 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q61037 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MAKPTSKDSGLKEKFKILLGLGTSRPNPRCAEGKQTEFIITSEILRELSG 50
51 ECGLNNRIRMIGQICDVAKTKKLEEHAVEALWKAVSDLLQPERPPEARHA 100
101 VLTLLKAIVQGQGDRLGVLRALFFKVIKDYPSNEDLHERLEVFKALTDNG 150
151 RHITYLEEELAEFVLQWMDVGLSSEFLLVLVNLVKFNSCYLDEYIASMVH 200
201 MICLLCIRTVSSVDIEVSLQVLDAVVCYNCLPAESLPLFIITLCRTINVK 250
251 ELCEPCWKLMRNLLGTHLGHSAIYNMCRIMEDRSYMEDAPLLRGAVFFVG 300
301 MALWGAHRLYSLKNSPTSVLPSFYEAMTCPNEVVSYEIVLSITRLIKKYR 350
351 KELQAVTWDILLDIIERLLQQLQNLDSPELKTIVHDLLTTVEELCDQNEF 400
401 HGSQERYYELVESYADQRPESSLLNLISYRAQSIHPAKDGWIQNLQLLME 450
451 RFFRNECRSAVAIKVLDVLSFVLLIIRQFYEEELINSVVISQLSHIPEDK 500
501 DHQVRKLATQLLVDLAEGCHTHHFNSLLDIIEKVMARSLSPPPELEERDL 550
551 AVHSASLEDVKTAVLGLLVILQTKLYTLPASHATRVYESLISHIQLHYKH 600
601 GYSLPIASSIRLQAFDFLLLLRADSLHRLGLPNKDGVVRFSPYCLCDCME 650
651 LDRASEKKASGPLSPPTGPPSPVPMGPAVRLGYLPYSLLFRVLLQCLKQE 700
701 SDWKVLKLVLSRLPESLRYKVLIFTSPCSVDQLSSALCSMLSAPKTLERL 750
751 RGTPEGFSRTDLHLAVVPVLTALISYHNYLDKTRQREMVYCLEQGLIYRC 800
801 ASQCVVALAICSVEMPDIIIKALPVLVVKLTHISATASMAIPLLEFLSTL 850
851 ARLPHLYRNFVPEQYASVFAISLPYTNPSKFNQYIVCLAHHVIAMWFIRC 900
901 RLPFRKDFVPYITKGLRSNVLLSFDDTPEKDSFRARSTSLNERPKSLRIA 950
951 RAPKQGLNNSPPVKEFKESCAAEAFRCRSISVSEHVVRSRIQTSLTSASL 1000
1001 GSADENSMAQADDNLKNLHLELTETCLDMMARYVFSNFTAVPKRSPVGEF 1050
1051 LLAGGRTKTWLVGNKLVTVTTSVGTGTRSLLGLDSGDLQGGSDSSSDPST 1100
1101 HVRQTKEAPAKLESQAGQQVSRGARDRVRSMSGGHGLRVGVLDTSAPYSP 1150
1151 GGSASLGPQTAVAAKPEKPPAGAQLPTAEKTNLAAYVPLLTQGWAEILVR 1200
1201 RPTGNTSWLMSLENPLSPFSSDINNMPLQELSNALMAAERFKEHGHAPVQ 1250
1251 VIVSATGCTAKPPTLPRSNTVASFSSLYQPSCQGQLHRSVSWADSAMVLE 1300
1301 EGSPGETQVPVEPPELEDFEAALGTDRHCQRPDTYSRSSSASSQEEKSHL 1350
1351 EELAAGGIPIERAISSEGARPAVDLSFQPSQPLSKSSSSPELQTLQDILG 1400
1401 DLGDKIDIGRLSPEAKVRSQSGILDGEAATWSATGEESRITVPPEGPLPS 1450
1451 SSPRSPSGLRPRGYTISDSAPSRRGKRVERDNFKSRAAASSAEKVPGINP 1500
1501 SFVFLQLYHSPFFGDESNKPILLPNESFERSVQLLDQIPSYDTHKIAVLY 1550
1551 VGEGQSSSELAILSNEHGSYRYTEFLTGLGRLIELKDCQPDKVYLGGLDV 1600
1601 CGEDGQFTYCWHDDIMQAVFHIATLMPTKDVDKHRCDKKRHLGNDFVSII 1650
1651 YNDSGEDFKLGTIKQGQFNFVHVIITPLDYKCNLLTLQCRKDGPACKCEW 1700
1701 WRQPGEIVVWALPVVMELTVTILLCHLQMASQVHHSRSNPTDIYPSKWIA 1750
1751 RLRHIKRLRQRIREEVHYSNPSLPLMHPPAHTKAPAQAPEATPTYETGQR 1800
1801 KRLISSVDDFTEFV 1814
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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