 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O13967 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MSWHEQDYGFGTSSENSKINSDEFEDSMDVTEFNNPGEESTYPQANSWND 50
51 SNKTKDIAEYYDYSWSGQREANQQIPQPVPVQPRYPDEQNFSMNGETYPS 100
101 EAYDYDYSGQEEAINAMNILQGNLERSNEYYSEGVPYSEEDLGAYAGGMT 150
151 EDDQMYSNFNETGEFNLDIGSSKFSYLNAKNPYPAWIAENSIPITAENIL 200
201 EIFQELQAKFGFQYDSMLNMYDFFMVLLDSRSSRMDAENALKSLHADYIG 250
251 GRNANYRKWYFSSSMDIDDSVGLQNCKFSSNGPKIKQAKKKQKRKSNKAE 300
301 TEGTNEPETSVQIDPLNVSMENWENEMKNLDCETQVRQLALYLLCWGEAN 350
351 NIRFCPECLCFIFKLANDFMQSEDYAKSEPIEDDCFYLDNVITPLYEFIR 400
401 DQQFELLDGKLVRRERDHAQIIGYDDINQLFWYPEGIARIVTVDGTQLIT 450
451 LPKWERFHKLSEVDWKKAFYKTFYESRSWFHLVTNFNRIWVIHFTTYWYY 500
501 TVFNSPTIIEKNFRQSVGPKPIPSCHWTSVSLGGAVATLLMLLATIFEWI 550
551 HVPRKFPGSRPLLKRFLILILFFILNVAPTVFVFGFSTEEQQRTTGRLTV 600
601 AIVHFIFSVFTFIYFSLVPLNNLFHRAYKSSSRTHLANRYFTADYARLQI 650
651 NDMCVSWGLWLLVFGAKFTESYFFLSLSFRDPILVLSTMKPYLCNITFLG 700
701 SHLCIWQPKILLGIMYVTDLVLFFLDTYLWYILVNTVFSVARSFFLGISI 750
751 WTPWRNIFARMPKRIYSKILCTPEVDSSYKPKVLVSQIWNSIIISLYREH 800
801 LLAIEHVQRLIYHQVNSLDGDGSKTLKTPTFFVSQEDSSFNTEYFPAHSE 850
851 AERRLSFFAQSLATPIPEPIPVDAMPTFTVLVPHYGEKILLSLKEIIREQ 900
901 DKLSRVTLLEYLKQLHANEWKCFVRDTKILAEEDALSNQDLNSQDESMKA 950
951 EQLHKKFDDLPFYCIGFKNATPEYTLRTRIWASLRSQTLYRTVSGFMNYS 1000
1001 RAIKLLYRVENPDVAQLFEGQMDVLEYELDRMASRKFKMCVSMQRYAKFT 1050
1051 ADEIENTEFILRAYPDLLIAYLDEDPPKEGETTPQLYAALIDGYSELDEN 1100
1101 KKRKPKYRIKLSGNPILGDGKSDNQNLSLPFYRGEYIQLIDANQDNYLEE 1150
1151 CLKIRSILAEFEAFDLKTNDPYAETNALYQNNPVAIMGAREYIFSENIGI 1200
1201 LGDVAAGKEQTFGTLFARTMAQIGGKLHYGHPDFLNAIYMTTRGGVSKAQ 1250
1251 KGLHVNEDIYAGMTALQRGGRIKHCEYYQCGKGRDLGFGSILNFTTKIGT 1300
1301 GMGEQMVSREYYYLGTQLPFDRFLSFYYAHPGFHINNIFIMLSVQLFMVV 1350
1351 LVNLGGMYHVVTVCDYDHDQKLTVPMRPEGCYQLNPVVNWLKRCIISIFI 1400
1401 VFFISFVPLTVQELTERGAWRALTRLGKHFASFSPMFEVFACQTYAQSVI 1450
1451 ANLSFGGARYIGTGRGFATARLSFSLLFSRFAGPSIYLGSRTLLMLLFGT 1500
1501 MTVWIPHLIYFWISTLAMCISPFIFNPHQFSWTDFFVDYREFIRWLSRGN 1550
1551 SRSHINSWIGYCRLTRTRITGYKRRLLGVPVSKGVIDTSRAHFTNMFFTE 1600
1601 IFIPLMLVPLTLVSYFFIDSQPGNPDPSKVTNPILRILILAFLPIIVAAV 1650
1651 VSMTFAGMACMMGPLLDLCCKKFGAVLAALAHGITVFMFIIVFEVSWYLE 1700
1701 AWCLAKTVLSMLCIIAIQRFFFKIIQVLFLTRELKHDGTNLAWWTGKWYS 1750
1751 RGLGFHALSQPSRELICKMTELNFFAADFFLCHLLLFLMLPVLLIPFIDR 1800
1801 WHSMMLFWLKPSKQIRPPIYSMRQNRLRKKIVQRYGTMFFLLLIAFLALI 1850
1851 IIPLVVAKDLLANFEMDILRTYGLMQPRDTNWTENGTNWTTVNE 1894
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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