 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P70700 from www.uniprot.org...
The NucPred score for your sequence is 0.17 (see score help below)
1 MDVDGRWRNLPSGPSLKHLTDPSYGIPPEQQKAALQDLTRAHVDSFNYAA 50
51 LEGLSHAVQAIPPFEFAFKDERISLTIVDAVISPPSVPKGTICKDLNVYP 100
101 AECRGRKSTYRGRLTADISWAVNGVPKGIIKQFLGYVPIMVKSKLCNLYN 150
151 LPPRVLIEHHEEAEEMGGYFIINGIEKVIRMLIVPRRNFPIAMVRPKWKS 200
201 RGLGYTQFGVSMRCVREEHSAVNMNLHYVENGTVMLNFIYRKELFFLPLG 250
251 FALKALVSFSDYQIFQELIKGKEEDSFFRNSVSQMLRIVIEEGCHSQKQV 300
301 LNYLGECFRVKLSLPDWYPNVEAAEFLLNQCICIHLQSNTDKFYLLCLMT 350
351 RKLFALARGECMDDNPDSLVNQEVLSPGQLFLMFLKEKMENWLVSIKIVL 400
401 DKRAQKANVSINNENLMKIFSMGTELTRPFEYLLATGNLRSKTGLGFLQD 450
451 SGLCVVADKLNFLRYLSHFRCVHRGAAFAKMRTTTVRRLLPESWGFLCPV 500
501 HTPDGAPCGLLNHLTAVCEVVTKFVYTASIPALLCGLGVTPVDTAPCRPY 550
551 SDCYPVLLDGVMVGWVDKDLAPEVADTLRRFKVLREKRIPPWMEVALIPM 600
601 TGKPSLYPGLFLFTTPCRLVRPVQNLELGREELIGTMEQLFMNVAIFEDE 650
651 VFGGISTHQELFPHSLLSVIANFIPFSDHNQSPRNMYQCQMGKQTMGFPL 700
701 LTYQNRSDNKLYRLQTPQSPLVRPCMYDFYDMDNYPIGTNAIVAVISYTG 750
751 YDMEDAMIVNKASWERGFAHGSVYKSEFIDLSEKFKQGEDNLVFGVKPGD 800
801 PRVMQKLDDDGLPFIGAKLEYGDPYYSYLNLNTGEGFVVYYKSKENCVVD 850
851 NIKVCSNDMGSGKFKCICITVRIPRNPTIGDKFASRHGQKGILSRLWPAE 900
901 DMPFTESGMMPDILFNPHGFPSRMTIGMLIESMAGKSAALHGLCHDATPF 950
951 IFSEENSALEYFGEMLKAAGYNFYGTERLYSGISGMELEADIFIGVVYYQ 1000
1001 RLRHMVSDKFQVRTTGARDKVTNQPLGGRNVQGGIRFGEMERDALLAHGT 1050
1051 SFLLHDRLFNCSDRSVAHMCVECGSLLSPLLEKPPPSWSAMRNRKYNCTV 1100
1101 CGRSDTIDTVSVPYVFRYFVAELAAMNIKVKLDVI 1135
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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