGTLYKNGCWRSADSELKHSTPSPTKYSLSPSKSYKYSPKTP 800
801 PRWAEDQNSLLKMICQQVEAIKKEMQELKLNSNNSASPHRWPAEPYGQDP 850
851 APDGYQGSQTFHGAPLTVATTGPSVYYSQSPAYNSQYLLRPAANVTPTKG 900
901 PVYGMNRLPPQQHIYAYSQQMHTPPVQSSSACMFSQEMYGPPLRFESPAT 950
951 GILSPRGDDYFNYNVQQTSTNPPLPEPGYFTKPPLVAHASRSAESKVIEF 1000
1001 GKSNFVQPMQGEVIRPPLTTPAHTTQPTPFKFNSNFKSNDGDFTFSSPQV 1050
1051 VAQPPSTAYSNSESLLGLLTSDKPLQGDGYSGLKPISGQASGSRNTFSFG 1100
1101 SKNTLTENMGPNQQKNFGFHRSDDMFAFHGPGKSVFTTAASELANKSHET 1150
1151 DGGSAHGDEEDDGPHFEPVVPLPDKIEVKTGEEDEEEFFCNRAKLFRFDG 1200
1201 ESKEWKERGIGNVKILRHKTSGKIRLLMRREQVLKICANHYISPDMKLTP 1250
1251 NAGSDRSFVWHALDYADELPKPEQLAIRFKTPEEAALFKCKFEEAQNILK 1300
1301 ALGTNTSTAPNHTLRIVKESATQDNKDICKADGGNLNFEFQIVKKEGPYW 1350
1351 NCNSCSFKNAATAKKCVSCQNTNPTSNKELLGPPLVENGFAPKTGLENAQ 1400
1401 DRFATMTANKEGHWDCSVCLVRNEPTVSRCIACQNTKSASSFVQTSFKFG 1450
1451 QGDLPKSVDSDFRSVFSKKEGQWECSVCLVRNERSAKKCVACENPGKQFK 1500
1501 EWHCSLCSVKNEAHAIKCVACNNPVTPSLSTAPPSFKFGTSEMSKPFRIG 1550
1551 FEGMFAKKEGQWDCSLCFVRNEASATHCIACQYPNKQNQPTSCVSAPASS 1600
1601 ETSRSPKSGFEGLFPKKEGEWECAVCSVQNESSSLKCVACEASKPTHKPH 1650
1651 EAPSAFTVGSKSQSNESAGSQVGTEFKSNFPEKNFKVGISEQKFKFGHVD 1700
1701 QEKTPSFAFQGGSNTEFKSIKDGFSFCIPVSADGFKFGIQEKGNQEKKSE 1750
1751 KHLENDPSFQAHDTSGQKNGSGVVFGQTSSTFTFADLAKSTSREGFQFGK 1800
1801 KDPNFKGFSGAGEKLFSSQSGKVAEKANTSSDLEKDDDAYKTEDSDDIHF 1850
1851 EPVVQMPEKVELVTGEEDEKVLYSQRVKLFRFDAEISQWKERGLGNLKIL 1900
1901 KNEVNGKLRMLMRREQVLKVCANHWITTTMNLKPLSGSDRAWMWLASDFS 1950
1951 DGDAKLEQLAAKFKTPELAEEFKQKFEECQRLLLDIPLQTPHKLVDTGRA 2000
2001 AKLIQRAEEMKSGLKDFKTFLTNDQVKVTDEENASSGADAPSASDTTAKQ 2050
2051 NPDNTGPALEWDNYDLREDALDDSVSSSSVHASPLASSPVRKNLFRFGES 2100
2101 TTGFNFSFKSALSPSKSPAKLNQSGASVGTDEESDVTQEEERDGQYFEPV 2150
2151 VPLPDLVEVSSGEENEQVVFSHRAKLYRYDKDVGQWKERGIGDIKILQNY 2200
2201 DNKQVRIVMRRDQVLKLCANHRITPDMTLQTMKGTERVWVWTACDFADGE 2250
2251 RKIEHLAVRFKLQDVADSFKKIFDEAKTAQEKDSLITPHVSHLSTPRESP 2300
2301 CGKIAIAVLEETTRERTDLTQGDEVIDTTSEAGETSSTSETTPKAVVSPP 2350
2351 KFVFGSESVKSIFSSEKSKPFAFGNSSATGSLFGFSFNAPLKNSNSEMTS 2400
2401 RVQSGSEGKVKPDKCELPQNSDIKQSSDGKVKNLSAFSKENSSTSYTFKT 2450
2451 PEKAQEKSKPEDLPSDNDILIVYELTPTPEQKALAEKLLLPSTFFCYKNR 2500
2501 PGYVSEEEEDDEDYEMAVKKLNGKLYLDDSEKPLEENLADNDKECVIVWE 2550
2551 KKPTVEERAKADTLKLPPTFFCGVCSDTDEDNGNGEDFQSELRKVCEAQK 2600
2601 SQNEKVTDRVGIEHIGETEVTNPVGCKSEEPDSDTKHSSSSPVSGTMDKP 2650
2651 VDLSTRKETDMEFPSKGENKPVLFGFGSGTGLSFADLASSNSGDFAFGSK 2700
2701 DKNFQWANTGAAVFGTQTTSKGGEDEDGSDEDVVHNEDIHFEPIVSLPEV 2750
2751 EVKSGEEDEEVLFKERAKLYRWDRDVSQWKERGIGDIKILWHTMKKYYRI 2800
2801 LMRRDQVFKVCANHVITKAMELKPLNVSNNALVWTASDYADGEAKVEQLA 2850
2851 VRFKTKEMTESFKKKFEECQQNIIKLQNGHTSLAAELSKDTNPVVFFDVC 2900
2901 ADGEPLGRIIMELFSNIVPQTAENFRALCTGEKGFGFKNSIFHRVVPDFI 2950
2951 CQGGDITKYNGTGGQSIYGDKFDDENFDLKHTGPGLLSMANYGQNTNSSQ 3000
3001 FFITLKKAEHLDFKHVVFGFVKDGMDTVRKIESFGSPKGSVSRRICITEC 3050
3051 GQL 3053
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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