 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q62407 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGRGVLVTGDGAGAPVFLRP 50
51 LKNAAVCAGSDVRLRVVVSGTPQPSLSWFRDGQLLPPPAPEPSCLWLRSC 100
101 GAQDAGVYSCSAQNERGQASCEAVLTVLEVRDSETAEDDISDVPGTQRLE 150
151 LRDDRAFSTPTGGSDTLVGTSLDTPPTSVTGTSEEQVSWWGSGQTVLEQE 200
201 AGSGGGTRPLPGSPRQAQTTGAGPRHLGVEPLVRASRANLVGASWGSEDS 250
251 LSVASDLYGSAFSLYRGRALSIHVSIPPSGLHREEPDLQPQPASDALRPR 300
301 PALPPPSKSALLPPPSPRVGKRALPGPSTQPPATPTSPHRRAQEPSLPED 350
351 ITTTEEKRGKKPKSSGPSLAGTVESRPQTPLSEASGRLSALGRSPRLVRA 400
401 GSRILDKLQFFEERRRSLERSDSPPAPLRPWVPLRKARSLEQPKSEGGAA 450
451 WGTPEASQEELRSPRGSVAERRRLFQQKAASLDERTRQRSATSDLELRFA 500
501 QELGRIRRSTSREELVRSHESLRATLQRAPSPREPGEPPLFSRPSTPKTS 550
551 RAVSPAATQPPPPSGAGKSGDEPGRPRSRGPVGRTEPGEGPQQEIKRRDQ 600
601 FPLTRSRAIQECRSPVPPYTADPPESRTKAPSGRKREPPAQAVRFLPWAT 650
651 PGVEDSVLPQTLEKNRAGPEAEKRLRRGPEEDGPWGPWDRRGTRSQGKGR 700
701 RARPTSPELESSDDSYVSAGEEPLEAPVFEIPLQNMVVAPGADVLLKCII 750
751 TANPPPQVSWKKDGSMLHSEGRLLIRAEGERHTLLLREAQAADAGSYTAT 800
801 ATNELGQATCASSLAVRPGGSTSPFSSPITSDEEYLSPPEEFPEPGETWP 850
851 RTPTMKLSPSQDHDSSDSSSKAPPTFKVSLMDQSVREGQDVIMSIRVQGE 900
901 PKPVVSWLRNRQPVRPDQRRFAEEAEGGLCRLRILAAERGDAGFYTCKAV 950
951 NEYGARQCEARLEVRAHPESRSLAVLAPLQDVDVGAGEMALFECLVAGPA 1000
1001 DVEVDWLCRGRLLQPALLKCKMHFDGRKCKLLLTSVHEDDSGVYTCKLST 1050
1051 AKDELTCSARLTVRPSLAPLFTRLLEDVEVLEGRAARLDCKISGTPPPSV 1100
1101 TWTHFGHPVNEGDNLRLRQDGGLHSLHIARVGSEDEGLYEVSATNTHGQA 1150
1151 HCSAQLYVEEPRTAASGPSSKLEKMPSIPEEPEHGDLERLSIPDFLRPLQ 1200
1201 DLEVGLAKEAMLECQVTGLPYPTISWFHNGHRIQSSDDRRMTQYRDIHRL 1250
1251 VFPAVGPQHAGVYKSVIANKLGKAACYAHLYVTDVVPGPPDGAPEVVAVT 1300
1301 GRMVTLSWNPPRSLDMAIDPDSLTYTVQHQVLGSDQWTALVTGLREPAWA 1350
1351 ATGLKKGIQHIFRVLSSSGKSSSKPSAPSEPVQLLEHGPPLEEAPAVLDK 1400
1401 QDIVYVVEGQPACVTVTFNHVEAQVVWRSCRGALLEARTGVYELSQPDDD 1450
1451 QYCLRICRVSRRDLGPLTCSARNRHGTKACSVTLELAEAPRFESIMEDVE 1500
1501 VGPGETARFAVVVEGKPLPDIMWYKDEVLLAESNHVSFVYEENECSLVLL 1550
1551 SAGSQDGGVYTCTARNLAGEVSCKAELSVLSAQTAMEVEGVGEDEEHRGR 1600
1601 RLSDYYDIHQEIGRGAFSYLRRVVERSSGLEFAAKFIPSQAKPKASARRE 1650
1651 ARLLARLQHGCVLYFHEAFERRRGLVIVTELCTEELLERMARKPTVCESE 1700
1701 TRTYMRQVLEGICYLHQSHVLHLDVKPENLLVWDGAGGEEQVRICDFGNA 1750
1751 QELTPGEPQYCQYGTPEFVAPEIVNQSPVSGVTDIWPVGVVAFLCLTGIS 1800
1801 PFVGENDRTTLMNIRNYNVAFEETTFLSLSREARGFLIKVLVQDRLRPTA 1850
1851 EETLEHPWFKTEAKGAEVSTDHLKLFLSRRRWQRSQISYKCHLVLRPIPE 1900
1901 LLRAPPERVWVAMPRRQPPSGGLSSSSDSEEEELEELPSVPRPLQPEFSG 1950
1951 SRVSLTDIPTEDEALGTPEAGAATPMDWQEQERTPSKDQEAPSPEALPSP 2000
2001 GQESPDGPSPRRPELRRGSSAESALPRVGSREPGRSLHKAASVELPQRRS 2050
2051 PSPGATRLTRGGLGEGEYAQRLQALRQRLLRGGPEDGKVSGLRGPLLESL 2100
2101 GGRARDPRMARAASSEAAPHHQPPPESRGLQKSSSFSQGEAEPRGRHRRA 2150
2151 GAPLEIPVARLGARRLQESPSLSALSETQPPSPARPSVPKLSITKSPEPS 2200
2201 AVTSRDSPQPPEPQPVPEKVPEPKPEPVRAAKPAQPPLALQMPTQPLTPY 2250
2251 AQIMQSLQLSSPTLSPQDPAVPPSEPKPHAAVFARVASPPPGVSEKRVPS 2300
2301 ARTPPVLAEKARVPTVPPRPGSSLSGSIENLESEAVFEAKFKRSRESPLS 2350
2351 RGLRLLSRSRSEERGPFRGAEDDGIYRPSPAGTPLELVRRPERSRSVQDL 2400
2401 RVAGEPGLVRRLSLSLSQKLRRTPPGQRHPAWESRSGDGESSEGGSSARA 2450
2451 SPVLAVRRRLSSTLERLSSRLQRSGSSEDSGGASGRSTPLFGRLRRATSE 2500
2501 GESLRRLGVPHNQLGSQTGATTPSAESLGSEASGTSGSSAPGESRSRHRW 2550
2551 GLSRLRKDKGLSQPNLSSSVQEDLGHQYVPSESDFPPVFHIKLKDQVLLE 2600
2601 GEAATLLCLPAACPAPRISWMKDKQSLRSEPSVVIVSCKDGRQLLSIPRA 2650
2651 GKRHAGLYECSATNVLGSITSSCTVAVARIPGKLAPPEVPQTYHDTALVV 2700
2701 WKPGDGRAPCTYTLERRVDGESVWHPVSSGIPDCYYNVTQLPVGVTVRFR 2750
2751 VACSNRAGQGPFSNPSEKVFIRGTPDSPAQPAAAPRDAPVTSGPTRAPPP 2800
2801 DSPTSLAPTPALAPPASQASTLSPSTSSMSANQALSSLKAVGPPPATPPR 2850
2851 KHRGLLATQQAEPSPPSIVVTPSEPRSFVPDTGTLTPTSSPQGVKPAPSS 2900
2901 TSLYMVTSFVSAPPAPQAPAPEPPPEPTKVTVRSLSPAKEVVSSPTPEST 2950
2951 TLRQGPPQKPYTFLEEKARGRFGVVRSCRENATGRTFVAKIVPYAAEGKR 3000
3001 RVLQEYEVLRTLHHERLMSLHEAYITPRYLVLIAESCGNRELLCGLSDRF 3050
3051 RYSEDDVATYVVQLLQGLDYLHGHHVLHLDIKPDNLLLAADNALKIVDFG 3100
3101 SAQPYNPQALKPLGHRTGTLEFMAPEMVKGDPIGSATDIWGAGVLTYIML 3150
3151 SGYSPFYEPDPQETEARIVGGRFDAFQLYPNTSQSATLFLRKVLSVHPWS 3200
3201 RPSLQDCLAHPWLQDAYLMKLRRQTLTFTTNRLKEFLGEQRRRRAEAATR 3250
3251 HKVLLRSYPGSP 3262
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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