 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9HBL0 from www.uniprot.org...
The NucPred score for your sequence is 0.81 (see score help below)
1 MTWICLSCMLWPEDLEAPKTHRFKVKTFKKVKPCGICRQVITQEGCTCKV 50
51 CSFSCHRKCQAKVAAPCVPPSNHELVPITTENAPKNVVDKGEGASRGGNT 100
101 RKSLEDNGSTRVTPSVQPHLQPIRNMSVSRTMEDSCELDLVYVTERIIAV 150
151 SFPSTANEENFRSNLREVAQMLKSKHGGNYLLFNLSERRPDITKLHAKVL 200
201 EFGWPDLHTPALEKICSICKAMDTWLNADPHNVVVLHNKGNRGRIGVVIA 250
251 AYMHYSNISASADQALDRFAMKRFYEDKIVPIGQPSQRRYVHYFSGLLSG 300
301 SIKMNNKPLFLHHVIMHGIPNFESKGGCRPFLRIYQAMQPVYTSGIYNIP 350
351 GDSQTSVCITIEPGLLLKGDILLKCYHKKFRSPARDVIFRVQFHTCAIHD 400
401 LGVVFGKEDLDDAFKDDRFPEYGKVEFVFSYGPEKIQGMEHLENGPSVSV 450
451 DYNTSDPLIRWDSYDNFSGHRDDGMEEVVGHTQGPLDGSLYAKVKKKDSL 500
501 HGSTGAVNATRPTLSATPNHVEHTLSVSSDSGNSTASTKTDKTDEPVPGA 550
551 SSATAALSPQEKRELDRLLSGFGLEREKQGAMYHTQHLRSRPAGGSAVPS 600
601 SGRHVVPAQVHVNGGALASERETDILDDELPNQDGHSAGSMGTLSSLDGV 650
651 TNTSEGGYPEALSPLTNGLDKSYPMEPMVNGGGYPYESASRAGPAHAGHT 700
701 APMRPSYSAQEGLAGYQREGPHPAWPQPVTTSHYAHDPSGMFRSQSFSEA 750
751 EPQLPPAPVRGGSSREAVQRGLNSWQQQQQQQQQPRPPPRQQERAHLESL 800
801 VASRPSPQPLAETPIPSLPEFPRAASQQEIEQSIETLNMLMLDLEPASAA 850
851 APLHKSQSVPGAWPGASPLSSQPLSGSSRQSHPLTQSRSGYIPSGHSLGT 900
901 PEPAPRASLESVPPGRSYSPYDYQPCLAGPNQDFHSKSPASSSLPAFLPT 950
951 THSPPGPQQPPASLPGLTAQPLLSPKEATSDPSRTPEEEPLNLEGLVAHR 1000
1001 VAGVQAREKQPAEPPAPLRRRAASDGQYENQSPEATSPRSPGVRSPVQCV 1050
1051 SPELALTIALNPGGRPKEPHLHSYKEAFEEMEGTSPSSPPPSGVRSPPGL 1100
1101 AKTPLSALGLKPHNPADILLHPTGEPRSYVESVARTAVAGPRAQDSEPKS 1150
1151 FSAPATQAYGHEIPLRNGTLGGSFVSPSPLSTSSPILSADSTSVGSFPSG 1200
1201 ESSDQGPRTPTQPLLESGFRSGSLGQPSPSAQRNYQSSSPLPTVGSSYSS 1250
1251 PDYSLQHFSSSPESQARAQFSVAGVHTVPGSPQARHRTVGTNTPPSPGFG 1300
1301 WRAINPSMAAPSSPSLSHHQMMGPPGTGFHGSTVSSPQSSAATTPGSPSL 1350
1351 CRHPAGVYQVSGLHNKVATTPGSPSLGRHPGAHQGNLASGLHSNAIASPG 1400
1401 SPSLGRHLGGSGSVVPGSPCLDRHVAYGGYSTPEDRRPTLSRQSSASGYQ 1450
1451 APSTPSFPVSPAYYPGLSSPATSPSPDSAAFRQGSPTPALPEKRRMSVGD 1500
1501 RAGSLPNYATINGKVSSPVASGMSSPSGGSTVSFSHTLPDFSKYSMPDNS 1550
1551 PETRAKVKFVQDTSKYWYKPEISREQAIALLKDQEPGAFIIRDSHSFRGA 1600
1601 YGLAMKVSSPPPTIMQQNKKGDMTHELVRHFLIETGPRGVKLKGCPNEPN 1650
1651 FGSLSALVYQHSIIPLALPCKLVIPNRDPTDESKDSSGPANSTADLLKQG 1700
1701 AACNVLFVNSVDMESLTGPQAISKATSETLAADPTPAATIVHFKVSAQGI 1750
1751 TLTDNQRKLFFRRHYPLNTVTFCDLDPQERKWMKTEGGAPAKLFGFVARK 1800
1801 QGSTTDNACHLFAELDPNQPASAIVNFVSKVMLNAGQKR 1839
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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