 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q756G2 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MVVTLTKLERLQKAEKSRTFKPLIDELINCEDAVFVGKLEAIREWDRPKD 50
51 DLFLWIPVLDRMDDMLGKVVAKYKYHTTDCKRHPVKLIEMAKPDEDWVAT 100
101 LLFFTCRLLNNTSNRSLYSSLDMMSDLLNCPNFRVKLGAMKVIATIGERH 150
151 VVARHRIENSSVLASQHLKKRSLSLALALPSSTTDDNSDHFSLVDLFFDK 200
201 RKYPSKWSQLQYTYYITKKQAGQQSTQKPSQVSSMKRFVLNNEELRFLSL 250
251 QQIFDRAMNDLPSEFWFDFSLQASIAKAFSDDSFENIQLRSLIIQTKFAA 300
301 IAFANAIYIPPQVSSKLFEMDPYAFNNLTDFISLSETKIPKDLRTDALFA 350
351 LECISLKHVWCSDIVRNLGGNMSHGLLFQILRYIAKVLREGDEDAVDEEY 400
401 NVRFFYLISNLADVKALQESLISAGLISSLLEIVSTQNSKYKRTLASAAH 450
451 LLEDVISDADATAEFINNNGFNILIQTVTYEVNFAVQNPSSAEPPKYSVV 500
501 YYSISFRQLGFIRSLLKLVLKLLKTDSGDRIRNLIDSPILLAFNKILENR 550
551 PVFGYTLVSHALDVVQTIINTEPTIYQVLVESGTIPYILQNFDQFLGPTS 600
601 DLLCMLPEVISAICLNTDGLKQVKEKNLLKYLFQVIKTPEFAKILSWEDQ 650
651 AVNYGVALDELARHYPELKPLIEDYFAATVKELPSLTSYTHSYLYESTAG 700
701 NGEFYLSENETIIDNEDGADELAFWEVQESSPIIDCFSGVFYSMASENIS 750
751 WANLTEKIAFQDFLSVAIPEKPTFDYINSQTLLNFTDVLKMFDDERRSYA 800
801 LPELLTILDSKFADLEDFLSYDFTKSYVLNEPKDRVVATLNKLNVLNVIL 850
851 YIMTDIYINITTLFPVRVIQMMEFFEKNGFNLITNLRKMFQRCVLEEMYI 900
901 RSRLPPAVAEETISPGISFVPPILIHKDVPLKTEAKQEKTSAKFKNTLET 950
951 RHLFQKVQSWTSMLFRCFLRLTHARKMNVERYDRALEIRIFDKVVNEIVE 1000
1001 MLNLDYLDSHLSYFLVVLDFNTHIFTCPKASLTISDGIVQTVPTFLFYQA 1050
1051 GGFAIYHELIKKLSTRLLGFDGIEAIEKVEYVKDQDDILTVGVLMNSIAF 1100
1101 MNRCIQLETMENIRSIAEYYPYDDIYYNLTRALIVPVKILSLGAISEIFS 1150
1151 QGEVFDTDRRRIPYSVFKQILSLLKNIYNSTFELEEGTDVYELRWDLMPI 1200
1201 SHRKVEMLKSCGISHDVAVGYLEEEKDELPIHVKPDVFSDSEWQRYQEER 1250
1251 SSGSWRTNIELLPPQYNGSSTRETLSKMRTEFYQNGFESGILKILQHYPK 1300
1301 LINAISHMFFEMYGELGFSHTTMLEDLQDMMNTIPIENTNKLAPVIHLFG 1350
1351 IFLNDKNIYDQAKKEIFKFVSYLSMNLHPENVNYSWFSKALYVYEIIFAK 1400
1401 SETPEASPLPENVTVSFSSIPIIYRISQKDKDIIFNLLIRANEVTDFYSA 1450
1451 LAISRILILYTREERYAQEVTQSGILSKLLKVIGANQKFDKINYLESSYL 1500
1501 LLVRRCFETKEIVTSLIDYELTRAFTTRAIGDHKEKPRDLPALVNEKASI 1550
1551 VMRDPEVFVNRISETARFEDFNSSKELASLSVRRHMDEKDEEMTSKPLSS 1600
1601 EGSNSPITGIVHLLLSQLMAAHKRDWVSETPLTPEEENQKNKKKKDEVKV 1650
1651 SKNPVCAYMIFLLKVLTELVSSYKQSKFEFLTYNKKNVYNETPKPRATAL 1700
1701 NFFLYQLLDTNFQDMDKHEGKRRAMISGLARDTIIGFVSSVQDSETKKVD 1750
1751 PKIVDSDMTYIRKFTIEAISKAIKEAGVSAKTIDANAGKLFGWFHLISSL 1800
1801 LVVDKGYIFSVLDSNKSSNDKYQLCKLLIEMNTPGTITDCMASLDLNYPL 1850
1851 TKKLFNSAVEPLNALNEVRNNFADLFKLENNEDEDVEDVESDKDDVPDMF 1900
1901 KNSALGMYDIEDVEDDHDDDENESLIGDDEDIAFVHDEDGLEVVFSEDED 1950
1951 ANEDTTDASNSIGSDSEGNSDFGGSGPNVTLEVYTDSEDAIEDVNTAAIR 2000
2001 ITSGNSAEHSYYSEGEDSAEIEIYEEEYDSEIDIDMDDDSELGSSNWESG 2050
2051 LSDLSDSEAYSDEERTNNTGDGFVRWYSDDGVEFEDDTDEEGRGLFTGIQ 2100
2101 HVFPTEEQLFRVHASGAARSTGRHHHRHGAAPFTTSTITLGASQRRPHSI 2150
2151 LSNPLGPSGLEEVENDIVSHYLGNVETSDRIGLSSIPRLPRVLLFDGELF 2200
2201 DDKSSSGILLKSTTARWNDIYEMFYDSKVYSNNVVTTIISRIFEPSAELY 2250
2251 LKEQEENAVKESSRINEPTRRQDERKRKLHEIDSDEEHIEEEEEHDEVVE 2300
2301 PIEAPGINSPQARAPQEPVYVTIDGEAVNIAGTDMDAEFLNALPDDIRAE 2350
2351 VFAQHIREYRTQFQGSEGSSRELDAGFLNTIPETLRQEILAQEVPLERNA 2400
2401 RPSILGLRNREGEEFSEVEDESPRFNEQRTESSKTKTDRVHFAPLLDRSG 2450
2451 IAAIMKAVFIPQPYLSREIYHELFYRLCSSKQNRSDIMNMLLLILMDGIN 2500
2501 DQHSLERVYNLLSNRAASSNSGTSKTPQRQLPPDCTPLIVTNQCIEILQS 2550
2551 MVDADSKLKYFFITEHENLLINKSPLKNKKDIFSKNMKWPINCILALLEK 2600
2601 KVITDEAVLMDLLTRILQVCSKPISSIVKSSKDGKKKKFEVPDIEKKYLA 2650
2651 SIVSIIKLDSCNTKVFQQTLNLMTNLFAIKDAHETFTTELCNLAKETIEV 2700
2701 LVTDLDALAKEVPAVDSGTEVSSEIIQKFTVPSSDQSKLLKVLTAIDYIY 2750
2751 VNRKKEEEQVVDQLLPLYNKMELGHIWVSLSNCLTRFEEKPRMSTSATIL 2800
2801 LPLIESLMVVCKHSKVRETKDALLKYEAKKCDFARTPVENLFFAFTDLHK 2850
2851 KLLNEMIRSNPKLMSGPFSLLVKNPKILDFDNKRYYFTAQLRAITHDRPK 2900
2901 LSISVRREHVFLDSYRSLFFKSNEDIKISKLEISFKGEAGVDAGGITREW 2950
2951 YQVLSRQMFNPDYALFIPVASDKTTFRPNRTSGINPEHLSFFKFIGMIIG 3000
3001 KAISDQCFLDCHFSREVYKNILGKPVALKDMESLDLDYYKSLIWILENDI 3050
3051 TDIIEETFSVETDDYGEHKVIELIENGAHVAVTEQNKHDYVKKIVEYKLQ 3100
3101 TSVKDQMENFLQGFYAIIPKDLISIFDEQELELLVSGLPDIDVDDWKNNT 3150
3151 IYVNYTPTCKQINYFWRAVRSFDKEERAKLLQFVTGTSKVPLNGFKELSG 3200
3201 VNGISKFSIHRDYGSIDRLPSSHTCFNQLDLPAYDSYETLRGSLLLAINE 3250
3251 GHEGFGIA 3258
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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