 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q7Z408 from www.uniprot.org...
The NucPred score for your sequence is 0.41 (see score help below)
1 MRLISIAPGPRWQVQSHHPRSAGQNCTFQLHGPNGTVESPGFPYGYPNYA 50
51 NCTWTITAEEQHRIQLVFQSFALEEDFDVLSVFDGPPQPENLRTRLTGFQ 100
101 LPATIVSAATTLSLRLISDYAVSAQGFHATYEVLPSHTCGNPGRLPNGIQ 150
151 QGSTFNLGDKVRYSCNLGFFLEGHAVLTCHAGSENSATWDFPLPSCRADD 200
201 ACGGTLRGQSGIISSPHFPSEYHNNADCTWTILAELGDTIALVFIDFQLE 250
251 DGYDFLEVTGTEGSSLWFTGASLPAPVISSKNWLRLHFTSDGNHRQRGFS 300
301 AQYQVKKQIELKSRGVKLMPSKDNSQKTSVLTQVGVSQGHNMCPDPGIPE 350
351 RGKRLGSDFRLGSSVQFTCNEGYDLQGSKRITCMKVSDMFAAWSDHRPVC 400
401 RARMCDAHLRGPSGIITSPNFPIQYDNNAHCVWIITALNPSKVIKLAFEE 450
451 FDLERGYDTLTVGDGGQDGDQKTVLYILTGTSVPDLIVSTNHQMWLLFQT 500
501 DGSGSSLGFKASYEEIEQGSCGDPGIPAYGRREGSRFHHGDTLKFECQPA 550
551 FELVGQKAITCQKNNQWSAKKPGCVFSCFFNFTSPSGVVLSPNYPEDYGN 600
601 HLHCVWLILARPESRIHLAFNDIDVEPQFDFLVIKDGATAEAPVLGTFSG 650
651 NQLPSSITSSGHVARLEFQTDHSTGKRGFNITFTTFRHNECPDPGVPVNG 700
701 KRFGDSLQLGSSISFLCDEGFLGTQGSETITCVLKEGSVVWNSAVLRCEA 750
751 PCGGHLTSPSGTILSPGWPGFYKDALSCAWVIEAQPGYPIKITFDRFKTE 800
801 VNYDTLEVRDGRTYSAPLIGVYHGTQVPQFLISTSNYLYLLFSTDKSHSD 850
851 IGFQLRYETITLQSDHCLDPGIPVNGQRHGNDFYVGALVTFSCDSGYTLS 900
901 DGEPLECEPNFQWSRALPSCEALCGGFIQGSSGTILSPGFPDFYPNNLNC 950
951 TWIIETSHGKGVFFTFHTFHLESGHDYLLITENGSFTQPLRQLTGSRLPA 1000
1001 PISAGLYGNFTAQVRFISDFSMSYEGFNITFSEYDLEPCEEPEVPAYSIR 1050
1051 KGLQFGVGDTLTFSCFPGYRLEGTARITCLGGRRRLWSSPLPRCVAECGN 1100
1101 SVTGTQGTLLSPNFPVNYNNNHECIYSIQTQPGKGIQLKARAFELSEGDV 1150
1151 LKVYDGNNNSARLLGVFSHSEMMGVTLNSTSSSLWLDFITDAENTSKGFE 1200
1201 LHFSSFELIKCEDPGTPKFGYKVHDEGHFAGSSVSFSCDPGYSLRGSEEL 1250
1251 LCLSGERRTWDRPLPTCVAECGGTVRGEVSGQVLSPGYPAPYEHNLNCIW 1300
1301 TIEAEAGCTIGLHFLVFDTEEVHDVLRIWDGPVESGVLLKELSGPALPKD 1350
1351 LHSTFNSVVLQFSTDFFTSKQGFAIQFSVSTATSCNDPGIPQNGSRSGDS 1400
1401 WEAGDSTVFQCDPGYALQGSAEISCVKIENRFFWQPSPPTCIAPCGGDLT 1450
1451 GPSGVILSPNYPEPYPPGKECDWKVTVSPDYVIALVFNIFNLEPGYDFLH 1500
1501 IYDGRDSLSPLIGSFYGSQLPGRIESSSNSLFLAFRSDASVSNAGFVIDY 1550
1551 TENPRESCFDPGSIKNGTRVGSDLKLGSSVTYYCHGGYEVEGTSTLSCIL 1600
1601 GPDGKPVWNNPRPVCTAPCGGQYVGSDGVVLSPNYPQNYTSGQICLYFVT 1650
1651 VPKDYVVFGQFAFFHTALNDVVEVHDGHSQHSRLLSSLSGSHTGESLPLA 1700
1701 TSNQVLIKFSAKGLAPARGFHFVYQAVPRTSATQCSSVPEPRYGKRLGSD 1750
1751 FSVGAIVRFECNSGYALQGSPEIECLPVPGALAQWNVSAPTCVVPCGGNL 1800
1801 TERRGTILSPGFPEPYLNSLNCVWKIVVPEGAGIQIQVVSFVTEQNWDSL 1850
1851 EVFDGADNTVTMLGSFSGTTVPALLNSTSNQLYLHFYSDISVSAAGFHLE 1900
1901 YKTVGLSSCPEPAVPSNGVKTGERYLVNDVVSFQCEPGYALQGHAHISCM 1950
1951 PGTVRRWNYPPPLCIAQCGGTVEEMEGVILSPGFPGNYPSNMDCSWKIAL 2000
2001 PVGFGAHIQFLNFSTEPNHDYIEIRNGPYETSRMMGRFSGSELPSSLLST 2050
2051 SHETTVYFHSDHSQNRPGFKLEYQDLTYSHQISSFLRGFDLSELERTNST 2100
2101 PPVAASYVWDLDPGCEAYELQECPDPEPFANGIVRGAGYNVGQSVTFECL 2150
2151 PGYQLTGHPVLTCQHGTNRNWDHPLPKCEVPCGGNITSSNGTVYSPGFPS 2200
2201 PYSSSQDCVWLITVPIGHGVRLNLSLLQTEPSGDFITIWDGPQQTAPRLG 2250
2251 VFTRSMAKKTVQSSSNQVLLKFHRDAATGGIFAIAFSAYPLTKCPPPTIL 2300
2301 PNAEVVTENEEFNIGDIVRYRCLPGFTLVGNEILTCKLGTYLQFEGPPPI 2350
2351 CEVHCPTNELLTDSTGVILSQSYPGSYPQFQTCSWLVRVEPDYNISLTVE 2400
2401 YFLSEKQYDEFEIFDGPSGQSPLLKALSGNYSAPLIVTSSSNSVYLRWSS 2450
2451 DHAYNRKGFKIRYSAPYCSLPRAPLHGFILGQTSTQPGGSIHFGCNAGYR 2500
2501 LVGHSMAICTRHPQGYHLWSEAIPLCQALSCGLPEAPKNGMVFGKEYTVG 2550
2551 TKAMYSCSEGYHLQAGAEATAECLDTGLWSNRNVPPQCVPVTCPDVSSIS 2600
2601 VEHGRWRLIFETQYQFQAQLMLICDPGYYYTGQRVIRCQANGKWSLGDST 2650
2651 PTCRIISCGELPIPPNGHRIGTLSVYGATAIFSCNSGYTLVGSRVRECMA 2700
2701 NGLWSGSEVRCLAGHCGTPEPIVNGHINGENYSYRGSVVYQCNAGFRLIG 2750
2751 MSVRICQQDHHWSGKTPFCVLVSCGHPGSPPHSQMSGDSYTVGAVVRYSC 2800
2801 IGKRTLVGNSTRMCGLDGHWTGSLPHCSGTSVGVCGDPGIPAHGIRLGDS 2850
2851 FDPGTVMRFSCEAGHVLRGSSERTCQANGSWSGSQPECGVISCGNPGTPS 2900
2901 NARVVFSDGLVFSSSIVYECREGYYATGLLSRHCSVNGTWTGSDPECLVI 2950
2951 NCGDPGIPANGLRLGNDFRYNKTVTYQCVPGYMMESHRVSVLSCTKDRTW 3000
3001 NGTKPVCKALMCKPPPLIPNGKVVGSDFMWGSSVTYACLEGYQLSLPAVF 3050
3051 TCEGNGSWTGELPQCFPVFCGDPGVPSRGRREDRGFSYRSSVSFSCHPPL 3100
3101 VLVGSPRRFCQSDGTWSGTQPSCIDPTLTTCADPGVPQFGIQNNSQGYQV 3150
3151 GSTVLFRCQKGYLLQGSTTRTCLPNLTWSGTPPDCVPHHCRQPETPTHAN 3200
3201 VGALDLPSMGYTLIYSCQEGFSLKGGSEHRTCKADGSWTGKPPICLEVRP 3250
3251 SGRPINTAREPPLTQALIPGDVFAKNSLWKGAYEYQGKKQPAMLRVTGFQ 3300
3301 VANSKVNATMIDHSGVELHLAGTYKKEDFHLLLQVYQITGPVEIFMNKFK 3350
3351 DDHWALDGHVSSESSGATFIYQGSVKGQGFGQFGFQRLDLRLLESDPESI 3400
3401 GRHFASNSSSVAAAILVPFIALIIAGFVLYLYKHRRRPKVPFNGYAGHEN 3450
3451 TNVRATFENPMYDRNIQPTDIMASEAEFTVSTVCTAV 3487
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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