 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q15772 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFLRPLK 50
51 NAAVCAGSDVRLRVVVSGTPQPSLRWFRDGQLLPAPAPEPSCLWLRRCGA 100
101 QDAGVYSCMAQNERGRASCEAVLTVLEVGDSETAEDDISDVQGTQRLELR 150
151 DDGAFSTPTGGSDTLVGTSLDTPPTSVTGTSEEQVSWWGSGQTVLEQEAG 200
201 SGGGTRRLPGSPRQAQATGAGPRHLGVEPLVRASRANLVGASWGSEDSLS 250
251 VASDLYGSAFSLYRGRALSIHVSVPQSGLRREEPDLQPQLASEAPRRPAQ 300
301 PPPSKSALLPPPSPRVGKRSPPGPPAQPAATPTSPHRRTQEPVLPEDTTT 350
351 EEKRGKKSKSSGPSLAGTAESRPQTPLSEASGRLSALGRSPRLVRAGSRI 400
401 LDKLQFFEERRRSLERSDSPPAPLRPWVPLRKARSLEQPKSERGAPWGTP 450
451 GASQEELRAPGSVAERRRLFQQKAASLDERTRQRSPASDLELRFAQELGR 500
501 IRRSTSREELVRSHESLRATLQRAPSPREPGEPPLFSRPSTPKTSRAVSP 550
551 AAAQPPSPSSAEKPGDEPGRPRSRGPAGRTEPGEGPQQEVRRRDQFPLTR 600
601 SRAIQECRSPVPPPAADPPEARTKAPPGRKREPPAQAVRFLPWATPGLEG 650
651 AAVPQTLEKNRAGPEAEKRLRRGPEEDGPWGPWDRRGARSQGKGRRARPT 700
701 SPELESSDDSYVSAGEEPLEAPVFEIPLQNVVVAPGADVLLKCIITANPP 750
751 PQVSWHKDGSALRSEGRLLLRAEGERHTLLLREARAADAGSYMATATNEL 800
801 GQATCAASLTVRPGGSTSPFSSPITSDEEYLSPPEEFPEPGETWPRTPTM 850
851 KPSPSQNRRSSDTGSKAPPTFKVSLMDQSVREGQDVIMSIRVQGEPKPVV 900
901 SWLRNRQPVRPDQRRFAEEAEGGLCRLRILAAERGDAGFYTCKAVNEYGA 950
951 RQCEARLEVRAHPESRSLAVLAPLQDVDVGAGEMALFECLVAGPTDVEVD 1000
1001 WLCRGRLLQPALLKCKMHFDGRKCKLLLTSVHEDDSGVYTCKLSTAKDEL 1050
1051 TCSARLTVRPSLAPLFTRLLEDVEVLEGRAARFDCKISGTPPPVVTWTHF 1100
1101 GCPMEESENLRLRQDGGLHSLHIAHVGSEDEGLYAVSAVNTHGQAHCSAQ 1150
1151 LYVEEPRTAASGPSSKLEKMPSIPEEPEQGELERLSIPDFLRPLQDLEVG 1200
1201 LAKEAMLECQVTGLPYPTISWFHNGHRIQSSDDRRMTQYRDVHRLVFPAV 1250
1251 GPQHAGVYKSVIANKLGKAACYAHLYVTDVVPGPPDGAPQVVAVTGRMVT 1300
1301 LTWNPPRSLDMAIDPDSLTYTVQHQVLGSDQWTALVTGLREPGWAATGLR 1350
1351 KGVQHIFRVLSTTVKSSSKPSPPSEPVQLLEHGPTLEEAPAMLDKPDIVY 1400
1401 VVEGQPASVTVTFNHVEAQVVWRSCRGALLEARAGVYELSQPDDDQYCLR 1450
1451 ICRVSRRDMGALTCTARNRHGTQTCSVTLELAEAPRFESIMEDVEVGAGE 1500
1501 TARFAVVVEGKPLPDIMWYKDEVLLTESSHVSFVYEENECSLVVLSTGAQ 1550
1551 DGGVYTCTAQNLAGEVSCKAELAVHSAQTAMEVEGVGEDEDHRGRRLSDF 1600
1601 YDIHQEIGRGAFSYLRRIVERSSGLEFAAKFIPSQAKPKASARREARLLA 1650
1651 RLQHDCVLYFHEAFERRRGLVIVTELCTEELLERIARKPTVCESEIRAYM 1700
1701 RQVLEGIHYLHQSHVLHLDVKPENLLVWDGAAGEQQVRICDFGNAQELTP 1750
1751 GEPQYCQYGTPEFVAPEIVNQSPVSGVTDIWPVGVVAFLCLTGISPFVGE 1800
1801 NDRTTLMNIRNYNVAFEETTFLSLSREARGFLIKVLVQDRLRPTAEETLE 1850
1851 HPWFKTQAKGAEVSTDHLKLFLSRRRWQRSQISYKCHLVLRPIPELLRAP 1900
1901 PERVWVTMPRRPPPSGGLSSSSDSEEEELEELPSVPRPLQPEFSGSRVSL 1950
1951 TDIPTEDEALGTPETGAATPMDWQEQGRAPSQDQEAPSPEALPSPGQEPA 2000
2001 AGASPRRGELRRGSSAESALPRAGPRELGRGLHKAASVELPQRRSPSPGA 2050
2051 TRLARGGLGEGEYAQRLQALRQRLLRGGPEDGKVSGLRGPLLESLGGRAR 2100
2101 DPRMARAASSEAAPHHQPPLENRGLQKSSSFSQGEAEPRGRHRRAGAPLE 2150
2151 IPVARLGARRLQESPSLSALSEAQPSSPARPSAPKPSTPKSAEPSATTPS 2200
2201 DAPQPPAPQPAQDKAPEPRPEPVRASKPAPPPQALQTLALPLTPYAQIIQ 2250
2251 SLQLSGHAQGPSQGPAAPPSEPKPHAAVFARVASPPPGAPEKRVPSAGGP 2300
2301 PVLAEKARVPTVPPRPGSSLSSSIENLESEAVFEAKFKRSRESPLSLGLR 2350
2351 LLSRSRSEERGPFRGAEEEDGIYRPSPAGTPLELVRRPERSRSVQDLRAV 2400
2401 GEPGLVRRLSLSLSQRLRRTPPAQRHPAWEARGGDGESSEGGSSARGSPV 2450
2451 LAMRRRLSFTLERLSSRLQRSGSSEDSGGASGRSTPLFGRLRRATSEGES 2500
2501 LRRLGLPHNQLAAQAGATTPSAESLGSEASATSGSSAPGESRSRLRWGFS 2550
2551 RPRKDKGLSPPNLSASVQEELGHQYVRSESDFPPVFHIKLKDQVLLEGEA 2600
2601 ATLLCLPAACPAPHISWMKDKKSLRSEPSVIIVSCKDGRQLLSIPRAGKR 2650
2651 HAGLYECSATNVLGSITSSCTVAVARVPGKLAPPEVPQTYQDTALVLWKP 2700
2701 GDSRAPCTYTLERRVDGESVWHPVSSGIPDCYYNVTHLPVGVTVRFRVAC 2750
2751 ANRAGQGPFSNSSEKVFVRGTQDSSAVPSAAHQEAPVTSRPARARPPDSP 2800
2801 TSLAPPLAPAAPTPPSVTVSPSSPPTPPSQALSSLKAVGPPPQTPPRRHR 2850
2851 GLQAARPAEPTLPSTHVTPSEPKPFVLDTGTPIPASTPQGVKPVSSSTPV 2900
2901 YVVTSFVSAPPAPEPPAPEPPPEPTKVTVQSLSPAKEVVSSPGSSPRSSP 2950
2951 RPEGTTLRQGPPQKPYTFLEEKARGRFGVVRACRENATGRTFVAKIVPYA 3000
3001 AEGKRRVLQEYEVLRTLHHERIMSLHEAYITPRYLVLIAESCGNRELLCG 3050
3051 LSDRFRYSEDDVATYMVQLLQGLDYLHGHHVLHLDIKPDNLLLAPDNALK 3100
3101 IVDFGSAQPYNPQALRPLGHRTGTLEFMAPEMVKGEPIGSATDIWGAGVL 3150
3151 TYIMLSGRSPFYEPDPQETEARIVGGRFDAFQLYPNTSQSATLFLRKVLS 3200
3201 VHPWSRPSLQDCLAHPWLQDAYLMKLRRQTLTFTTNRLKEFLGEQRRRRA 3250
3251 EAATRHKVLLRSYPGGP 3267
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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