 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5PSV9 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MESTQVIDWDAEEEEETELSSGSLGYSVEPIGQLRLFSGTHGPERDFPLY 50
51 LGKNVVGRSPDCSVALPFPSISKQHAVIEISAWNKAPILQDCGSLNGTQI 100
101 VKPPRVLPPGVSHRLRDQELILFADFPCQYHRLDVPPPLVPRSLLTIEKT 150
151 PRIRIESQNSRVLLAADSEEEGDFPSGRCVANGQRNTASPSATVVPESDE 200
201 EVSSPAPSVPGPSSPFGLGSDTDEEQGQQPGVEESSLADSSGAAGEAEQP 250
251 EANGTTAGIQAQPTEHKLKDTKVKKEAGRAGVSDGSVLERSPTLGEDSDT 300
301 EVDEDHKPGFADSETDVEEERIPVTPPVAPVKKNQVLLAVGIGDPEAPGV 350
351 AHLQDCLAGSGTDVEDKTALDVPLERNHTPMVINSDTDEEEEEEEEVSAA 400
401 LTLAHLKERGIGLWSRDPGAEEVKSQPQVLVEQSQSASGRDSDTDVEEES 450
451 SGRKREIIPDSPMDVDEALTVTQPESQPPRRPNDADEYMDMSSPGSHLVV 500
501 NQASFAVVGKTRAQVEEEVPGPSVILGEKHQVPLEGAQPPEEAWETAVQE 550
551 GSSSPEAAASVRPSQQPVAEDAGTECATAVSEQESTLEVRSQSGSPAAPV 600
601 EQVVIHTDTSGDPTLPQREGAQTPTGREREAHVGRTKSAKECCDAEPEDL 650
651 CLPATQCFVEGESQHPEAVQSLENEPTQLFPCTLPQEPGPSHLSLQTPGA 700
701 DTLDVPWEVLATQPFCLREQSETSELHEAHGSQPSLPREPPGHQHLVHTS 750
751 PVHTELLRIEGREIQTVEKAMGIPKEMADRMTPEREPLEREIRGRTENSE 800
801 RDVIGEELIQGTKDREPKKVLARDSQRKEADKDLEGNRESLEVEIEMSKD 850
851 SQKRERKVEKPEPKREWEPADLEVTPDRGVTEEGSHDQKGQIASLTLKPG 900
901 VGVKDLEGLASAPIITGSQADGGKGDPLSPGRQQRGRLSCQTTPAGKASR 950
951 GDPEPPDHCLFSSVPEASTQSLLTSQSQKQSTPQPLFSTSSSEIPLPESL 1000
1001 HTKPNVRPRRSSRMTPSPHSSAALKPNTTCPTNQPAASRPTSRPTRGRAN 1050
1051 RSSTRTPELIVPVDPELQPSTSTEQPVIPKLTSQVTEGRVQMPEPLLTGP 1100
1101 EIQSPTSTEQSVTPDRKPRATRGRPSKSPNKTPEPLISTGPELQPPTSIE 1150
1151 QPVIPKPTSRVTRGRPRKSSVRTPESVVSTGPELQPLTSIEQPVIPEPRA 1200
1201 TRGRPSKSSIKTPESVVPTGPELQPLTSAKQPVTPNLTSRASRGRSSKSI 1250
1251 RTPEPVVQTGPEFHPSTSTEQPDTREPSSQARTRRSAVKTPEASVPTTPE 1300
1301 LQPFTSKKQPAPKPTALVTQGRTYKPSTEDCESVGPVAPDFEPSTSTDHL 1350
1351 VTPKVTDQSLTLQSSPLSASPVSSTPDLKPPVPIAQPVTPEPIPQANHQR 1400
1401 KRRAAGKQGSRTVPLGHKSYSALSEPEPQSSASQSSGASEADSPRQKRPR 1450
1451 RQASQKTVVIKEEPVETEVKEEPQETAIPTPEKRKRDHAEEVTQGKPTRS 1500
1501 RRTKPNQETAPKVLFTGVMDSRGERAVLALGGSLASSVNEASHLVTDRIR 1550
1551 RTVKFLCALGKGIPILSLNWLYQSRKAGCFLPPDDYLVTDPEQEKNFSFS 1600
1601 LRDSLCRARERRLLEDYEIHVTPGVQPPPPQMGEIISCCGGTFLPSMPHS 1650
1651 YKLHRVIITCTEDLPRCAIPSRLGLPLLSPEFLLTGVLKQEATPEAFVLS 1700
1701 NLEMSST 1707
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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