 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q06518 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MACPWKFLFRVKSYQGDLKEEKDINNNVEKTPGAIPSPTTQDDPKSHKHQ 50
51 NGFPQFLTGTAQNVPESLDKLHVTPSTRPQHVRIKNWGNGEIFHDTLHHK 100
101 ATSDISCKSKLCMGSIMNSKSLTRGPRDKPTPVEELLPQAIEFINQYYGS 150
151 FKEAKIEEHLARLEAVTKEIETTGTYQLTLDELIFATKMAWRNAPRCIGR 200
201 IQWSNLQVFDARSCSTASEMFQHICRHILYATNSGNIRSAITVFPQRSDG 250
251 KHDFRIWNSQLIRYAGYQMPDGTIRGDPATLEFTQLCIDLGWKPRYGRFD 300
301 VLPLVLQAHGQDPEVFEIPPDLVLEVTMEHPKYEWFQELGLKWYALPAVA 350
351 NMLLEVGGLEFPACPFNGWYMGTEIGVRDFCDTQRYNILEEVGRRMGLET 400
401 HTLASLWKDRAVTEINAAVLHSFQKQNVTIMDHHTASESFMKHMQNEYRA 450
451 RGGCPADWIWLVPPVSGSITPVFHQEMLNYVLSPFYYYQIEPWKTHIWQD 500
501 EKLRPRRREIRFTVLVKAVFFASVLMRKVMASRVRATVLFATETGKSEAL 550
551 ARDLAALFSYAFNTKVVCMEQYKANTLEEEQLLLVVTSTFGNGDCPSNGQ 600
601 TLKKSLFMMKELGHTFRYAVFGLGSSMYPQFCAFAHDIDQKLSHLGASQL 650
651 APTGEGDELSGQEDAFRSWAVQTFRAACETFDVRSKHCIQIPKRYTSNAT 700
701 WEPEQYKLTQSPESLDLNKALSSIHAKNVFTMRLKSLQNLQSEKSSRTTL 750
751 LVQLTFEGSRGPSYLPGEHLGIFPGNQTALVQGILERVVDCSSPDQTVCL 800
801 EVLDESGSYWVKDKRLPPCSLRQALTYFLDITTPPTQLQLHKLARFATEE 850
851 THRQRLEALCQPSEYNDWKFSNNPTFLEVLEEFPSLRVPAAFLLSQLPIL 900
901 KPRYYSISSSQDHTPSEVHLTVAVVTYRTRDGQGPLHHGVCSTWINNLKP 950
951 EDPVPCFVRSVSGFQLPEDPSQPCILIGPGTGIAPFRSFWQQRLHDSQHR 1000
1001 GLKGGRMTLVFGCRHPEEDHLYQEEMQEMVRKGVLFQVHTGYSRLPGKPK 1050
1051 VYVQDILQKELADEVFSVLHGEQGHLYVCGDVRMARDVATTLKKLVAAKL 1100
1101 NLSEEQVEDYFFQLKSQKRYHEDIFGAVFSYGAKKGNTLEEPKGTRL 1147
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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