 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P49793 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MFNKSFGTPFGGGTGGFGTTSTFGQNTGFGTTSGGAFGTSAFGSSNNTGG 50
51 LFGNSQTKPGGLFGTSSFSQPATSTSTGFGFGTSTGTSNSLFGTANTGTS 100
101 LFSSQNNAFAQNKPTGFGNFGTSTSSGGLFGTTNTTSNPFGNTSGSLFGP 150
151 SSFTAAPTGTTIKFNPPTGTDTMVKAGVSTNISTKHQCITAMKEYESKSL 200
201 EELRLEDYQANRKGPQNQVGAGTTTGLFGSSPATSSATGLFSSSTTNSAF 250
251 SYGQNKTAFGTSTTGFGTNPGGLFGQQNQQTTSLFSKPFGQATTTPNTGF 300
301 SFGNTSTLGQPSTNTMGLFGVTQASQPGGLFGTATNTSTGTAFGTGTGLF 350
351 GQPNTGFGAVGSTLFGNNKLTTFGTSTTSAPSFGTTSGGLFGNKPTLTLG 400
401 TNTNTSNFGFGTNNSGSSIFGSKPAAGTLGTGLGTGFGTALGAGQASLFG 450
451 NNQPKIGGPLGTGAFGAPGFNTSTAILGFGAPQAPVALTDPNASAAQQAV 500
501 LQQHLNSLTYSPFGDSPLFRNPMSDPKKKEERLKPTNPAAQKALTTPTHY 550
551 KLTPRPATRVRPKALQTTGTAKSHLFDGLDDDEPSLANGAFMPKKSIKKL 600
601 VLKNLNNSNLFSPVNHDSEDLASPSEYPENGERFSFLSKPVDENHQQDGD 650
651 DDSLVSRFYTNPIAKPIPQTPESAGNKNNSSSNVEDTFIALNMRAALRNG 700
701 LEGSSEETSFHDESLQDDRDEIENSAFQIHPAGIVLTKVGYYTIPSMDDL 750
751 AKITNEKGECIVSDFTIGRKGYGSIYFEGDVNLTNLNLDDIVHIRRKEVI 800
801 VYVDDNQKPPVGEGLNRKAEVTLDGVWPTDKTSRCLIKSPDRLADINYEG 850
851 RLEAVSRKQGAQFKEYRPETGSWVFKVSHFSKYGLQDSDEEEEEHPPKTT 900
901 SKKLKTAPLPPAGQATTFQMTLNGKPAPPPQSQSPEVEQLGRVVELDSDM 950
951 VDITQEPVPDSVLEESVPEDQEPVSASTQIASSLGINPHVLQIMKASLLV 1000
1001 DEEDVDAMEQRFGHFPSRGDTAQEICSPRLPISASHSSKSRSIVGGLLQS 1050
1051 KFASGTFLSPSASVQECRTPRTSSLMNVPSTSPWSVPLPLATVFTVPSPA 1100
1101 PEVPLKTVGIRRQPGLVPLEKSITYGKGKLLMDMALFMGRSFRVGWGPNW 1150
1151 TLANSGEQLHGSHELENHQVAESMEYGFLPNPVAVKSLSESPFKVHLEKL 1200
1201 GLRQRKLDEDLQLYQTPLELKLKHSTVHVDELCPLIVPNPGVSVIHGYAD 1250
1251 WVKKSPRDLLELPIVKHWSLTWTLCEALWGHLKELDSQLDEPSEYIQTLE 1300
1301 RRRAFSRWLSHTAAPQIEEEVSLTRRDSPIEAVFSYLTGSRISEACCLAQ 1350
1351 QSGDHRLALLLSQLVGSQSVRELLTMQLADWHQLQADSFIHDERLRIFAL 1400
1401 LAGKPVWQLSEQKQINVCSQLDWKRTLAIHLWYLLPPTASISRALSMYEE 1450
1451 AFQNTCEGDKYACPPLPSYLEGSGCVVEEEKDPQRPLQDVCFHLLKLYSD 1500
1501 RHYGLNQLLEPRSITADPLDYRLSWHLWEVLRALNYTHLSEQCEGVLQAS 1550
1551 YAGQLESEGLWEWAIFVFLHIDNSGMREKAVRELLTRHCQLSETPESWAK 1600
1601 ETFLTQKLCVPAEWIHEAKAVRAHMESNKHLEALYLFKAGHWNRCHKLVV 1650
1651 RHLASDAIINENYDYLKGFLEDLAPPERSSLIQDWETSGLVYLDYIRVIE 1700
1701 MLHRIQQVDCSGYELEHLHTKVTSLCNRIEQIPCYNAKDRLAQSDMAKRV 1750
1751 ANLLRVVLSLQHTPDATSNSTPDPQRVPLRLLAPHIGRLPMPEDYALEEL 1800
1801 RGLTQSYLRELTVGSQ 1816
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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