 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9BBN6 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MIFQSFILDNLVSLCLKIINSVIVVGLYYGFMTTFSTGPSYLFLLRAHVM 50
51 EEGTEKKISATTGFITGQLVMFISIYYAPLHIALDRPHTITVITLPYLLL 100
101 YFLGNNQKNFLNYVYKNQNSIRHFSIQRIFFQNLFFQLLNPFFLPSSILM 150
151 RLANIYIFQSNNKVLFLTSSFVGWLIGHVFFMKWIGLMLVWIQEKNNSIK 200
201 STVAIRSNKGVLAKFRKSMFQIFLIFFFITCLYYLGRIPPIYFFTPKMSE 250
251 IKERGEIEKREGEIDIEINSQRAGSKQEQKITAEEKLSPYLFSKKNNNLD 300
301 KIKEENDIFGFQKPLVTILFDYNRWNRPLRYIKNDRFENVVRNEISQFFF 350
351 FTCQSDGKERISFTYPPNLSTFQKMMEMKISLFTRDIISYEELSNSWRST 400
401 NEEKKKKLTNEFLNRVEVLDKESLPVDIFENRIRLCNDEKKQKYLTKEYD 450
451 PFLNGPCRGQIQKWFSPPIQKETYKKNSLFINKIHGILFSNTNNYPKFEQ 500
501 KKNIFDRKSLLTDINFFFNLITKFSRKSVSSLNFEGLYLFPKDNKGKMSS 550
551 KKKKFLFDTIRPDLNDNKIVNLQKCIGINEIVKKLPRWSYNLIDELEQLE 600
601 GKKKVEYHQIRSRKAKRVVLLTKNSQNDDNYDETTDTDNTEKKKELALIR 650
651 YSQQPDFRRDIIKGSIRAQRRKTVTCKLFQRSVDSPLFLEKMEKTSFFCF 700
701 DILDSSKIFFMFKNWIRKKKELKNSDYTDEKAKESQKKEEEKIKKNEKEE 750
751 KRRIEIGEAWDSIIFAQVIRGCLLITQSILRKYILLPSLIITKNIVRILL 800
801 FQFPEWSEDFRDWQREMYIKCTYNGVQLSETEFPKKWLTDGIQIKILFPF 850
851 RLKPWHRSKLRFTEKKKDPLKNKKVKKKNFCFLTIFGMEVELPFSGYPRN 900
901 RFSFFDPILKELKKKMKKLKNNFFLILKIVNERTKNFITTLKETSKRIIQ 950
951 SILKKVLFLNKKIKKLYNYLFLFRFKKIDELNQNKKNFPITKNNPIIYES 1000
1001 TILIQAINKTNCSLTEKKIKAINAKTKKIIKKIERMTKENKGGFLISEIN 1050
1051 SNSKKTSSNTKGLELEKKILQILQRRNVQLTHKLYSFFKFLLNFMKKVYT 1100
1101 DIFLCIVSVPRINVQFFLESTKKIINQSIYNKKTNEEIIDKTNQSIIHFI 1150
1151 SIINKSSNTKNTNSAANSYEVSALSQAYVFFKISQIQVLNVYKYKFKYVF 1200
1201 DYDGRSFFIKDEIKDYFFGIQGIIHSKLRHKNSPVSLKNQWTNWLKVHYQ 1250
1251 YDLSQNRWSRLVQKNLKNRINKHRLDQNKDLTKCDSYKKTQLIVSKNKKQ 1300
1301 QVDFLVNLLIQKKIKKQSRYDLLLYKFINYAEKKELSIYGYRSPFQANKK 1350
1351 RAISYDYNTQKKEFFDRMDDISIKNYIAEDAIRYIEQNRDRKYFDWVVMD 1400
1401 VKIQNNSISNLQFSFFFKFLRFYDAYRNKPWIIPIKFLFLHFSVNQNFNK 1450
1451 IKNIIEKKRRIDIFKPWKKKKILEVELETPNRAKKEYTSRVDLNKPSLSN 1500
1501 QEKDIEEDYGESDSKKGGKDKNKKKYKNKIEAEVNLLLRKYLNFHLNWKG 1550
1551 SLNKRVINNVKVYCLLIRLKNIKQIAISSIQRGELSLDIMMIQNEKDSTL 1600
1601 TGFRKKKEFIEKGIFIIEPVRLSRKNNEQFFMYETARLLLIHKSKRQINQ 1650
1651 RNPEKSDLDKQIFYKNIPPKRDQRITQNKEKKHYALVVIENILSARRRRE 1700
1701 LRILICFNPRSINSMPRKTIFDNENKINNCCQVFAKNKDLDKEKKILMNL 1750
1751 KLILWPNYRLEDLACINRYWFDTYNGSRFSIVRIHMYPRLKMR 1793
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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