 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P774 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MLSHNQNDLLFTKLIDKLTDYMSSKDAATSLASRVLTIAKGSSSSTEFSN 50
51 ALHTFGRFSDNDSVLIYDLCKSIIGLETNLGISKDKLPGNDGSQVAGLVL 100
101 SNRSGLKGREPKKSQLGLDVLAVQKKKEKSQVEGRSELSQVENDSLESIG 150
151 NYDRVEFKRPKNPHEKHFRPLRQRSSVDDHQEFESEDDKYRRNSYSSWSG 200
201 SNFESSNGSNRRRYRTQMEEPLSSKRRNRFGNGSRRDVDSSKYSHDSDYS 250
251 YGAHSSWDARDVEYPEEDPESKADRQRWEEEQAHLDRDWYMNSESQNLLG 300
301 DEVHNPFSDFETVEDRAHEAEFIEKQKKHLSIEASDRFKENSMWEKNRMI 350
351 TSGVSKAPGLESDYSLMEERRVHLLVDELRPHFLDGAEFSSKKVGDITSV 400
401 RDPQSDLAINARLGSRLVRERREFRERQKAASAATSLAGTSLGNVMGLKD 450
451 SNDEDAKAGTTPVKVAGRSEQSNKKDTEFARTKSYREQREFLPAFAVREQ 500
501 LLSVIRDNQVLIVVGETGSGKTTQLAQFLYEDGYHRNGMIGCTQPRRVAA 550
551 MSVAKRVSEEMGVRLGSTVGYSIRFEDVTGPDTVIKYMTDGVLLRESLMQ 600
601 NNLEKYSVIIMDEAHERSLNTDILMGLLKKVLSRRRDIKLLVTSATMNSQ 650
651 KFSDFFGGAPQFTIPGRTYPVDIMFAKAPCSDYVEAAVRQVLQIHLSQPA 700
701 GDILVFMTGQEDIEATCEIIADRLNQLHDAPRLSILPIYSQMPADLQAKI 750
751 FDSAEPGVRKVVVATNIAETSLTVHGISYVVDTGYCKLKMYNSKLGIDTL 800
801 QVTPISQANANQRAGRAGRTGPGIAYRLYTEMAYIREMFETTLPEIQRTN 850
851 LSNTVLILKSLGVEEISDFDFMDRPPNDTLMASLYELWTLGALDNFGKLT 900
901 TLGKKMSLFPMDPSLSKLIIIAEDYKCTEEIITIVSMLSVPSVFYRPKER 950
951 AEESDAAREKFNVPESDHLMLLNIYQHWQRNGYSNSWCSKHFLHSKTLKR 1000
1001 ARDIRQQLVEIMSKQKISLESVSDWDIVRRVLCSAYFHQAACAKGIGEYV 1050
1051 HLRSGMPCHLHVTSSLYGLGYLPDYVIYHELVLTSKEYMNIVTSVDPYWL 1100
1101 AEFGGVYYSVKERFRNETESYDRVFSSKPQLDAQIAADRELDAKQKLAKN 1150
1151 QEPVKSKRKSVIRAKPPRRVRGF 1173
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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