 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9GLM4 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MTWICLSCMLWPEDLEAPKTHHFKVKTFKKVKPCGICRQVITREGCTCKV 50
51 CSFSCHRKCQAKVAAPCIPPSSHELVPVNTETAPKNVVDTGEAASRGGNT 100
101 RKSLEEDSSTRVTPSVQPHPQPIRNMSTSRTTEDSCELDLVYVTERIIAV 150
151 SFPSTANEENFRSNLREVAQMLKSKHGGNYLLFNLSERRPDITKLHAKVL 200
201 EFGWPDLHTPALEKICSVCKAMDTWLNADPHNVVVLHNKGNRGRIGVVIA 250
251 AYMHYSNISASADQALDRFAMKRFYEDKIVPIGQPSQRRYVHYFSGLLSG 300
301 SIKMNNKPLFLHHVIMHGIPNFESKGGCRPFLRIYQAMQPVYTSGIYNVQ 350
351 GDSQTSICITIEPGLLLKGDILLKCYHKKFRSPARDVIFRVQFHTCAIHD 400
401 LGVVFGKEDLDDAFKDDRFPEYGKVEFVFSYGPEKIQGMEHLENGPSVSV 450
451 DYNTSDPLIRWDSYDNFNGHRDDGMEEVVGHTQGPLDGSLYAKVKKKDSL 500
501 HGSTGAVNATRPVLSATPNHVEHTLSVSSDSGNSTASTKTDKTDEPAPGP 550
551 ASAPAALSPEEKRELDRLLSGFGLEREKQGAMYHPQHLRSRPVGGPAAPS 600
601 SGRHIVPAQVHVNGGALASERETDILDDELPNQDGHSVGSMGTLSSLDGV 650
651 TNTSEGGYPEALSPLTNGLDKPYPMEPMVNGGGYPYESASRAVSAQAGHT 700
701 APMRPSYSTQEGLAGYQREGPHPAWPQSATTSHYGHDPNGMFRSQSFPET 750
751 EPQLPPAPARGGSSREAVQRGLNSWQQQQQQQQQQQQPRPPPRQQERVHL 800
801 ESLGLSRPSPQPLAEPPMSGLPEFPRAASQQEIEQSIEALNMLMLDLEPA 850
851 TAGAPLHKSQSVPGAWPGASPLSSQPFSGSSCQSHPLTQSRSGYIPSGHS 900
901 LGTPEPAPRAPLESVPTGRPYSPYDYQPCPTGPNQSYHPKSPATSSSSSF 950
951 LPTTQSSVGPQQPPASLPGLTTQPQLPPKEVTSDPSRTPEEEPLNLEGLV 1000
1001 AHRVAAYNARLQGTGRRVSSWHPPLHRLRSSSLSGVQAREKQPAEPPAPL 1050
1051 RKRAASDGQYENQSPEPTSPRSPGVRSPVQCVSPELALTIALNPGGRPKE 1100
1101 PHLHSYKEAFEEMEGTSPTSPPPSGVRSPPGLAKTPLSALGLKPHNPADI 1150
1151 LLHPTGEEDERKVLTELSEEPRSYVESVVRTAVAGPRTQEPEPKSFSAPG 1200
1201 AQAYGHETPLRIGTLGGSFVSPSPLSTSSPILSADSTSVGSFPSGESSDQ 1250
1251 GARTPTQPLLDSGFRSGSLGQPSPLAQRNYQSSSPLPTAGSSYSSPDYSL 1300
1301 QQFSSPEGQARSQFSVAGVHTVPGSPQARHRTVGTNTPPSPGFGRRAVNP 1350
1351 SLAAPSSPSLSHRQVMGPLGTGFHGNTGSSPQSSAATTPGSPSLGRHPGA 1400
1401 HQVSNLHGNVVTAPGSPSLGRHPGAHQGTLASNLHSNAVASPGSPSLGRH 1450
1451 LGGSGSVVPGSPSLDRHVPYGGYSTPEDRRPTLSRQSSASGYQAPSTPSF 1500
1501 PVSPAYYPGLSSPATSPSPDSAAFRQGSPTPALPEKRRMSMGDRAGSLPN 1550
1551 YATVNGKVSSSPVASGMSSPSGGSTVSFSHTLPDFSKYSMPDNSPETRAK 1600
1601 VKFVQDTSKYWYKPEISREQAIALLKDQEPGAFIIRDSHSFRGAYGLAMK 1650
1651 VSSPPPTIMQQNKKGDMTHELVRHFLIETGPRGVKLKGCPNEPNFGSLSA 1700
1701 LVYQHSIIPLALPCKLVIPNRDPTDESKDSSGPANSTSDLLKQGAACNVL 1750
1751 FVNSVDMESLTGPQAISKAISETLAADPTPAATIVHFKVSAQGITLTDNQ 1800
1801 RKLFFRRHYPLNTVTFCDLDPQERKWTKTEGGAPAKLFGFVARKQGSATD 1850
1851 NACHLFAELDPNQPASAIVSFVSKVMLSAGQKR 1883
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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