 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P49816 from www.uniprot.org...
The NucPred score for your sequence is 0.81 (see score help below)
1 MAKPTSKDSGLKEKFKILLGLGTSRPNPRCAEGKQTEFIITAEILRELSG 50
51 ECGLNNRIRMIGQICDVAKTKKLEEHAVEALWKAVSDLLQPERPPEARHA 100
101 VLALLKAIVQGQGDRLGVLRALFFKVIKDYPSNEDLHERLEVFKALTDNG 150
151 RHITYLEEELAEFVLQWMDVGLSSEFLLVLVNLVKFNSCYLDEYIAPMVH 200
201 MICLLCIRTVSSVDIEVSLQVLDAVVCYNCLPAESLPLFIITLCRTVNVK 250
251 ELCEPCWKLMRNLLGTHLGHSAIYNMCRIMENRSYMEDAPLLRGAVFFVG 300
301 MALWGAHRLYSLKNSPTSVLPSFYEAMTCPNEVVSYEIVLSITRLIKKYR 350
351 KELQAVTWDILLDIIERLLQQLQNLDSPELRTIVHDLLTTVEELCDQNEF 400
401 HGSQERYYELVESYADQRPESSLLNLITYRAQSIHPAKDGWIQNLQLLME 450
451 RFFRNECRSAVRIKVLDVLSFVLLINRQFYEEELINSVVISQLSHIPEDK 500
501 DHQVRKLATQLLVDLAEGCHTHHFNSLLDIIEKVMARSLSPPLELEERDL 550
551 AVYSASLEDVKTAVLGLLVILQTKLYTLPASHATRVYETLISHIQLHYKH 600
601 GYSLPIASSIRLQAFDFLLLLRADSLHRLGLPNKDGVVRFSPYCLCDCAE 650
651 LDRASEKKASGPLSPPTGPPSPVPTGPAVRLGHLPYSLLFRVLLQCLKQE 700
701 TDWKVLKLVLSKLPESLRYKVLIFTSPCSVDQLSSALCSMLSAPKTLERL 750
751 RGTPEGFSRTDLHLAVVPVLTALISYHNYLDKTRQREMVYCLEQGLIYRC 800
801 ASQCVVALAICSVEMPDIIIKALPVLVVKLTHISATASMAIPLLEFLSTL 850
851 ARLPHLYRNFAAEQYASVFAISLPYTNPSKFNQYIVCLAHHVIAMWFIRC 900
901 RLPFRKDFVPYITKGLRSNVLLSFDDTPEKDKFRARSTSLNERPKSLRIA 950
951 RAPKQGLNNSPPVKEFKESCAAEAFRCRSISVSEHVVRSRIQTSLTSASL 1000
1001 GSADENSMAQADDNLKNLHLELTETCLDMMARYVFSNFTAVPKRSPVGEF 1050
1051 LLAGGRTKTWLVGNKLVTVTTSVGTGTRSLLGLDSGDLQGGSASSSDPGT 1100
1101 HVRQTKEAPAKLESQAGQQVSRGARDRVRSMSGGHGLRVGVLDTSAPYTP 1150
1151 GGPASLGAQAAPAARPEKPCAGAQLPAAEKANLAAYVPLLTQGWAEILVR 1200
1201 RPTGNTSWLMSLENPLSPFSSDINNMPLQELSNALMAAERFKEHRDTALY 1250
1251 KSLSVPAAGTAKPPTLPRSNTVASFSSLYQPSCQGQLHRSVSWADSAVVL 1300
1301 EEGSPGEAHVPVEPPELEDFEAALGTDRHCQRPDAYSRSSSASSQEEKSH 1350
1351 LEELAAGGIPIERAISSEGARPTVDLSFQPSQPLSKSSSSPELQTLQDIL 1400
1401 GDLGDKTDIGRLSPEAKVRSQSGILDGEAATWSAPGEESRITVPPEGPLP 1450
1451 SSSPRSPSGLRPRGYTISDSAPSRRGKRVERDNFKSRTAASSAEKVPGIN 1500
1501 PSFVFLQLYHSPFCGDESNKPILLPNESFERSVQLLDQIPSYDTHKIAVL 1550
1551 YVGEGQSSSELAILSNEHGSYRYTEFLTGLGRLIELKDCQPDKVYLGGLD 1600
1601 VCGEDGQFTYCWHDDIMQAVFHIATLMPTKDVDKHRCDKKRHLGNDFVSI 1650
1651 IYNDSGEDFKLGTIKGQFNFVHVIITPLDYKCNLLTLQCRKDMEGLVDTS 1700
1701 VAKIVSDRNLSFVARQMALHANMASQVHHRRSNPTDIYPSKWIARLRHIK 1750
1751 RLRQRIREEVHYSNPSLPLMHPPAHTKVPAQAPTEATPTYETGQRKRLIS 1800
1801 SVDDFTEFV 1809
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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