 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9H2Y7 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MPVGRIECPSSPSFPRDISHECRVCGVTEVGLSAYAKHISGQLHKDNVDA 50
51 QEREDDGKGEEEEEDYFDKELIQLIKQRKEQSRQDEPSNSNQEINSDDRR 100
101 PQWRREDRIPYQDRESYSQPAWHHRGPPQRDWKWEKDGFNNTRKNSFPHS 150
151 LRNGGGPRGRSGWHKGVAGGSSTWFHNHSNSGGGWLSNSGAVDWNHNGTG 200
201 RNSSWLSEGTGGFSSWHMNNSNGNWKSSVRSTNNWNYSGPGDKFQPGRNR 250
251 NSNCQMEDMTMLWNKKSNKSNKYSHDRYNWQRQENDKLGTVATYRGPSEG 300
301 FTSDKFPSEGLLDFNFEQLESQTTKQADTATSKVSGKNGSAAREKPRRWT 350
351 PYPSQKTLDLQSGLKDITGNKSEMIEKPLFDFSLITTGIQEPQTDETRNS 400
401 PTQKTQKEIHTGSLNHKASSDSAASFEVVRQCPTAEKPEQEHTPNKMPSL 450
451 KSPLLPCPATKSLSQKQDPKNISKNTKTNFFSPGEHSNPSNKPTVEDNHG 500
501 PYISKLRSSCPHVLKGNKSTFGSQKQSGDNLNDTLRKAKEVLQCHESLQN 550
551 PLLSTSKSTRNYAKASRNVEESEKGSLKIEFQVHALEDESDGETSDTEKH 600
601 GTKIGTLGSATTELLSGSTRTADEKEEDDRILKTSRELSTSPCNPIVRQK 650
651 ESELQMTSAASPHPGLLLDLKTSLEDAQVDDSIKSHVSYETEGFESASLD 700
701 AELQKSDISQPSGPLLPELSKLGFPASLQRDLTRHISLKSKTGVHLPEPN 750
751 LNSARRIRNISGHRKSETEKESGLKPTLRQILNASRRNVNWEQVIQQVTK 800
801 KKQELGKGLPRFGIEMVPLVQNEQEALDLDGEPDLSSLEGFQWEGVSISS 850
851 SPGLARKRSLSESSVIMDRAPSVYSFFSEEGTGKENEPQQMVSPSNSLRA 900
901 GQSQKATMHLKQEVTPRAASLRTGERAENVATQRRHSAQLSSDHIIPLMH 950
951 LAKDLNSQERSIPPSENQNSQESNGEGNCLSSSASSALAISSLADAATDS 1000
1001 SCTSGAEQNDGQSIRKKRRATGDGSSPELPSLERKNKRRKIKGKKERSQV 1050
1051 DQLLNISLREEELSKSLQCMDNNLLQARAALQTAYVEVQRLLMLKQQITM 1100
1101 EMSALRTHRIQILQGLQETYEPSEHPDQVPCSLTRERRNSRSQTSIDAAL 1150
1151 LPTPFFPLFLEPPSSHVSPSPTGASLQITTSPTFQTHGSVPAPDSSVQIK 1200
1201 QEPMSPEQDENVNAVPPSSACNVSKELLEANREISDSCPVYPVITARLSL 1250
1251 PESTESFHEPSQELKFSVEQRNTRNRENSPSSQSAGLSSINKEGEEPTKG 1300
1301 NSGSEACTSSFLRLSFASETPLEKEPHSPADQPEQQAESTLTSAETRGSK 1350
1351 KKKKLRKKKSLRAAHVPENSDTEQDVLTVKPVRKVKAGKLIKGGKVTTST 1400
1401 WEDSRTGREQESVRDEPDSDSSLEVLEIPNPQLEVVAIDSSESGEEKPDS 1450
1451 PSKKDIWNSTEQNPLETSRSGCDEVSSTSEIGTRYKDGIPVSVAETQTVI 1500
1501 SSIKGSKNSSEISSEPGDDDEPTEGSFEGHQAAVNAIQIFGNLLYTCSAD 1550
1551 KTVRVYNLVSRKCIGVFEGHTSKVNCLLVTQTSGKNAALYTGSSDHTIRC 1600
1601 YNVKSRECVEQLQLEDRVLCLHSRWRILYAGLANGTVVTFNIKNNKRLEI 1650
1651 FECHGPRAVSCLATAQEGARKLLVVGSYDCTISVRDARNGLLLRTLEGHS 1700
1701 KTILCMKVVNDLVFSGSSDQSVHAHNIHTGELVRIYKGHNHAVTVVNILG 1750
1751 KVMVTACLDKFVRVYELQSHDRLQVYGGHKDMIMCMTIHKSMIYTGCYDG 1800
1801 SIQAVRLNLMQNYRCWWHGCSLIFGVVDHLKQHLLTDHTNPNFQTLKCRW 1850
1851 KNCDAFFTARKGSKQDAAGHIERHAEDDSKIDS 1883
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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