 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q61292 from www.uniprot.org...
The NucPred score for your sequence is 0.71 (see score help below)
1 MEWASGEPGRGRQGQPLPWELRLGLLLSVLAATLAQAPSLDVPGCSRGSC 50
51 YPATGDLLVGRADRLTASSTCGLHSPQPYCIVSHLQDEKKCFLCDSRRPF 100
101 SARDNPNSHRIQNVVTSFAPQRRTAWWQSENGVPMVTIQLDLEAEFHFTH 150
151 LIMTFKTFRPAAMLVERSADFGRTWHVYRYFSYDCGADFPGIPLAPPRRW 200
201 DDVVCESRYSEIEPSTEGEVIYRVLDPAIPIPDPYSSRIQNLLKITNLRV 250
251 NLTRLHTLGDNLLDPRREIREKYYYALYELVIRGNCFCYGHASQCAPAPG 300
301 APAHAEGMVHGACICKHNTRGLNCEQCQDFYQDLPWHPAEDGHTHACRKC 350
351 ECNGHTHSCHFDMAVYLASGNVSGGVCDGCQHNTAGRHCEFCRPFFYRDP 400
401 TKDMRDPAVCRPCDCDPMGSQDGGRCDSHDDPVLGLVSGQCRCKEHVVGT 450
451 RCQQCRDGFFGLSASDPRGCQRCQCNSRGTVPGSSPCDSSSGTCFCKRLV 500
501 TGHGCDRCLPGHWGLSHDLLGCRPCDCDVGGALDPQCDEATGQCRCRQHM 550
551 IGRRCEQVQPGYFRPFLDHLTWEAEAAQGQGLEVVERLVTNRETPSWTGP 600
601 GFVRLREGQEVEFLVTSLPRAMDYDLLLRWEPQVPEQWAELELMVQRPGP 650
651 VSAHSPCGHVLPKDDRIQGMLHPNTRFLVFPRPVCLEPGISYKLKLKLIG 700
701 TGGRAQPETSYSGLLIDSLVLQPHVLVLEMFSGGDAAALERRTTFERYRC 750
751 HEEGLMPSKAPLSETCAPLLISVSALIYNGALPCQCDPQGSLSSECSPHG 800
801 GQCRCKPGVVGRRCDVCATGYYGFGPAGCQACQCSPDGALSALCEGTSGQ 850
851 CPCRPGAFGLRCDHCQRGQWGFPNCRPCVCNGRADECDTHTGACLGCRDY 900
901 TGGEHCERCIAGFHGDPRLPYGGQCRPCPCPEGPGSQRHFATSCHRDGYS 950
951 QQIVCQCREGYTGLRCEACAPGHFGDPSKPGGRCQLCECSGNIDPMDPDA 1000
1001 CDPHTGQCLRCLHNTEGPHCGYCKPGFHGQAARQSCHRCTCNLLGTDPRR 1050
1051 CPSTDLCHCDPSTGQCPCLPHVQGLNCDHCAPNFWNFTSGRGCQPCACHP 1100
1101 SRARGPTCNEFTGQCHCHAGFGGRTCSECQELYWGDPGLQCRACDCDPRG 1150
1151 IDKPQCHRSTGHCSCRPGVSGVRCDQCARGFSGVFPACHPCHACFGDWDR 1200
1201 VVQDLAARTRRLEQWAQELQQTGVLGAFESSFLNMQGKLGMVQAIMSARN 1250
1251 ASAASTAKLVEATEGLRHEIGKTTERLTQLEAELTAVQDENFNANHALSG 1300
1301 LERDGLALNLTLRQLDQHLEILKHSNFLGAYDSIRHAHSQSTEAERRANA 1350
1351 STFAVPSPVSNSADTRRRTEVLMGAQKENFNRQHLANQQALGRLSAHAHT 1400
1401 LSLTGINELVCGAPGDAPCATSPCGGAGCRDEDGQPRCGGLGCSGAAATA 1450
1451 DLALGRARHTQAELQRALVEGGGILSRVSETRRQAEEAQQRAQAALDKAN 1500
1501 ASRGQVEQANQELRELIQNVKDFLSQEGADPDSIEMVATRVLDISIPASP 1550
1551 EQIQRLASEIAERVRSLADVDTILAHTMGDVRRAEQLLQDAHRARSRAEG 1600
1601 ERQKAETVQAALEEAQRAQGAAQGAIWGAVVDTQNTEQTLQRVQERMAGA 1650
1651 EKSLNSAGERARQLDALLEALKLKRAGNSLAASTAEETAGSAQSRAREAE 1700
1701 KQLREQVGDQYQTVRALAERKAEGVLAAQARAEQLRDEARDLLQAAQDKL 1750
1751 QRLQELEGTYEENERALEGKAAQLDGLEARMRSVLQAINLQVQIYNTCQ 1799
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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