 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q63638 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGRGVLVTGAGAGAPVFLRP 50
51 LKNAAVCAGSDVRLRVVVSGTPEPSLSWFRDGQLLPTPVPEPSCLWLRNC 100
101 GAQDAGVYSCSAQNERGKASCEAVLTVLEVRDSETAEDDISDVPGTQRLE 150
151 LRDDRAFSTPTGGSDTLVGTSLDTPPTSVTGTSEEQVSWWGSGQTVLEQE 200
201 AGSGGGTRPLPGSPRQAQTTGAGPRHLGVEPLVRASRANLVGASWGSEDS 250
251 LSVASDLYGSAFSLYRGRALSIHVSIPPSGLRREEPDLQPQPASDALRPR 300
301 PALPPPSKSALLPPPSPRVGKRALPGSSAQPPATPTSPHRCTQEPSLPED 350
351 ITTTEEKRGKKPKSSGPSLAGTVESRPQTPLSEASGRLSALGRSPRLVRA 400
401 GSRILDKLQFFEERRRSLERSDSPPAPLRPWVPLRKARSLEQPKSEGGAA 450
451 WDTPGASQEELRSPRGSVAERRRLFQQKAASLDERTRQRSATSDLELRFA 500
501 QELGRIRRSTSREELVRSHESLRATLQRAPSPREPGEPPLFSRPSIPKTS 550
551 RAVSPAATQPPPPSGAGKSGDETGRPRSRGPVGRTEPGEGPQQEIKRRDQ 600
601 FPLTRSRAIQECRSPVPPFTADPPESRTKAPSARKREPPAQAVRFLPWAT 650
651 PGVEDSVLPQTLEKNRAGPEAEKRLRRGPEEDGPWGAWDRRGTRSQGKGR 700
701 RARPTSPELESSDDSYVSAGEEPLEAPVFEIPLQNMVVAPGADVLLKCII 750
751 TANPPPQVSWKKDGSVLHSEGRLLIRAEGERHTLLLREAQAADAGSYTAT 800
801 ATNELGQASCASSLAVRPGASTSPFSSPITSDEEYLSPPEEFPEPGETWS 850
851 RTPTMKPSPSQDRDSSDSSSKAPPTFKVSLMDQSVREGQDVIMSIRVQGE 900
901 PKPVVSWLRNRQPVRPDQRRFAEEAEGGLCRLRILAAERGDAGFYTCKAV 950
951 NEYGARQCEARLEVRAHPESRSLAVLAPLQDVDVGAGEMALFECLVAGPA 1000
1001 DVEVDWLCRGRLLQPALLKCKMHFDGRKCKLLLTSVHEDDSGVYTCKLST 1050
1051 VKDELTCSARLTVRPSLAPLFTRLLEDVEVLEGRAARLDCKISGTPPPSV 1100
1101 TWTHFGHPVNESENLRLRQDGGLHSLHIARVGSEDEGLYEVSATNTHGQA 1150
1151 HCSAQLYVEEPRTAASGPSSKLEKMPSIPEEPEHGDLERLSIPDFLRPLQ 1200
1201 DLEVGLAKEAMLECQVTGLPYPTISWFHNGHRIQSSDDRRMTQYRDIHRL 1250
1251 VFPAVGPQHAGVYKSVIANKLGKAACYAHLYVTDVVPGPPDGAPQVVAVT 1300
1301 GRMVTLSWNPPRSLDMAIDPDSLTYTVQHQVLGSDQWTALVTGLREPEWA 1350
1351 ATGLKKGLHHIFRVLSSSGKSSSKPSAPSEPVQLLEHGPPLEEAPAVLDK 1400
1401 QDIVYVVEGQPACVTVTFNHVEAQVVWRSCRGALLEPRTGVYELSQPDDD 1450
1451 QYCLRICRVSRRDLGPLTCSARNRHGTKACSITLELAEAPRFESIMEDVE 1500
1501 VGPGETARFAVVVEGKPLPDIMWYKDEVLLAESNHVSFVYEENECSLVVL 1550
1551 SAGSQDGGVYTCTARNLAGEVSCKAELSVHSAQTAMEVEGVGEDEEHRGR 1600
1601 RLSDYYDIHQEIGRGAFSYLRRVVERSSGLEFAAKFIPSQAKPKASARRE 1650
1651 ARLLARLQHDCVLYFHEAFERRRGLVIVTELCTEELLERMARKPTVCESE 1700
1701 TRTYMRQVLEGIGYLHQSHVLHLDVKPENLLVWDGAGGEEQVRICDFGNA 1750
1751 QELTPGEPQYCQFGTPEFVAPEIVNQSPVSGVTDIWPVGVVAFLCLTGIS 1800
1801 PFVGENDRTTLMNIRNYNVAFEETTFLSLSREARGFLIKVLVQDRLRPTA 1850
1851 EETLEHPWFKTEAKGAEVSTDHLKLFLSRRRWQRSQISYKCHLVLRPIPE 1900
1901 LLRAPPERVWVAMPRRQPPSGGLSSSSDSEEEELEELPSVPRPLQPEFSG 1950
1951 SRVSLTDIPTEDEALGTPEAGAATPMDWQEQGRAPSKDQEAPSPEALPSP 2000
2001 GQESPDGPSPRRPELRRGSSAESALPRVGSREPGRSLHKAASVELPQRRS 2050
2051 PSPGATRLTRGGLGEGEYAQRLQALRQRLLRGGPEDGKVSGLRGPLLESL 2100
2101 GGRARDPRMARAASSEAAPHHQPPPESRGLQKSSSFSQGEAEPRGRHRRA 2150
2151 GAPLEIPVARLGARRLQESPSLSALSETQPPSPALPSAPKPSITKSPEPS 2200
2201 AATSRDSPQPPAPQPVPEKIPEPKPEPVRAAKPAQPPLALQMPAQSLTPY 2250
2251 AQIMQSLQLSSPTLSPQVPPSEPKPHAAVFARVASPPPGASEKRVPSARI 2300
2301 PPVLAEKVRVPTVPPRPGSSLSGSIENLESEAVFEAKFKRSRESPLSRGL 2350
2351 RLLSRSRSEERGPFRGAEDDGIYRPSPAGTPLELVRRPERSRSVQDLRVA 2400
2401 GEPGLVRRLSLSLSQKLRRTPPGQRHPAWESRSGDGESSEGGSSARGSPV 2450
2451 LAVRRRLSSTLERLSSRLQRSGSSEDSGGASGRSTPLFGRLRRATSEGES 2500
2501 LRRLGVPHNQLASQTGATTPSAESLGSEASGTSGSSAPGESRSRHRWGLS 2550
2551 RLRKDKGLSQPNLSASVQEDLGHQYVPSESDFPPVFHIKLKDQVLLEGEA 2600
2601 ATLLCLPAACPTPRISWMKDKQSLRSEPSVVIVSCKDGRQLLSIPRASKR 2650
2651 HAGLYECSATNVLGSITSSCTVAVARTPGKLAPPEVPQTYCDTALVLWKP 2700
2701 GDSRAPCTYTLERRVDGESVWHPVSSGIPDCYYNVTQLPVGVTVRFRVAC 2750
2751 SNRAGQGPFSNPSEKVFIRGTQDSPAQSAAAPRDAPVTSGPTRAPPPDSP 2800
2801 TSLVPTPPLAPQVSQASTLSPSTSSMSANQALSSLKAVGPPPATPPRKHR 2850
2851 GLLATQQAEPSPPSILVTPSEHKSFVPDTGTLTPTSSPQGVKPAPSSSSL 2900
2901 YMVTSFVSAPPDPQPPAPEPPPEPTKVTVRSLSPAKEVVSSPTPESTTLR 2950
2951 QGPPQKPYTFLEEKARGRFGVVRSCRENATGRTFVAKIVPYAAEGKRRVL 3000
3001 QEYEVLRTLHHERLMSLHEAYITPRYLVLIAESCGNRELLCGLSDRFRYS 3050
3051 EDDVATYVVQLLQGLDYLHGRHVLHLDIKPDNLLLAADNALKIVDFGSAQ 3100
3101 PYNPQALKPLGHRTGTLEFMAPEMVRGDPIGSATDIWGAGVLTYIMLSGY 3150
3151 SPFYEPDPQETEARIVGGRFDAFQLYPNTSQSATLFLRKVLSVHPWSRPS 3200
3201 LQDCLAHPWLQDAYLMKLRRQTLTFTTNRLKEFLGEQRRRRAEAATRHKV 3250
3251 LLRSYPGSP 3259
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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