 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O88658 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MSGASVKVAVRVRPFNSRETSKESKCIIQMQGNSTSIINPKNPKEAPKSF 50
51 SFDYSYWSHTSPEDPCFASQSRVYNDIGKEMLLHAFEGYNVCIFAYGQTG 100
101 AGKSYTMMGKQEESQAGIIPQLCEELFEKINDNCNEDMSYSVEVSYMEIY 150
151 CERVRDLLNPKNKGNLRVREHPLLGPYVEDLSKLAVTSYTDIADLMDAGN 200
201 KARTVAATNMNETSSRSHAVFTIVFTQKKQDPETNLSTEKVSKISLVDLA 250
251 GSERADSTGAKGTRLKEGANINKSLTTLGKVISALAEVDNCTSKSKKKKK 300
301 TDFIPYRDSVLTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADR 350
351 AKQIKCNAVINEDPNAKLVRELKEEVTRLKDLLRAQGLGDIIDIDPLMDD 400
401 YSGSGGKYLKDFQNNKHRYLLASENQRPGNFSTASMGSLTSSPSSCSLNS 450
451 QAGLTSVTSIQERIMSTPGGEEAIERLKESEKIIAELNETWEEKLRKTEA 500
501 IRMEREALLAEMGVAIREDGGTLGVFSPKKTPHLVNLNEDPLMSECLLYY 550
551 IKDGITRVGQADAERRQDIVLSGAHIKEEHCIFRSERNNTGEVIVTLEPC 600
601 ERSETYVNGKRVAHPVQLRSGNRIIMGKNHVFRFNHPEQARAEREKTPSA 650
651 ETPSEPVDWTFAQRELLEKQGIDMKQEMEKRLQEMEILYKREKEEADLLL 700
701 EQQRLDYESKLQALQKQVETRSLAAETTEEEEEEEEVPWTQHEFELAQWA 750
751 FRKWKSHQFTSLRDLLWGNAVYLKEANAISVELKKKVQFQFVLLTDTLYS 800
801 PVPPELLPTEMGKTHEDRPFPRTVVAVEVQDLKNGATHYWSLDKLKQRLD 850
851 LMREMYDRAGEVGSNAQDDSETTMTGSDPFYDRFHWFKLVGSSPIFHGCV 900
901 NERLADRTPSPTFSTADSDITELADEQQDAMEDFDDEAFVDDTGSDAGTE 950
951 EGSELFSDGHDPFYDRSPWFILVGRAFVYLSNLLYPVPLIHRVAIVSEKG 1000
1001 EVRGFLRVAVQAIAADEEAPDYGSGIRQSGTAKISFDNEYFNQSDFPSAA 1050
1051 MTRSGLSLEELRIVEGQGQSSEVISPPEEVNRMNDLDLKSGTLLDGKMVM 1100
1101 EGFSEEIGNHLKLGSAFTFRVTVLQASGILPEYADIFCQFNFLHRHDEAF 1150
1151 STEPLKNNGRGSPLGFYHVQNIAVEVTESFVDYIKTKPIVFEVFGHYQQH 1200
1201 PLHLQGQELNSPPQPSRRFFPPPMPLSRPVPATKLNTMNKTSLGQSMSKY 1250
1251 DLLVWFEISELEPTGEYIPAVVDHTAGLPCQGTFLLHQGIQRRITVTIIH 1300
1301 EKGSELHWKDVRELVVGRIRNKPEVDEAAVDAILSLNIISAKSLKSSHSS 1350
1351 SRTFYRFEAVWDSSLHNSLLLNRVTPYGEKIYMTLSAYLELDHCIQPAVI 1400
1401 TKDVCMVFYSRDAKISPPRSLRNLFGSGYSKSPDSNRVTGIYELSLCKMA 1450
1451 DTGSPGMQRRRRKVLDTSVAYVRGEENLAGWRPRGDSLILEHQWELEKLE 1500
1501 LLHEVEKTRHFLLLRERLGDSIPKSMSDSLSPSLSSGTLSTSTSISSQIS 1550
1551 TTTFESAITPSESSGYDSADIESLVDREKELATKCLQLLTHTFNREFSQV 1600
1601 HGSISDCKLSDISPIGRDPSVSSFSSSTLTPSSTCPSLVDSRSSSMDQKT 1650
1651 PEANSRASSPCQEFEQFQIIPTVETPYLARAGKNEFLNLVPDIEEVRAGS 1700
1701 VVSKKGYLHFKEPLSSNWAKHFVVVRRPYVFIYNSDKDPVERGIINLSTA 1750
1751 QVEYSEDQQAMLKTPNTFAVCTKHRGVLLQALNDKDMNDWLYAFNPLLAG 1800
1801 TIRSKLSRRCPSQPKY 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.) |
Go back to the NucPred Home Page.