 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P04114 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MDPPRPALLALLALPALLLLLLAGARAEEEMLENVSLVCPKDATRFKHLR 50
51 KYTYNYEAESSSGVPGTADSRSATRINCKVELEVPQLCSFILKTSQCTLK 100
101 EVYGFNPEGKALLKKTKNSEEFAAAMSRYELKLAIPEGKQVFLYPEKDEP 150
151 TYILNIKRGIISALLVPPETEEAKQVLFLDTVYGNCSTHFTVKTRKGNVA 200
201 TEISTERDLGQCDRFKPIRTGISPLALIKGMTRPLSTLISSSQSCQYTLD 250
251 AKRKHVAEAICKEQHLFLPFSYKNKYGMVAQVTQTLKLEDTPKINSRFFG 300
301 EGTKKMGLAFESTKSTSPPKQAEAVLKTLQELKKLTISEQNIQRANLFNK 350
351 LVTELRGLSDEAVTSLLPQLIEVSSPITLQALVQCGQPQCSTHILQWLKR 400
401 VHANPLLIDVVTYLVALIPEPSAQQLREIFNMARDQRSRATLYALSHAVN 450
451 NYHKTNPTGTQELLDIANYLMEQIQDDCTGDEDYTYLILRVIGNMGQTME 500
501 QLTPELKSSILKCVQSTKPSLMIQKAAIQALRKMEPKDKDQEVLLQTFLD 550
551 DASPGDKRLAAYLMLMRSPSQADINKIVQILPWEQNEQVKNFVASHIANI 600
601 LNSEELDIQDLKKLVKEALKESQLPTVMDFRKFSRNYQLYKSVSLPSLDP 650
651 ASAKIEGNLIFDPNNYLPKESMLKTTLTAFGFASADLIEIGLEGKGFEPT 700
701 LEALFGKQGFFPDSVNKALYWVNGQVPDGVSKVLVDHFGYTKDDKHEQDM 750
751 VNGIMLSVEKLIKDLKSKEVPEARAYLRILGEELGFASLHDLQLLGKLLL 800
801 MGARTLQGIPQMIGEVIRKGSKNDFFLHYIFMENAFELPTGAGLQLQISS 850
851 SGVIAPGAKAGVKLEVANMQAELVAKPSVSVEFVTNMGIIIPDFARSGVQ 900
901 MNTNFFHESGLEAHVALKAGKLKFIIPSPKRPVKLLSGGNTLHLVSTTKT 950
951 EVIPPLIENRQSWSVCKQVFPGLNYCTSGAYSNASSTDSASYYPLTGDTR 1000
1001 LELELRPTGEIEQYSVSATYELQREDRALVDTLKFVTQAEGAKQTEATMT 1050
1051 FKYNRQSMTLSSEVQIPDFDVDLGTILRVNDESTEGKTSYRLTLDIQNKK 1100
1101 ITEVALMGHLSCDTKEERKIKGVISIPRLQAEARSEILAHWSPAKLLLQM 1150
1151 DSSATAYGSTVSKRVAWHYDEEKIEFEWNTGTNVDTKKMTSNFPVDLSDY 1200
1201 PKSLHMYANRLLDHRVPQTDMTFRHVGSKLIVAMSSWLQKASGSLPYTQT 1250
1251 LQDHLNSLKEFNLQNMGLPDFHIPENLFLKSDGRVKYTLNKNSLKIEIPL 1300
1301 PFGGKSSRDLKMLETVRTPALHFKSVGFHLPSREFQVPTFTIPKLYQLQV 1350
1351 PLLGVLDLSTNVYSNLYNWSASYSGGNTSTDHFSLRARYHMKADSVVDLL 1400
1401 SYNVQGSGETTYDHKNTFTLSCDGSLRHKFLDSNIKFSHVEKLGNNPVSK 1450
1451 GLLIFDASSSWGPQMSASVHLDSKKKQHLFVKEVKIDGQFRVSSFYAKGT 1500
1501 YGLSCQRDPNTGRLNGESNLRFNSSYLQGTNQITGRYEDGTLSLTSTSDL 1550
1551 QSGIIKNTASLKYENYELTLKSDTNGKYKNFATSNKMDMTFSKQNALLRS 1600
1601 EYQADYESLRFFSLLSGSLNSHGLELNADILGTDKINSGAHKATLRIGQD 1650
1651 GISTSATTNLKCSLLVLENELNAELGLSGASMKLTTNGRFREHNAKFSLD 1700
1701 GKAALTELSLGSAYQAMILGVDSKNIFNFKVSQEGLKLSNDMMGSYAEMK 1750
1751 FDHTNSLNIAGLSLDFSSKLDNIYSSDKFYKQTVNLQLQPYSLVTTLNSD 1800
1801 LKYNALDLTNNGKLRLEPLKLHVAGNLKGAYQNNEIKHIYAISSAALSAS 1850
1851 YKADTVAKVQGVEFSHRLNTDIAGLASAIDMSTNYNSDSLHFSNVFRSVM 1900
1901 APFTMTIDAHTNGNGKLALWGEHTGQLYSKFLLKAEPLAFTFSHDYKGST 1950
1951 SHHLVSRKSISAALEHKVSALLTPAEQTGTWKLKTQFNNNEYSQDLDAYN 2000
2001 TKDKIGVELTGRTLADLTLLDSPIKVPLLLSEPINIIDALEMRDAVEKPQ 2050
2051 EFTIVAFVKYDKNQDVHSINLPFFETLQEYFERNRQTIIVVLENVQRNLK 2100
2101 HINIDQFVRKYRAALGKLPQQANDYLNSFNWERQVSHAKEKLTALTKKYR 2150
2151 ITENDIQIALDDAKINFNEKLSQLQTYMIQFDQYIKDSYDLHDLKIAIAN 2200
2201 IIDEIIEKLKSLDEHYHIRVNLVKTIHDLHLFIENIDFNKSGSSTASWIQ 2250
2251 NVDTKYQIRIQIQEKLQQLKRHIQNIDIQHLAGKLKQHIEAIDVRVLLDQ 2300
2301 LGTTISFERINDILEHVKHFVINLIGDFEVAEKINAFRAKVHELIERYEV 2350
2351 DQQIQVLMDKLVELAHQYKLKETIQKLSNVLQQVKIKDYFEKLVGFIDDA 2400
2401 VKKLNELSFKTFIEDVNKFLDMLIKKLKSFDYHQFVDETNDKIREVTQRL 2450
2451 NGEIQALELPQKAEALKLFLEETKATVAVYLESLQDTKITLIINWLQEAL 2500
2501 SSASLAHMKAKFRETLEDTRDRMYQMDIQQELQRYLSLVGQVYSTLVTYI 2550
2551 SDWWTLAAKNLTDFAEQYSIQDWAKRMKALVEQGFTVPEIKTILGTMPAF 2600
2601 EVSLQALQKATFQTPDFIVPLTDLRIPSVQINFKDLKNIKIPSRFSTPEF 2650
2651 TILNTFHIPSFTIDFVEMKVKIIRTIDQMLNSELQWPVPDIYLRDLKVED 2700
2701 IPLARITLPDFRLPEIAIPEFIIPTLNLNDFQVPDLHIPEFQLPHISHTI 2750
2751 EVPTFGKLYSILKIQSPLFTLDANADIGNGTTSANEAGIAASITAKGESK 2800
2801 LEVLNFDFQANAQLSNPKINPLALKESVKFSSKYLRTEHGSEMLFFGNAI 2850
2851 EGKSNTVASLHTEKNTLELSNGVIVKINNQLTLDSNTKYFHKLNIPKLDF 2900
2901 SSQADLRNEIKTLLKAGHIAWTSSGKGSWKWACPRFSDEGTHESQISFTI 2950
2951 EGPLTSFGLSNKINSKHLRVNQNLVYESGSLNFSKLEIQSQVDSQHVGHS 3000
3001 VLTAKGMALFGEGKAEFTGRHDAHLNGKVIGTLKNSLFFSAQPFEITAST 3050
3051 NNEGNLKVRFPLRLTGKIDFLNNYALFLSPSAQQASWQVSARFNQYKYNQ 3100
3101 NFSAGNNENIMEAHVGINGEANLDFLNIPLTIPEMRLPYTIITTPPLKDF 3150
3151 SLWEKTGLKEFLKTTKQSFDLSVKAQYKKNKHRHSITNPLAVLCEFISQS 3200
3201 IKSFDRHFEKNRNNALDFVTKSYNETKIKFDKYKAEKSHDELPRTFQIPG 3250
3251 YTVPVVNVEVSPFTIEMSAFGYVFPKAVSMPSFSILGSDVRVPSYTLILP 3300
3301 SLELPVLHVPRNLKLSLPDFKELCTISHIFIPAMGNITYDFSFKSSVITL 3350
3351 NTNAELFNQSDIVAHLLSSSSSVIDALQYKLEGTTRLTRKRGLKLATALS 3400
3401 LSNKFVEGSHNSTVSLTTKNMEVSVATTTKAQIPILRMNFKQELNGNTKS 3450
3451 KPTVSSSMEFKYDFNSSMLYSTAKGAVDHKLSLESLTSYFSIESSTKGDV 3500
3501 KGSVLSREYSGTIASEANTYLNSKSTRSSVKLQGTSKIDDIWNLEVKENF 3550
3551 AGEATLQRIYSLWEHSTKNHLQLEGLFFTNGEHTSKATLELSPWQMSALV 3600
3601 QVHASQPSSFHDFPDLGQEVALNANTKNQKIRWKNEVRIHSGSFQSQVEL 3650
3651 SNDQEKAHLDIAGSLEGHLRFLKNIILPVYDKSLWDFLKLDVTTSIGRRQ 3700
3701 HLRVSTAFVYTKNPNGYSFSIPVKVLADKFIIPGLKLNDLNSVLVMPTFH 3750
3751 VPFTDLQVPSCKLDFREIQIYKKLRTSSFALNLPTLPEVKFPEVDVLTKY 3800
3801 SQPEDSLIPFFEITVPESQLTVSQFTLPKSVSDGIAALDLNAVANKIADF 3850
3851 ELPTIIVPEQTIEIPSIKFSVPAGIVIPSFQALTARFEVDSPVYNATWSA 3900
3901 SLKNKADYVETVLDSTCSSTVQFLEYELNVLGTHKIEDGTLASKTKGTFA 3950
3951 HRDFSAEYEEDGKYEGLQEWEGKAHLNIKSPAFTDLHLRYQKDKKGISTS 4000
4001 AASPAVGTVGMDMDEDDDFSKWNFYYSPQSSPDKKLTIFKTELRVRESDE 4050
4051 ETQIKVNWEEEAASGLLTSLKDNVPKATGVLYDYVNKYHWEHTGLTLREV 4100
4101 SSKLRRNLQNNAEWVYQGAIRQIDDIDVRFQKAASGTTGTYQEWKDKAQN 4150
4151 LYQELLTQEGQASFQGLKDNVFDGLVRVTQEFHMKVKHLIDSLIDFLNFP 4200
4201 RFQFPGKPGIYTREELCTMFIREVGTVLSQVYSKVHNGSEILFSYFQDLV 4250
4251 ITLPFELRKHKLIDVISMYRELLKDLSKEAQEVFKAIQSLKTTEVLRNLQ 4300
4301 DLLQFIFQLIEDNIKQLKEMKFTYLINYIQDEINTIFSDYIPYVFKLLKE 4350
4351 NLCLNLHKFNEFIQNELQEASQELQQIHQYIMALREEYFDPSIVGWTVKY 4400
4401 YELEEKIVSLIKNLLVALKDFHSEYIVSASNFTSQLSSQVEQFLHRNIQE 4450
4451 YLSILTDPDGKGKEKIAELSATAQEIIKSQAIATKKIISDYHQQFRYKLQ 4500
4501 DFSDQLSDYYEKFIAESKRLIDLSIQNYHTFLIYITELLKKLQSTTVMNP 4550
4551 YMKLAPGELTIIL 4563
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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