 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P51587 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MPIGSKERPTFFEIFKTRCNKADLGPISLNWFEELSSEAPPYNSEPAEES 50
51 EHKNNNYEPNLFKTPQRKPSYNQLASTPIIFKEQGLTLPLYQSPVKELDK 100
101 FKLDLGRNVPNSRHKSLRTVKTKMDQADDVSCPLLNSCLSESPVVLQCTH 150
151 VTPQRDKSVVCGSLFHTPKFVKGRQTPKHISESLGAEVDPDMSWSSSLAT 200
201 PPTLSSTVLIVRNEEASETVFPHDTTANVKSYFSNHDESLKKNDRFIASV 250
251 TDSENTNQREAASHGFGKTSGNSFKVNSCKDHIGKSMPNVLEDEVYETVV 300
301 DTSEEDSFSLCFSKCRTKNLQKVRTSKTRKKIFHEANADECEKSKNQVKE 350
351 KYSFVSEVEPNDTDPLDSNVANQKPFESGSDKISKEVVPSLACEWSQLTL 400
401 SGLNGAQMEKIPLLHISSCDQNISEKDLLDTENKRKKDFLTSENSLPRIS 450
451 SLPKSEKPLNEETVVNKRDEEQHLESHTDCILAVKQAISGTSPVASSFQG 500
501 IKKSIFRIRESPKETFNASFSGHMTDPNFKKETEASESGLEIHTVCSQKE 550
551 DSLCPNLIDNGSWPATTTQNSVALKNAGLISTLKKKTNKFIYAIHDETSY 600
601 KGKKIPKDQKSELINCSAQFEANAFEAPLTFANADSGLLHSSVKRSCSQN 650
651 DSEEPTLSLTSSFGTILRKCSRNETCSNNTVISQDLDYKEAKCNKEKLQL 700
701 FITPEADSLSCLQEGQCENDPKSKKVSDIKEEVLAAACHPVQHSKVEYSD 750
751 TDFQSQKSLLYDHENASTLILTPTSKDVLSNLVMISRGKESYKMSDKLKG 800
801 NNYESDVELTKNIPMEKNQDVCALNENYKNVELLPPEKYMRVASPSRKVQ 850
851 FNQNTNLRVIQKNQEETTSISKITVNPDSEELFSDNENNFVFQVANERNN 900
901 LALGNTKELHETDLTCVNEPIFKNSTMVLYGDTGDKQATQVSIKKDLVYV 950
951 LAEENKNSVKQHIKMTLGQDLKSDISLNIDKIPEKNNDYMNKWAGLLGPI 1000
1001 SNHSFGGSFRTASNKEIKLSEHNIKKSKMFFKDIEEQYPTSLACVEIVNT 1050
1051 LALDNQKKLSKPQSINTVSAHLQSSVVVSDCKNSHITPQMLFSKQDFNSN 1100
1101 HNLTPSQKAEITELSTILEESGSQFEFTQFRKPSYILQKSTFEVPENQMT 1150
1151 ILKTTSEECRDADLHVIMNAPSIGQVDSSKQFEGTVEIKRKFAGLLKNDC 1200
1201 NKSASGYLTDENEVGFRGFYSAHGTKLNVSTEALQKAVKLFSDIENISEE 1250
1251 TSAEVHPISLSSSKCHDSVVSMFKIENHNDKTVSEKNNKCQLILQNNIEM 1300
1301 TTGTFVEEITENYKRNTENEDNKYTAASRNSHNLEFDGSDSSKNDTVCIH 1350
1351 KDETDLLFTDQHNICLKLSGQFMKEGNTQIKEDLSDLTFLEVAKAQEACH 1400
1401 GNTSNKEQLTATKTEQNIKDFETSDTFFQTASGKNISVAKESFNKIVNFF 1450
1451 DQKPEELHNFSLNSELHSDIRKNKMDILSYEETDIVKHKILKESVPVGTG 1500
1501 NQLVTFQGQPERDEKIKEPTLLGFHTASGKKVKIAKESLDKVKNLFDEKE 1550
1551 QGTSEITSFSHQWAKTLKYREACKDLELACETIEITAAPKCKEMQNSLNN 1600
1601 DKNLVSIETVVPPKLLSDNLCRQTENLKTSKSIFLKVKVHENVEKETAKS 1650
1651 PATCYTNQSPYSVIENSALAFYTSCSRKTSVSQTSLLEAKKWLREGIFDG 1700
1701 QPERINTADYVGNYLYENNSNSTIAENDKNHLSEKQDTYLSNSSMSNSYS 1750
1751 YHSDEVYNDSGYLSKNKLDSGIEPVLKNVEDQKNTSFSKVISNVKDANAY 1800
1801 PQTVNEDICVEELVTSSSPCKNKNAAIKLSISNSNNFEVGPPAFRIASGK 1850
1851 IVCVSHETIKKVKDIFTDSFSKVIKENNENKSKICQTKIMAGCYEALDDS 1900
1901 EDILHNSLDNDECSTHSHKVFADIQSEEILQHNQNMSGLEKVSKISPCDV 1950
1951 SLETSDICKCSIGKLHKSVSSANTCGIFSTASGKSVQVSDASLQNARQVF 2000
2001 SEIEDSTKQVFSKVLFKSNEHSDQLTREENTAIRTPEHLISQKGFSYNVV 2050
2051 NSSAFSGFSTASGKQVSILESSLHKVKGVLEEFDLIRTEHSLHYSPTSRQ 2100
2101 NVSKILPRVDKRNPEHCVNSEMEKTCSKEFKLSNNLNVEGGSSENNHSIK 2150
2151 VSPYLSQFQQDKQQLVLGTKVSLVENIHVLGKEQASPKNVKMEIGKTETF 2200
2201 SDVPVKTNIEVCSTYSKDSENYFETEAVEIAKAFMEDDELTDSKLPSHAT 2250
2251 HSLFTCPENEEMVLSNSRIGKRRGEPLILVGEPSIKRNLLNEFDRIIENQ 2300
2301 EKSLKASKSTPDGTIKDRRLFMHHVSLEPITCVPFRTTKERQEIQNPNFT 2350
2351 APGQEFLSKSHLYEHLTLEKSSSNLAVSGHPFYQVSATRNEKMRHLITTG 2400
2401 RPTKVFVPPFKTKSHFHRVEQCVRNINLEENRQKQNIDGHGSDDSKNKIN 2450
2451 DNEIHQFNKNNSNQAVAVTFTKCEEEPLDLITSLQNARDIQDMRIKKKQR 2500
2501 QRVFPQPGSLYLAKTSTLPRISLKAAVGGQVPSACSHKQLYTYGVSKHCI 2550
2551 KINSKNAESFQFHTEDYFGKESLWTGKGIQLADGGWLIPSNDGKAGKEEF 2600
2601 YRALCDTPGVDPKLISRIWVYNHYRWIIWKLAAMECAFPKEFANRCLSPE 2650
2651 RVLLQLKYRYDTEIDRSRRSAIKKIMERDDTAAKTLVLCVSDIISLSANI 2700
2701 SETSSNKTSSADTQKVAIIELTDGWYAVKAQLDPPLLAVLKNGRLTVGQK 2750
2751 IILHGAELVGSPDACTPLEAPESLMLKISANSTRPARWYTKLGFFPDPRP 2800
2801 FPLPLSSLFSDGGNVGCVDVIIQRAYPIQWMEKTSSGLYIFRNEREEEKE 2850
2851 AAKYVEAQQKRLEALFTKIQEEFEEHEENTTKPYLPSRALTRQQVRALQD 2900
2901 GAELYEAVKNAADPAYLEGYFSEEQLRALNNHRQMLNDKKQAQIQLEIRK 2950
2951 AMESAEQKEQGLSRDVTTVWKLRIVSYSKKEKDSVILSIWRPSSDLYSLL 3000
3001 TEGKRYRIYHLATSKSKSKSERANIQLAATKKTQYQQLPVSDEILFQIYQ 3050
3051 PREPLHFSKFLDPDFQPSCSEVDLIGFVVSVVKKTGLAPFVYLSDECYNL 3100
3101 LAIKFWIDLNEDIIKPHMLIAASNLQWRPESKSGLLTLFAGDFSVFSASP 3150
3151 KEGHFQETFNKMKNTVENIDILCNEAENKLMHILHANDPKWSTPTKDCTS 3200
3201 GPYTAQIIPGTGNKLLMSSPNCEIYYQSPLSLCMAKRKSVSTPVSAQMTS 3250
3251 KSCKGEKEIDDQKNCKKRRALDFLSRLPLPPPVSPICTFVSPAAQKAFQP 3300
3301 PRSCGTKYETPIKKKELNSPQMTPFKKFNEISLLESNSIADEELALINTQ 3350
3351 ALLSGSTGEKQFISVSESTRTAPTSSEDYLRLKRRCTTSLIKEQESSQAS 3400
3401 TEECEKNKQDTITTKKYI 3418
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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