 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9V677 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 METGPNAEIELLERVLLRLGNADSDEKLEAAVGKFLTPVILKMNSPHNVV 50
51 RTKVVEVLTHIKRRLSSRGHVQIPVEALLDQYAAQDSSTFLKNFAIIFIT 100
101 MGFPRLALEQQTALASKVVGCEDKLESYQDKLFALLLPVLSDMKIPDDPT 150
151 QRSKLLDLQDKPAICANFLALLQDVLLLPYGITQEQDVPPGLSPYSFTRI 200
201 IAYNWRAEELEKVKKGIVRFLCASVFSDLQIFVPLVVASADTRFSVATPA 250
251 ITELSKLCTMLDFSNPAVTAPLYTLFAGNQSKITDRQTRPCCARVRQKLL 300
301 QYLIKCRGKSINVTKGLQVTFDGFFGTNTNQKCKVLALQFMELLLRDGPR 350
351 ELVSKVSKVILTGITKIIGRDSNEPNDVQNAAYSALAQHARSFPQDVSQD 400
401 LKLVLGYFNNLASCAPELHSSIREALVSMAPAFAWKTKEKSDAMEVDKNV 450
451 DSSSVKLDGQQHLLLAMLLDNAESKVQIVQNVTSVFLTSCYPEYYAPARY 500
501 LLLLIAGERNSLRENVTTYLYGTSKKDHINYSMLSSIDHSKNRISSESIS 550
551 DFNHLSLEQRRVMLPSFQVMMSHVHEMAEKRLKKNTACVVVGRTKLPYSL 600
601 EVYEELLDYLRLCLWYSAGVVAAPGDEKYTHELREYITLHYEDTEDNALH 650
651 QYAQFVQRSVEAKRSESNLICLYDLLNAAPELFAAKQLHLLEPLGNTLKD 700
701 VSETMRINVAQVYGILWAFGLSDEKFDEEVGECLNSLTQKTLEHKHGWLL 750
751 VVGHTFNRKIAMLKQENKTKDFAAWPQFVNAVKIISKCLCESQWLLVSAA 800
801 VKCISMIGKAVEIPNVPVEIQVPSKDGDDDDEEMTEYTSIDSSTKLVIFG 850
851 VVFQLLRSSSARQKIREDSARCLGYLAIGDGEHFTKRNLDKFLTLTKIQK 900
901 DAALNIAISEAIVNTLCGYDVNKGQPDEKFVNKHCNDDDFDQFLNSLIRL 950
951 VTDPNPHSRQAISVWLLAVVKHCSQRPAVLAKKELLQFAFTELLSDDSEF 1000
1001 VQDVASRGLGLVYSISDSGSQSDLANSLLDQLIGGKRKVNQVTADTELFA 1050
1051 EGMLGKTPTGGNITTYKELCSLASDLNQPDMIYQFMQLANHNATWTSKLG 1100
1101 AAFGLKTLSAESRQKMEPYLGKIIPRLYRYKYDPTPKIQNSMISIWDTIV 1150
1151 TDSKEVTERYYWEILRELLDNLTCKEWRVRIACCLAVRDLLNRPNGLKLR 1200
1201 SEEPVRRALPDNSMEVDEVPEPELKELWFQLFRVMDDIHEGTRVAAHGTA 1250
1251 SFLGKLCVIASSSDHGKSGTAVASSILPFLLETGVGHKVPEIRRVSIKTI 1300
1301 SDMIDSSGSLIAPHLATLIPCLLRATGELENTKLSYVSTRLGADNEAQEA 1350
1351 VDTLRAEAAKSLHTMETIGKCVRYIDYSVLEKMTPEVLELMKGSVNLGTK 1400
1401 IGCAHFVCLISIRLGKEMTPLVGKYIRACFVGIKNRNATVRKYNASAIGH 1450
1451 LLGLAKEQSIKSLFTKLEELYAEQPGNRSIALTIQSINKRHHELLKDYMD 1500
1501 SMLPLIFFAMHEEPNEETKANVELWKDLWHDVSPGDAGIRLNLNVIIPKL 1550
1551 ESSLTDASWSRKAQAANAIQTIATRLSSSLDEPDRLRLIKLLLSGLQGRT 1600
1601 FEGKERLLQALAALTKGLDRNHQICSSIIDAAMREARKREPVYRTMALAS 1650
1651 LGEILDQLEADRFEEVYNMSWNLLEKKELRKESDDEDEPNTSQELSADER 1700
1701 NKRAQTLNRLKEVVWETLGKSWPRHSIETQHRYQLFFAENCTSILAESTR 1750
1751 PVQVSLLAALTKYIERLHVFDESAQVPDLTQINQEKKIKTEETAPKTREA 1800
1801 IVEKICTDVLAAVALAAGVPHSGLKKEALNIVLMLIKRLSGSGNGNQQPL 1850
1851 RLIKQSFESNLEKFQRDSAPEIRCRIKDIEDKLSKLNPQN 1890
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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