 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q61789 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MAVALGRAPRSLPLLLTLLLLLLLRMSPSWSVVGQDHPMSSRSLHPPYFN 50
51 LAQAARIWATATCGERDPEVSRPRPELFCKLVGGPAAQGSGHTIQGQFCD 100
101 YCNSEDSRKAHPASHAIDGSERWWQSPPLSSGTQYNQVNLTLDLGQLFHV 150
151 AYILIKFANSPRPDLWILERSVDFGSTYSPWQYFAHSRRDCVEQFGQEAN 200
201 MAITQDDQMLCVTEYSRIVPLENGEIVVSLINGRPGAKKFAFSDTLREFT 250
251 KATNIRLRFLRTNTLLGHLISKAERDPTVTRRYYYSIKDISVGGRCVCNG 300
301 HAEACSADNPEKQFRCECQHHTCGDTCNRCCAGYNQRRWQPAGQEQHNEC 350
351 EACNCHGHAVDCYYDPDVEHQQASLNSKGVYAGGGVCINCQHNTAGVNCE 400
401 KCAKGYFRPHGVPVDALHGCIPCSCDPERADDCDQGSGHCHCKPNFSGDY 450
451 CETCADGYYNFPFCLRIPVFPNYTPSPEDPVAGNIKGCDCNLEGVLPEIC 500
501 DDRGRCLCRPGVEGPQCDSCRSGSYSFPICQACQCSTIGSYPVPCDPGNG 550
551 QCDCLPGITGRQCDRCLSGAYDFPYCQGSGSVCHPAGTLDSSLGYCQCKQ 600
601 HVASPTCSVCKPLYWNLAKENPRGCSECQCHEAGTLSGIGECGQEDGDCS 650
651 CKAHVTGDACDTCEDGFFSLEKSNYFGCQGCQCDIGGALTTMCSGPSGVC 700
701 QCREHVEGKQCQRPENNYYFPDLHHMKYEVEDGTGPNGRNLRFGFDPLVF 750
751 PEFSWRGYAPMTSVQNEVRVRLSVRQSSLSLFRIVLRYISPGTEAISGRI 800
801 TLYSSQGDSDALQSRKITFPPSKEPAFVTVPGNGFAGPFSITPGTWIACI 850
851 QVEGVLLDYLVLLPRDYYEAFTLQVPVTEPCAHTGSPQDNCLLYQHLPLT 900
901 AFSCTLACEARHFLLDGELRPLAMRQPTPTHPAMVDLSGREVELQLRLRV 950
951 PQVGHYVVLLEYATEVEQLFVVDVNLKSSGSALAGQVNIYSCKYSIPCRS 1000
1001 VVIDSLSRTAVHELLADADIQLKAHMAHFLLYHICIIPAEEFSTEYLRPQ 1050
1051 VHCIASYRQHANPSASCVSLAHETPPTASILDATSRGLFSALPHEPSSPA 1100
1101 DGVTLKAPQSQVTLKGLIPHLGRHVFVIHFYQAEHPGFPTEVIVNGGRQW 1150
1151 SGSFLASFCPHLLGCRDQVISDGQVEFDISEAEVAVTVKIPDGKSLTLVR 1200
1201 VLVVPAENYDYQILHKTTVDKSSEFISSCGGDSFYIDPQAASGFCKNSAR 1250
1251 SLVAFYHNGAIPCECDPAGTAGHHCSPEGGQCPCRPNVIGRQCSRCATGY 1300
1301 YGFPYCKPCNCGRRLCEEVTGKCLCPPHTVRPQCEVCEMNSFNFHPVAGC 1350
1351 DVCNCSRKGTIEAAVSECDRDSGQCRCKPRVTGQQCDKCAPGFYQFPECV 1400
1401 PCSCNRDGTEPSVCDPETGACMCKENVEGPQCQLCREGSFYLDPTNPKGC 1450
1451 TKCFCFGVNTDCQSSHKQRAKFVDMMGWRLETADGVDVPVSFNPGSNSMV 1500
1501 ADLQELPPSVHSASWVAPPSYLGDKVSSYGGYLTYHAKSFGLPGDMVLLG 1550
1551 KQPDVQLTGQHMSLIHKEPSDPRPDRLHHGRVQVIEGNFRHEGSSAPVSR 1600
1601 EELMTVLSRLERLHIRGLHFTETQRLTLGEVGLEEASDTGSGPRAHLVEM 1650
1651 CACPPDYTGDSCQGCRPGYYWDNKSLPVGRCVPCNCNGHSNRCQDGSGIC 1700
1701 INCQHNTAGEHCERCQAGHYGNAIHGSCRVCPCPHTNSFATGCAVDGGAV 1750
1751 RCACKPGYTGTQCERCAPGYFGNPQKFGGSCQPCNCNSNGQLGPCDPLTG 1800
1801 DCVNQEPKDGSPAEECDDCDSCVMTLLNDLASMGEELRLVKSKLQGLSVS 1850
1851 TGALEQIRHMETQAKDLRNQLLGFRSATSSHGSKMDDLEKELSHLNREFE 1900
1901 TLQEKAQVNSRKAQTLYNNIDQTIQSAKELDMKIKNIVQNVHILLKQMAR 1950
1951 PGGEGTDLPVGDWSRELAEAQRMMRDLRSRDFKKHLQEAEAEKMEAQLLL 2000
2001 HRIRTWLESHQVENNGLLKNIRDSLNDYEDKLQDLRSILQEAAAQAKQAT 2050
2051 GINHENEGVLGAIQRQMKEMDSLKNDFTKYLATADSSLLQTNNLLQQMDK 2100
2101 SQKEYESLAAALNGARQELSDRVRELSRSGGKAPLVVEAEKHAQSLQELA 2150
2151 KQLEEIKRNTSGDELVRCAVDAATAYENILNAIRAAEDAASKATSASKSA 2200
2201 FQTVIKEDLPKRAKTLSSDSEELLNEAKMTQKRLQQVSPALNSLQQTLKT 2250
2251 VSVQKDLLDANLTVARDDLHGIQRGDIDSVVIGAKSMVREANGITSEVLD 2300
2301 GLNPIQTDLGRIKDSYESARREDFSKALVDANNSVKKLTRKLPDLFIKIE 2350
2351 SINQQLLPLGNISDNVDRIRELIQQARDAANKVAIPMRFNGKSGVEVRLP 2400
2401 NDLEDLKGYTSLSLFLQRPDLRENGGTEDMFVMYLGNKDASKDYIGMAVV 2450
2451 DGQLTCVYNLGDREAEVQIDQVLTESESQEAVMDRVKFQRIYQFAKLNYT 2500
2501 KEATSTKPKAPGVYDMESASSNTLLNLDPENAVFYVGGYPPGFELPRRLR 2550
2551 FPPYKGCIELDDLNENVLSLYNFKTTFNLNTTEVEPCRRRKEESDKNYFE 2600
2601 GTGYARIPTQPNAPFPNFMQTIQTTVDRGLLFFAENQDNFISLNIEDGNL 2650
2651 MVKYKLNSEPPKEKGIRDTINNGRDHMILISIGKSQKRMLINMNKHSIII 2700
2701 EGEIFDFSTYYLGGIPIAIRERFNISTPAFQGCMKNLKKTSGVVRLNDTV 2750
2751 GVTKKCSEDWKLVRTASFSRGGQMSFTNLDVPSLDRFQLSFGFQTFQPSG 2800
2801 TLLNHQTRTSSLLVTLEDGHIALSTRDSSSPIFKSPGTYMDGLLHHVSVI 2850
2851 SDTSGLRLLIDDQVLRRNQRLASFSNAQQSLSMGGGYFEGCISNVFVQRM 2900
2901 SQSPEVLDMASKSTKRDAFLGGCSLNKPPFLMLFKSPKGFNKARSFNVNQ 2950
2951 LLQDAPQAARSIEAWQDGKSCLPPLNTKATHRALQFGDSPTSHLLFKLPQ 3000
3001 ELLKPRLQFSLDIQTTSSRGLVFHTGTRDSFVALYLSEGHVIFALGAGGK 3050
3051 KLRLRSKERYHDGKWHSVVFGLSGRKVHLVVDGLRAQEGSLPGNSTISPR 3100
3101 EQVYLGLSPSRKSKSLPQHSFVGCLRNFQLDSKPLDSPSARSGVSPCLGG 3150
3151 SLEKGIYFSQGGGHVVLANSVSLEPALTLTLSIRPRSLTGVLIHIASQSG 3200
3201 EHLSVYMEAGKVTTSMNSEAGGTVTSITPKRSLCDGQWHSVTVSIKQHTL 3250
3251 HLELDTDNSYTAGQLSFPPNSTRGSLHIGGVPDKLKMLTLPVWNSFFGCL 3300
3301 KNIQVNHIPVPITEATDVQGSVSLNGCPDH 3330
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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