 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P46013 from www.uniprot.org...
The NucPred score for your sequence is 1.00 (see score help below)
1 MWPTRRLVTIKRSGVDGPHFPLSLSTCLFGRGIECDIRIQLPVVSKQHCK 50
51 IEIHEQEAILHNFSSTNPTQVNGSVIDEPVRLKHGDVITIIDRSFRYENE 100
101 SLQNGRKSTEFPRKIREQEPARRVSRSSFSSDPDEKAQDSKAYSKITEGK 150
151 VSGNPQVHIKNVKEDSTADDSKDSVAQGTTNVHSSEHAGRNGRNAADPIS 200
201 GDFKEISSVKLVSRYGELKSVPTTQCLDNSKKNESPFWKLYESVKKELDV 250
251 KSQKENVLQYCRKSGLQTDYATEKESADGLQGETQLLVSRKSRPKSGGSG 300
301 HAVAEPASPEQELDQNKGKGRDVESVQTPSKAVGASFPLYEPAKMKTPVQ 350
351 YSQQQNSPQKHKNKDLYTTGRRESVNLGKSEGFKAGDKTLTPRKLSTRNR 400
401 TPAKVEDAADSATKPENLSSKTRGSIPTDVEVLPTETEIHNEPFLTLWLT 450
451 QVERKIQKDSLSKPEKLGTTAGQMCSGLPGLSSVDINNFGDSINESEGIP 500
501 LKRRRVSFGGHLRPELFDENLPPNTPLKRGEAPTKRKSLVMHTPPVLKKI 550
551 IKEQPQPSGKQESGSEIHVEVKAQSLVISPPAPSPRKTPVASDQRRRSCK 600
601 TAPASSSKSQTEVPKRGGRKSGNLPSKRVSISRSQHDILQMICSKRRSGA 650
651 SEANLIVAKSWADVVKLGAKQTQTKVIKHGPQRSMNKRQRRPATPKKPVG 700
701 EVHSQFSTGHANSPCTIIIGKAHTEKVHVPARPYRVLNNFISNQKMDFKE 750
751 DLSGIAEMFKTPVKEQPQLTSTCHIAISNSENLLGKQFQGTDSGEEPLLP 800
801 TSESFGGNVFFSAQNAAKQPSDKCSASPPLRRQCIRENGNVAKTPRNTYK 850
851 MTSLETKTSDTETEPSKTVSTANRSGRSTEFRNIQKLPVESKSEETNTEI 900
901 VECILKRGQKATLLQQRREGEMKEIERPFETYKENIELKENDEKMKAMKR 950
951 SRTWGQKCAPMSDLTDLKSLPDTELMKDTARGQNLLQTQDHAKAPKSEKG 1000
1001 KITKMPCQSLQPEPINTPTHTKQQLKASLGKVGVKEELLAVGKFTRTSGE 1050
1051 TTHTHREPAGDGKSIRTFKESPKQILDPAARVTGMKKWPRTPKEEAQSLE 1100
1101 DLAGFKELFQTPGPSEESMTDEKTTKIACKSPPPESVDTPTSTKQWPKRS 1150
1151 LRKADVEEEFLALRKLTPSAGKAMLTPKPAGGDEKDIKAFMGTPVQKLDL 1200
1201 AGTLPGSKRQLQTPKEKAQALEDLAGFKELFQTPGHTEELVAAGKTTKIP 1250
1251 CDSPQSDPVDTPTSTKQRPKRSIRKADVEGELLACRNLMPSAGKAMHTPK 1300
1301 PSVGEEKDIIIFVGTPVQKLDLTENLTGSKRRPQTPKEEAQALEDLTGFK 1350
1351 ELFQTPGHTEEAVAAGKTTKMPCESSPPESADTPTSTRRQPKTPLEKRDV 1400
1401 QKELSALKKLTQTSGETTHTDKVPGGEDKSINAFRETAKQKLDPAASVTG 1450
1451 SKRHPKTKEKAQPLEDLAGLKELFQTPVCTDKPTTHEKTTKIACRSQPDP 1500
1501 VDTPTSSKPQSKRSLRKVDVEEEFFALRKRTPSAGKAMHTPKPAVSGEKN 1550
1551 IYAFMGTPVQKLDLTENLTGSKRRLQTPKEKAQALEDLAGFKELFQTRGH 1600
1601 TEESMTNDKTAKVACKSSQPDPDKNPASSKRRLKTSLGKVGVKEELLAVG 1650
1651 KLTQTSGETTHTHTEPTGDGKSMKAFMESPKQILDSAASLTGSKRQLRTP 1700
1701 KGKSEVPEDLAGFIELFQTPSHTKESMTNEKTTKVSYRASQPDLVDTPTS 1750
1751 SKPQPKRSLRKADTEEEFLAFRKQTPSAGKAMHTPKPAVGEEKDINTFLG 1800
1801 TPVQKLDQPGNLPGSNRRLQTRKEKAQALEELTGFRELFQTPCTDNPTTD 1850
1851 EKTTKKILCKSPQSDPADTPTNTKQRPKRSLKKADVEEEFLAFRKLTPSA 1900
1901 GKAMHTPKAAVGEEKDINTFVGTPVEKLDLLGNLPGSKRRPQTPKEKAKA 1950
1951 LEDLAGFKELFQTPGHTEESMTDDKITEVSCKSPQPDPVKTPTSSKQRLK 2000
2001 ISLGKVGVKEEVLPVGKLTQTSGKTTQTHRETAGDGKSIKAFKESAKQML 2050
2051 DPANYGTGMERWPRTPKEEAQSLEDLAGFKELFQTPDHTEESTTDDKTTK 2100
2101 IACKSPPPESMDTPTSTRRRPKTPLGKRDIVEELSALKQLTQTTHTDKVP 2150
2151 GDEDKGINVFRETAKQKLDPAASVTGSKRQPRTPKGKAQPLEDLAGLKEL 2200
2201 FQTPICTDKPTTHEKTTKIACRSPQPDPVGTPTIFKPQSKRSLRKADVEE 2250
2251 ESLALRKRTPSVGKAMDTPKPAGGDEKDMKAFMGTPVQKLDLPGNLPGSK 2300
2301 RWPQTPKEKAQALEDLAGFKELFQTPGTDKPTTDEKTTKIACKSPQPDPV 2350
2351 DTPASTKQRPKRNLRKADVEEEFLALRKRTPSAGKAMDTPKPAVSDEKNI 2400
2401 NTFVETPVQKLDLLGNLPGSKRQPQTPKEKAEALEDLVGFKELFQTPGHT 2450
2451 EESMTDDKITEVSCKSPQPESFKTSRSSKQRLKIPLVKVDMKEEPLAVSK 2500
2501 LTRTSGETTQTHTEPTGDSKSIKAFKESPKQILDPAASVTGSRRQLRTRK 2550
2551 EKARALEDLVDFKELFSAPGHTEESMTIDKNTKIPCKSPPPELTDTATST 2600
2601 KRCPKTRPRKEVKEELSAVERLTQTSGQSTHTHKEPASGDEGIKVLKQRA 2650
2651 KKKPNPVEEEPSRRRPRAPKEKAQPLEDLAGFTELSETSGHTQESLTAGK 2700
2701 ATKIPCESPPLEVVDTTASTKRHLRTRVQKVQVKEEPSAVKFTQTSGETT 2750
2751 DADKEPAGEDKGIKALKESAKQTPAPAASVTGSRRRPRAPRESAQAIEDL 2800
2801 AGFKDPAAGHTEESMTDDKTTKIPCKSSPELEDTATSSKRRPRTRAQKVE 2850
2851 VKEELLAVGKLTQTSGETTHTDKEPVGEGKGTKAFKQPAKRKLDAEDVIG 2900
2901 SRRQPRAPKEKAQPLEDLASFQELSQTPGHTEELANGAADSFTSAPKQTP 2950
2951 DSGKPLKISRRVLRAPKVEPVGDVVSTRDPVKSQSKSNTSLPPLPFKRGG 3000
3001 GKDGSVTGTKRLRCMPAPEEIVEELPASKKQRVAPRARGKSSEPVVIMKR 3050
3051 SLRTSAKRIEPAEELNSNDMKTNKEEHKLQDSVPENKGISLRSRRQNKTE 3100
3101 AEQQITEVFVLAERIEINRNEKKPMKTSPEMDIQNPDDGARKPIPRDKVT 3150
3151 ENKRCLRSARQNESSQPKVAEESGGQKSAKVLMQNQKGKGEAGNSDSMCL 3200
3201 RSRKTKSQPAASTLESKSVQRVTRSVKRCAENPKKAEDNVCVKKIRTRSH 3250
3251 RDSEDI 3256
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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