 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O60333 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MSGASVKVAVRVRPFNSRETSKESKCIIQMQGNSTSIINPKNPKEAPKSF 50
51 SFDYSYWSHTSPEDPCFASQNRVYNDIGKEMLLHAFEGYNVCIFAYGQTG 100
101 AGKSYTMMGKQEESQAGIIPQLCEELFEKINDNCNEEMSYSVEVSYMEIY 150
151 CERVRDLLNPKNKGNLRVREHPLLGPYVEDLSKLAVTSYTDIADLMDAGN 200
201 KARTVAATNMNETSSRSHAVFTIVFTQKKHDNETNLSTEKVSKISLVDLA 250
251 GSERADSTGAKGTRLKEGANINKSLTTLGKVISALAEVDNCTSKSKKKKK 300
301 TDFIPYRDSVLTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADR 350
351 AKQIKCNAVINEDPNAKLVRELKEEVTRLKDLLRAQGLGDIIDIDPLIDD 400
401 YSGSGSKYLKDFQNNKHRYLLASENQRPGHFSTASMGSLTSSPSSCSLSS 450
451 QVGLTSVTSIQERIMSTPGGEEAIERLKESEKIIAELNETWEEKLRKTEA 500
501 IRMEREALLAEMGVAIREDGGTLGVFSPKKTPHLVNLNEDPLMSECLLYY 550
551 IKDGITRVGQADAERRQDIVLSGAHIKEEHCIFRSERSNSGEVIVTLEPC 600
601 ERSETYVNGKRVSQPVQLRSGNRIIMGKNHVFRFNHPEQARAEREKTPSA 650
651 ETPSEPVDWTFAQRELLEKQGIDMKQEMEKRLQEMEILYKKEKEEADLLL 700
701 EQQRLDYESKLQALQKQVETRSLAAETTEEEEEEEEVPWTQHEFELAQWA 750
751 FRKWKSHQFTSLRDLLWGNAVYLKEANAISVELKKKVQFQFVLLTDTLYS 800
801 PLPPELLPTEMEKTHEDRPFPRTVVAVEVQDLKNGATHYWSLEKLKQRLD 850
851 LMREMYDRAGEMASSAQDESETTVTGSDPFYDRFHWFKLVGSSPIFHGCV 900
901 NERLADRTPSPTFSTADSDITELADEQQDEMEDFDDEAFVDDAGSDAGTE 950
951 EGSDLFSDGHDPFYDRSPWFILVGRAFVYLSNLLYPVPLIHRVAIVSEKG 1000
1001 EVRGFLRVAVQAIAADEEAPDYGSGIRQSGTAKISFDNEYFNQSDFSSVA 1050
1051 MTRSGLSLEELRIVEGQGQSSEVITPPEEISRINDLDLKSSTLLDGKMVM 1100
1101 EGFSEEIGNHLKLGSAFTFRVTVLQASGILPEYADIFCQFNFLHRHDEAF 1150
1151 STEPLKNNGRGSPLAFYHVQNIAVEITESFVDYIKTKPIVFEVFGHYQQH 1200
1201 PLHLQGQELNSPPQPCRRFFPPPMPLSKPVPATKLNTMSKTSLGQSMSKY 1250
1251 DLLVWFEISELEPTGEYIPAVVDHTAGLPCQGTFLLHQGIQRRITVTIIH 1300
1301 EKGSELHWKDVRELVVGRIRNKPEVDEAAVDAILSLNIISAKYLKSSHNS 1350
1351 SRTFYRFEAVWDSSLHNSLLLNRVTPYGEKIYMTLSAYLELDHCIQPAVI 1400
1401 TKDVCMVFYSRDAKISPPRSLRSLFGSGYSKSPDSNRVTGIYELSLCKMS 1450
1451 DTGSPGMQRRRRKILDTSVAYVRGEENLAGWRPRGDSLILEHQWELEKLE 1500
1501 LLHEVEKTRHFLLLRERLGDSIPKSLSDSLSPSLSSGTLSTSTSISSQIS 1550
1551 TTTFESAITPSESSGYDSGDIESLVDREKELATKCLQLLTHTFNREFSQV 1600
1601 HGSVSDCKLSDISPIGRDPSESSFSSATLTPSSTCPSLVDSRSNSLDQKT 1650
1651 PEANSRASSPCPEFEQFQIVPAVETPYLARAGKNEFLNLVPDIEEIRPSS 1700
1701 VVSKKGYLHFKEPLYSNWAKHFVVVRRPYVFIYNSDKDPVERGIINLSTA 1750
1751 QVEYSEDQQAMVKTPNTFAVCTKHRGVLLQALNDKDMNDWLYAFNPLLAG 1800
1801 TIRSKLSRRCPSQSKY 1816
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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