 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O14981 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MAVSRLDRLFILLDTGTTPVTRKAAAQQLGEVVKLHPHELNNLLSKVLIY 50
51 LRSANWDTRIAAGQAVEAIVKNVPEWNPVPRTRQEPTSESSMEDSPTTER 100
101 LNFDRFDICRLLQHGASLLGSAGAEFEVQDEKSGEVDPKERIARQRKLLQ 150
151 KKLGLNMGEAIGMSTEELFNDEDLDYTPTSASFVNKQPTLQAAELIDSEF 200
201 RAGMSNRQKNKAKRMAKLFAKQRSRDAVETNEKSNDSTDGEPEEKRRKIA 250
251 NVVINQSANDSKVLIDNIPDSSSLIEETNEWPLESFCEELCNDLFNPSWE 300
301 VRHGAGTGLREILKAHGKSGGKMGDSTLEEMIQQHQEWLEDLVIRLLCVF 350
351 ALDRFGDFVSDEVVAPVRETCAQTLGVVLKHMNETGVHKTVDVLLKLLTQ 400
401 EQWEVRHGGLLGIKYALAVRQDVINTLLPKVLTRIIEGLQDLDDDVRAVA 450
451 AASLVPVVESLVYLQTQKVPFIINTLWDALLELDDLTASTNSIMTLLSSL 500
501 LTYPQVQQCSIQQSLTVLVPRVWPFLHHTISSVRRAALETLFTLLSTQDQ 550
551 NSSSWLIPILPDMLRHIFQFCVLESSQEILDLIHKVWMELLSKASVQYVV 600
601 AAACPWMGAWLCLMMQPSHLPIDLNMLLEVKARAKEKTGGKVRQGQSQNK 650
651 EVLQEYIAGADTIMEDPATRDFVVMRARMMAAKLLGALCCCICDPGVNVV 700
701 TQEIKPAESLGQLLLFHLNSKSALQRISVALVICEWAALQKECKAVTLAV 750
751 QPRLLDILSEHLYYDEIAVPFTRMQNECKQLISSLADVHIEVGNRVNNNV 800
801 LTIDQASDLVTTVFNEATSSFDLNPQVLQQLDSKRQQVQMTVTETNQEWQ 850
851 VLQLRVHTFAACAVVSLQQLPEKLNPIIKPLMETIKKEENTLVQNYAAQC 900
901 IAKLLQQCTTRTPCPNSKIIKNLCSSLCVDPYLTPCVTCPVPTQSGQENS 950
951 KGSTSEKDGMHHTVTKHRGIITLYRHQKAAFAITSRRGPTPKAVKAQIAD 1000
1001 LPAGSSGNILVELDEAQKPYLVQRRGAEFALTTIVKHFGGEMAVKLPHLW 1050
1051 DAMVGPLRNTIDINNFDGKSLLDKGDSPAQELVNSLQVFETAAASMDSEL 1100
1101 HPLLVQHLPHLYMCLQYPSTAVRHMAARCVGVMSKIATMETMNIFLEKVL 1150
1151 PWLGAIDDSVKQEGAIEALACVMEQLDVGIVPYIVLLVVPVLGRMSDQTD 1200
1201 SVRFMATQCFATLIRLMPLEAGIPDPPNMSAELIQLKAKERHFLEQLLDG 1250
1251 KKLENYKIPVPINAELRKYQQDGVNWLAFLNKYKLHGILCDDMGLGKTLQ 1300
1301 SICILAGDHCHRAQEYARSKLAECMPLPSLVVCPPTLTGHWVDEVGKFCS 1350
1351 REYLNPLHYTGPPTERIRLQHQVKRHNLIVASYDVVRNDIDFFRNIKFNY 1400
1401 CILDEGHVIKNGKTKLSKAVKQLTANYRIILSGTPIQNNVLELWSLFDFL 1450
1451 MPGFLGTERQFAARYGKPILASRDARSSSREQEAGVLAMDALHRQVLPFL 1500
1501 LRRMKEDVLQDLPPKIIQDYYCTLSPLQVQLYEDFAKSRAKCDVDETVSS 1550
1551 ATLSEETEKPKLKATGHVFQALQYLRKLCNHPALVLTPQHPEFKTTAEKL 1600
1601 AVQNSSLHDIQHAPKLSALKQLLLDCGLGNGSTSESGTESVVAQHRILIF 1650
1651 CQLKSMLDIVEHDLLKPHLPSVTYLRLDGSIPPGQRHSIVSRFNNDPSID 1700
1701 VLLLTTHVGGLGLNLTGADTVVFVEHDWNPMRDLQAMDRAHRIGQKRVVN 1750
1751 VYRLITRGTLEEKIMGLQKFKMNIANTVISQENSSLQSMGTDQLLDLFTL 1800
1801 DKDGKAEKADTSTSGKASMKSILENLSDLWDQEQYDSEYSLENFMHSLK 1849
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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