 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q92608 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MAPWRKADKERHGVAIYNFQGSGAPQLSLQIGDVVRIQETCGDWYRGYLI 50
51 KHKMLQGIFPKSFIHIKEVTVEKRRNTENIIPAEIPLAQEVTTTLWEWGS 100
101 IWKQLYVASKKERFLQVQSMMYDLMEWRSQLLSGTLPKDELKELKQKVTS 150
151 KIDYGNKILELDLIVRDEDGNILDPDNTSVISLFHAHEEATDKITERIKE 200
201 EMSKDQPDYAMYSRISSSPTHSLYVFVRNFVCRIGEDAELFMSLYDPNKQ 250
251 TVISENYLVRWGSRGFPKEIEMLNNLKVVFTDLGNKDLNRDKIYLICQIV 300
301 RVGKMDLKDTGAKKCTQGLRRPFGVAVMDITDIIKGKAESDEEKQHFIPF 350
351 HPVTAENDFLHSLLGKVIASKGDSGGQGLWVTMKMLVGDIIQIRKDYPHL 400
401 VDRTTVVARKLGFPEIIMPGDVRNDIYITLLQGDFDKYNKTTQRNVEVIM 450
451 CVCAEDGKTLPNAICVGAGDKPMNEYRSVVYYQVKQPRWMETVKVAVPIE 500
501 DMQRIHLRFMFRHRSSLESKDKGEKNFAMSYVKLMKEDGTTLHDGFHDLV 550
551 VLKGDSKKMEDASAYLTLPSYRHHVENKGATLSRSSSSVGGLSVSSRDVF 600
601 SISTLVCSTKLTQNVGLLGLLKWRMKPQLLQENLEKLKIVDGEEVVKFLQ 650
651 DTLDALFNIMMEHSQSDEYDILVFDALIYIIGLIADRKFQHFNTVLEAYI 700
701 QQHFSATLAYKKLMTVLKTYLDTSSRGEQCEPILRTLKALEYVFKFIVRS 750
751 RTLFSQLYEGKEQMEFEESMRRLFESINNLMKSQYKTTILLQVAALKYIP 800
801 SVLHDVEMVFDAKLLSQLLYEFYTCIPPVKLQKQKVQSMNEIVQSNLFKK 850
851 QECRDILLPVITKELKELLEQKDDMQHQVLERKYCVELLNSILEVLSYQD 900
901 AAFTYHHIQEIMVQLLRTVNRTVITMGRDHILISHFVACMTAILNQMGDQ 950
951 HYSFYIETFQTSSELVDFLMETFIMFKDLIGKNVYPGDWMAMSMVQNRVF 1000
1001 LRAINKFAETMNQKFLEHTNFEFQLWNNYFHLAVAFITQDSLQLEQFSHA 1050
1051 KYNKILNKYGDMRRLIGFSIRDMWYKLGQNKICFIPGMVGPILEMTLIPE 1100
1101 AELRKATIPIFFDMMLCEYQRSGDFKKFENEIILKLDHEVEGGRGDEQYM 1150
1151 QLLESILMECAAEHPTIAKSVENFVNLVKGLLEKLLDYRGVMTDESKDNR 1200
1201 MSCTVNLLNFYKDNNREEMYIRYLYKLRDLHLDCDNYTEAAYTLLLHTWL 1250
1251 LKWSDEQCASQVMQTGQQHPQTHRQLKETLYETIIGYFDKGKMWEEAISL 1300
1301 CKELAEQYEMEIFDYELLSQNLIQQAKFYESIMKILRPKPDYFAVGYYGQ 1350
1351 GFPSFLRNKVFIYRGKEYERREDFQMQLMTQFPNAEKMNTTSAPGDDVKN 1400
1401 APGQYIQCFTVQPVLDEHPRFKNKPVPDQIINFYKSNYVQRFHYSRPVRR 1450
1451 GTVDPENEFASMWIERTSFVTAYKLPGILRWFEVVHMSQTTISPLENAIE 1500
1501 TMSTANEKILMMINQYQSDETLPINPLSMLLNGIVDPAVMGGFAKYEKAF 1550
1551 FTEEYVRDHPEDQDKLTHLKDLIAWQIPFLGAGIKIHEKRVSDNLRPFHD 1600
1601 RMEECFKNLKMKVEKEYGVREMPDFDDRRVGRPRSMLRSYRQMSIISLAS 1650
1651 MNSDCSTPSKPTSESFDLELASPKTPRVEQEEPISPGSTLPEVKLRRSKK 1700
1701 RTKRSSVVFADEKAAAESDLKRLSRKHEFMSDTNLSEHAAIPLKASVLSQ 1750
1751 MSFASQSMPTIPALALSVAGIPGLDEANTSPRLSQTFLQLSDGDKKTLTR 1800
1801 KKVNQFFKTMLASKSAEEGKQIPDSLSTDL 1830
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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