 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q923J1 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MSQKSWIESTLTKRECVYIIPSSKDPHRCLPGCQICQQLVRCFCGRLVKQ 50
51 HACFTASLAMKYSDVKLGEHFNQAIEEWSVEKHTEQSPTDAYGVINFQGG 100
101 SHSYRAKYVRLSYDTKPEIILQLLLKEWQMELPKLVISVHGGMQKFELHP 150
151 RIKQLLGKGLIKAAVTTGAWILTGGVNTGVAKHVGDALKEHASRSSRKIC 200
201 TIGIAPWGVIENRNDLVGRDVVAPYQTLLNPLSKLNVLNNLHSHFILVDD 250
251 GTVGKYGAEVRLRRELEKTINQQRIHARIGQGVPVVALIFEGGPNVILTV 300
301 LEYLQESPPVPVVVCEGTGRAADLLAYIHKQTEEGGNLPDAAEPDIISTI 350
351 KKTFNFGQSEAVHLFQTMMECMKKKELITVFHIGSEDHQDIDVAILTALL 400
401 KGTNASAFDQLILTLAWDRVDIAKNHVFVYGQQWLVGSLEQAMLDALVMD 450
451 RVSFVKLLIENGVSMHKFLTIPRLEELYNTKQGPTNPMLFHLIRDVKQGN 500
501 LPPGYKITLIDIGLVIEYLMGGTYRCTYTRKRFRLIYNSLGGNNRRSGRN 550
551 TSSSTPQLRKSHETFGNRADKKEKMRHNHFIKTAQPYRPKMDASMEEGKK 600
601 KRTKDEIVDIDDPETKRFPYPLNELLIWACLMKRQVMARFLWQHGEESMA 650
651 KALVACKIYRSMAYEAKQSDLVDDTSEELKQYSNDFGQLAVELLEQSFRQ 700
701 DETMAMKLLTYELKNWSNSTCLKLAVSSRLRPFVAHTCTQMLLSDMWMGR 750
751 LNMRKNSWYKVILSILVPPAILMLEYKTKAEMSHIPQSQDAHQMTMEDSE 800
801 NNFHNITEEIPMEVFKEVKILDSSDGKNEMEIHIKSKKLPITRKFYAFYH 850
851 APIVKFWFNTLAYLGFLMLYTFVVLVKMEQLPSVQEWIVIAYIFTYAIEK 900
901 VREVFMSEAGKISQKIKVWFSDYFNVSDTIAIISFFVGFGLRFGAKWNYI 950
951 NAYDNHVFVAGRLIYCLNIIFWYVRLLDFLAVNQQAGPYVMMIGKMVANM 1000
1001 FYIVVIMALVLLSFGVPRKAILYPHEEPSWSLAKDIVFHPYWMIFGEVYA 1050
1051 YEIDVCANDSTLPTICGPGTWLTPFLQAVYLFVQYIIMVNLLIAFFNNVY 1100
1101 LQVKAISNIVWKYQRYHFIMAYHEKPVLPPPLIILSHIVSLFCCVCKRRK 1150
1151 KDKTSDGPKLFLTEEDQKKLHDFEEQCVEMYFDEKDDKFNSGSEERIRVT 1200
1201 FERVEQMSIQIKEVGDRVNYIKRSLQSLDSQIGHLQDLSALTVDTLKTLT 1250
1251 AQKASEASKVHNEITRELSISKHLAQNLIDDVPVRPLWKKPSAVNTLSSS 1300
1301 LPQGDRESNNPFLCNIFMKDEKDPQYNLFGQDLPVIPQRKEFNIPEAGSS 1350
1351 CGALFPSAVSPPELRQRRHGVEMLKIFNKNQKLGSSPNSSPHMSSPPTKF 1400
1401 SVSTPSQPSCKSHLESTTKDQEPIFYKAAEGDNIEFGAFVGHRDSMDLQR 1450
1451 FKETSNKIRELLSNDTPENTLKHVGAAGYSECCKTSTSLHSVQAESCSRR 1500
1501 ASTEDSPEVDSKAALLPDWLRDRPSNREMPSEGGTLNGLASPFKPVLDTN 1550
1551 YYYSAVERNNLMRLSQSIPFVPVPPRGEPVTVYRLEESSPSILNNSMSSW 1600
1601 SQLGLCAKIEFLSKEEMGGGLRRAVKVLCTWSEHDILKSGHLYIIKSFLP 1650
1651 EVINTWSSIYKEDTVLHLCLREIQQQRAAQKLTFAFNQMKPKSIPYSPRF 1700
1701 LEVFLLYCHSAGQWFAVEECMTGEFRKYNNNNGDEIIPTNTLEEIMLAFS 1750
1751 HWTYEYTRGELLVLDLQGVGENLTDPSVIKAEEKRSCDMVFGPANLGEDA 1800
1801 IKNFRAKHHCNSCCRKLKLPDLKRNDYTPDKIIFPQDESSDLNLQSGNST 1850
1851 KESEATNSVRLML 1863
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.