 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O75445 from www.uniprot.org...
The NucPred score for your sequence is 0.73 (see score help below)
1 MNCPVLSLGSGFLFQVIEMLIFAYFASISLTESRGLFPRLENVGAFKKVS 50
51 IVPTQAVCGLPDRSTFCHSSAAAESIQFCTQRFCIQDCPYRSSHPTYTAL 100
101 FSAGLSSCITPDKNDLHPNAHSNSASFIFGNHKSCFSSPPSPKLMASFTL 150
151 AVWLKPEQQGVMCVIEKTVDGQIVFKLTISEKETMFYYRTVNGLQPPIKV 200
201 MTLGRILVKKWIHLSVQVHQTKISFFINGVEKDHTPFNARTLSGSITDFA 250
251 SGTVQIGQSLNGLEQFVGRMQDFRLYQVALTNREILEVFSGDLLRLHAQS 300
301 HCRCPGSHPRVHPLAQRYCIPNDAGDTADNRVSRLNPEAHPLSFVNDNDV 350
351 GTSWVSNVFTNITQLNQGVTISVDLENGQYQVFYIIIQFFSPQPTEIRIQ 400
401 RKKENSLDWEDWQYFARNCGAFGMKNNGDLEKPDSVNCLQLSNFTPYSRG 450
451 NVTFSILTPGPNYRPGYNNFYNTPSLQEFVKATQIRFHFHGQYYTTETAV 500
501 NLRHRYYAVDEITISGRCQCHGHADNCDTTSQPYRCLCSQESFTEGLHCD 550
551 RCLPLYNDKPFRQGDQVYAFNCKPCQCNSHSKSCHYNISVDPFPFEHFRG 600
601 GGGVCDDCEHNTTGRNCELCKDYFFRQVGADPSAIDVCKPCDCDTVGTRN 650
651 GSILCDQIGGQCNCKRHVSGRQCNQCQNGFYNLQELDPDGCSPCNCNTSG 700
701 TVDGDITCHQNSGQCKCKANVIGLRCDHCNFGFKFLRSFNDVGCEPCQCN 750
751 LHGSVNKFCNPHSGQCECKKEAKGLQCDTCRENFYGLDVTNCKACDCDTA 800
801 GSLPGTVCNAKTGQCICKPNVEGRQCNKCLEGNFYLRQNNSFLCLPCNCD 850
851 KTGTINGSLLCNKSTGQCPCKLGVTGLRCNQCEPHRYNLTIDNFQHCQMC 900
901 ECDSLGTLPGTICDPISGQCLCVPNRQGRRCNQCQPGFYISPGNATGCLP 950
951 CSCHTTGAVNHICNSLTGQCVCQDASIAGQRCDQCKDHYFGFDPQTGRCQ 1000
1001 PCNCHLSGALNETCHLVTGQCFCKQFVTGSKCDACVPSASHLDVNNLLGC 1050
1051 SKTPFQQPPPRGQVQSSSAINLSWSPPDSPNAHWLTYSLLRDGFEIYTTE 1100
1101 DQYPYSIQYFLDTDLLPYTKYSYYIETTNVHGSTRSVAVTYKTKPGVPEG 1150
1151 NLTLSYIIPIGSDSVTLTWTTLSNQSGPIEKYILSCAPLAGGQPCVSYEG 1200
1201 HETSATIWNLVPFAKYDFSVQACTSGGCLHSLPITVTTAQAPPQRLSPPK 1250
1251 MQKISSTELHVEWSPPAELNGIIIRYELYMRRLRSTKETTSEESRVFQSS 1300
1301 GWLSPHSFVESANENALKPPQTMTTITGLEPYTKYEFRVLAVNMAGSVSS 1350
1351 AWVSERTGESAPVFMIPPSVFPLSSYSLNISWEKPADNVTRGKVVGYDIN 1400
1401 MLSEQSPQQSIPMAFSQLLHTAKSQELSYTVEGLKPYRIYEFTITLCNSV 1450
1451 GCVTSASGAGQTLAAAPAQLRPPLVKGINSTTIHLRWFPPEELNGPSPIY 1500
1501 QLERRESSLPALMTTMMKGIRFIGNGYCKFPSSTHPVNTDFTGIKASFRT 1550
1551 KVPEGLIVFAASPGNQEEYFALQLKKGRLYFLFDPQGSPVEVTTTNDHGK 1600
1601 QYSDGKWHEIIAIRHQAFGQITLDGIYTGSSAILNGSTVIGDNTGVFLGG 1650
1651 LPRSYTILRKDPEIIQKGFVGCLKDVHFMKNYNPSAIWEPLDWQSSEEQI 1700
1701 NVYNSWEGCPASLNEGAQFLGAGFLELHPYMFHGGMNFEISFKFRTDQLN 1750
1751 GLLLFVYNKDGPDFLAMELKSGILTFRLNTSLAFTQVDLLLGLSYCNGKW 1800
1801 NKVIIKKEGSFISASVNGLMKHASESGDQPLVVNSPVYVGGIPQELLNSY 1850
1851 QHLCLEQGFGGCMKDVKFTRGAVVNLASVSSGAVRVNLDGCLSTDSAVNC 1900
1901 RGNDSILVYQGKEQSVYEGGLQPFTEYLYRVIASHEGGSVYSDWSRGRTT 1950
1951 GAAPQSVPTPSRVRSLNGYSIEVTWDEPVVRGVIEKYILKAYSEDSTRPP 2000
2001 RMPSASAEFVNTSNLTGILTGLLPFKNYAVTLTACTLAGCTESSHALNIS 2050
2051 TPQEAPQEVQPPVAKSLPSSLLLSWNPPKKANGIITQYCLYMDGRLIYSG 2100
2101 SEENYIVTDLAVFTPHQFLLSACTHVGCTNSSWVLLYTAQLPPEHVDSPV 2150
2151 LTVLDSRTIHIQWKQPRKISGILERYVLYMSNHTHDFTIWSVIYNSTELF 2200
2201 QDHMLQYVLPGNKYLIKLGACTGGGCTVSEASEALTDEDIPEGVPAPKAH 2250
2251 SYSPDSFNVSWTEPEYPNGVITSYGLYLDGILIHNSSELSYRAYGFAPWS 2300
2301 LHSFRVQACTAKGCALGPLVENRTLEAPPEGTVNVFVKTQGSRKAHVRWE 2350
2351 APFRPNGLLTHSVLFTGIFYVDPVGNNYTLLNVTKVMYSGEETNLWVLID 2400
2401 GLVPFTNYTVQVNISNSQGSLITDPITIAMPPGAPDGVLPPRLSSATPTS 2450
2451 LQVVWSTPARNNAPGSPRYQLQMRSGDSTHGFLELFSNPSASLSYEVSDL 2500
2501 QPYTEYMFRLVASNGFGSAHSSWIPFMTAEDKPGPVVPPILLDVKSRMML 2550
2551 VTWQHPRKSNGVITHYNIYLHGRLYLRTPGNVTNCTVMHLHPYTAYKFQV 2600
2601 EACTSKGCSLSPESQTVWTLPGAPEGIPSPELFSDTPTSVIISWQPPTHP 2650
2651 NGLVENFTIERRVKGKEEVTTLVTLPRSHSMRFIDKTSALSPWTKYEYRV 2700
2701 LMSTLHGGTNSSAWVEVTTRPSRPAGVQPPVVTVLEPDAVQVTWKPPLIQ 2750
2751 NGDILSYEIHMPDPHITLTNVTSAVLSQKVTHLIPFTNYSVTIVACSGGN 2800
2801 GYLGGCTESLPTYVTTHPTVPQNVGPLSVIPLSESYVVISWQPPSKPNGP 2850
2851 NLRYELLRRKIQQPLASNPPEDLNRWHNIYSGTQWLYEDKGLSRFTTYEY 2900
2901 MLFVHNSVGFTPSREVTVTTLAGLPERGANLTASVLNHTAIDVRWAKPTV 2950
2951 QDLQGEVEYYTLFWSSATSNDSLKILPDVNSHVIGHLKPNTEYWIFISVF 3000
3001 NGVHSINSAGLHATTCDGEPQGMLPPEVVIINSTAVRVIWTSPSNPNGVV 3050
3051 TEYSIYVNNKLYKTGMNVPGSFILRDLSPFTIYDIQVEVCTIYACVKSNG 3100
3101 TQITTVEDTPSDIPTPTIRGITSRSLQIDWVSPRKPNGIILGYDLLWKTW 3150
3151 YPCAKTQKLVQDQSDELCKAVRCQKPESICGHICYSSEAKVCCNGVLYNP 3200
3201 KPGHRCCEEKYIPFVLNSTGVCCGGRIQEAQPNHQCCSGYYARILPGEVC 3250
3251 CPDEQHNRVSVGIGDSCCGRMPYSTSGNQICCAGRLHDGHGQKCCGRQIV 3300
3301 SNDLECCGGEEGVVYNRLPGMFCCGQDYVNMSDTICCSASSGESKAHIKK 3350
3351 NDPVPVKCCETELIPKSQKCCNGVGYNPLKYVCSDKISTGMMMKETKECR 3400
3401 ILCPASMEATEHCGRCDFNFTSHICTVIRGSHNSTGKASIEEMCSSAEET 3450
3451 IHTGSVNTYSYTDVNLKPYMTYEYRISAWNSYGRGLSKAVRARTKEDVPQ 3500
3501 GVSPPTWTKIDNLEDTIVLNWRKPIQSNGPIIYYILLRNGIERFRGTSLS 3550
3551 FSDKEGIQPFQEYSYQLKACTVAGCATSSKVVAATTQGVPESILPPSITA 3600
3601 LSAVALHLSWSVPEKSNGVIKEYQIRQVGKGLIHTDTTDRRQHTVTGLQP 3650
3651 YTNYSFTLTACTSAGCTSSEPFLGQTLQAAPEGVWVTPRHIIINSTTVEL 3700
3701 YWSLPEKPNGLVSQYQLSRNGNLLFLGGSEEQNFTDKNLEPNSRYTYKLE 3750
3751 VKTGGGSSASDDYIVQTPMSTPEEIYPPYNITVIGPYSIFVAWIPPGILI 3800
3801 PEIPVEYNVLLNDGSVTPLAFSVGHHQSTLLENLTPFTQYEIRIQACQNG 3850
3851 SCGVSSRMFVKTPEAAPMDLNSPVLKALGSACIEIKWMPPEKPNGIIINY 3900
3901 FIYRRPAGIEEESVLFVWSEGALEFMDEGDTLRPFTLYEYRVRACNSKGS 3950
3951 VESLWSLTQTLEAPPQDFPAPWAQATSAHSVLLNWTKPESPNGIISHYRV 4000
4001 VYQERPDDPTFNSPTVHAFTVKGTSHQAHLYGLEPFTTYRIGVVAANHAG 4050
4051 EILSPWTLIQTLESSPSGLRNFIVEQKENGRALLLQWSEPMRTNGVIKTY 4100
4101 NIFSDGFLEYSGLNRQFLFRRLDPFTLYTLTLEACTRAGCAHSAPQPLWT 4150
4151 DEAPPDSQLAPTVHSVKSTSVELSWSEPVNPNGKIIRYEVIRRCFEGKAW 4200
4201 GNQTIQADEKIVFTEYNTERNTFMYNDTGLQPWTQCEYKIYTWNSAGHTC 4250
4251 SSWNVVRTLQAPPEGLSPPVISYVSMNPQKLLISWIPPEQSNGIIQSYRL 4300
4301 QRNEMLYPFSFDPVTFNYTDEELLPFSTYSYALQACTSGGCSTSKPTSIT 4350
4351 TLEAAPSEVSPPDLWAVSATQMNVCWSPPTVQNGKITKYLVRYDNKESLA 4400
4401 GQGLCLLVSHLQPYSQYNFSLVACTNGGCTASVSKSAWTMEALPENMDSP 4450
4451 TLQVTGSESIEITWKPPRNPNGQIRSYELRRDGTIVYTGLETRYRDFTLT 4500
4501 PGVEYSYTVTASNSQGGILSPLVKDRTSPSAPSGMEPPKLQARGPQEILV 4550
4551 NWDPPVRTNGDIINYTLFIRELFERETKIIHINTTHNSFGMQSYIVNQLK 4600
4601 PFHRYEIRIQACTTLGCASSDWTFIQTPEIAPLMQPPPHLEVQMAPGGFQ 4650
4651 PTVSLLWTGPLQPNGKVLYYELYRRQIATQPRKSNPVLIYNGSSTSFIDS 4700
4701 ELLPFTEYEYQVWAVNSAGKAPSSWTWCRTGPAPPEGLRAPTFHVISSTQ 4750
4751 AVVNISAPGKPNGIVSLYRLFSSSAHGAETVLSEGMATQQTLHGLQAFTN 4800
4801 YSIGVEACTCFNCCSKGPTAELRTHPAPPSGLSSPQIGTLASRTASFRWS 4850
4851 PPMFPNGVIHSYELQFHVACPPDSALPCTPSQIETKYTGLGQKASLGGLQ 4900
4901 PYTTYKLRVVAHNEVGSTASEWISFTTQKELPQYRAPFSVDSNLSVVCVN 4950
4951 WSDTFLLNGQLKEYVLTDGGRRVYSGLDTTLYIPRTADKTFFFQVICTTD 5000
5001 EGSVKTPLIQYDTSTGLGLVLTTPGKKKGSRSKSTEFYSELWFIVLMAML 5050
5051 GLILLAIFLSLILQRKIHKEPYIRERPPLVPLQKRMSPLNVYPPGENHMG 5100
5101 LADTKIPRSGTPVSIRSNRSACVLRIPSQNQTSLTYSQGSLHRSVSQLMD 5150
5151 IQDKKVLMDNSLWEAIMGHNSGLYVDEEDLMNAIKDFSSVTKERTTFTDT 5200
5201 HL 5202
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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