From Bioinformatikpedia


First, we used GOPET to predict the GO terms.

Figure 1: Result of the GOPET prediction for BACR_HALSA

The method only predicts functional GO terms. BACR_HALSA has 3 annotated GO functions. The methods predicts also 3 GO function terms, which can be seen on Figure 1. Therefore, we decided to check if all predictions are correct.

GO term confidence prediction term prediction GOid
ion channel activity 77% right right
G-protein coupled photoreceptor activity 75% right wrong
hydrogen ion transmembrane transporter activity 60% wrong wrong

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We used the webserver for our analysis. We decided to only trust the significant Pfam-A matches. To check if the predictions are correct we mapped the Pfam ids to the Go ids with help of a mapping website [[1]]. If a successful mapping was not possible, we compared the names of the predicted Pfam family with the names of the GO terms. If the names are similar or equal, we decided to trust the mapping.

Graphical representation of the prediction result of Pfam:

Figure 2: Result of the Pfam prediction for BACR_HALSA

Pfam found one significant Pfam-A matches:

Family E-Value GOid prediction
Bacteriorhodopsin-like protein 2e-88 GO:0005216 right
GO:0006811 right
GO:0016020 right

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ProtFun 2.2

ProtFun 2.2 does not give clear predictions if the protein belongs to this class or not, instead it gives probabilities and odd scores. We decided to make a cutoff by 2. So all classes with an odd score of 2 or higher are results for us. You can also find a "=>" sign in the result file. This sign shows the result with the highest information content. We also take this line as result, although if the odd score is lower than 2. If we only have result with a odd score lower than 2, the line with this sign is our onliest result.
Because the prediction categories are very general, it was not possible to map the GOids. Therefore, we checked the known GO annotations. If there was a hint for a category and the protein was predicted to be in this category, we decided that the prediction is right, otherwise if the known GO annotations and the categories conflict, we count the prediction as wrong.

The ProtFun Server calculated following prediction result for BACR_HALSA:

Functional category
Functional category Probability Odd score Prediction
Amino acid biosynthesis 0.033 1.495 right
Biosynthesis of cofactors 0.186 2.589 wrong
Cell envelope 0.029 0.483 right
Cellular processes 0.051 0.698 right
Central intermediary metabolism 0.045 0.711 right
Engergy metabolism 0.138 1.537 right
Fatty acid metabolsim 0.016 1.265 right
Purines and Pyrimidines 0.302 1.244 right
Regulatory functions 0.013 0.080 wrong
Replication and transcription 0.019 0.073 right
Translation 0.059 1.339 right
Transport and binding 0.791 => 1.929 => right
Enzyme/non-enzyme Probability Odd score Prediction
Enzyme 0.199 0.696 right
Nonenzyme 0.801 => 1.122 => right
Enyzme class
Enzyme class Probability Odd score Prediction
Oxidoreductase (EC 1.-.-.-) 0.114 0.549 right
Transferase (EC 2.-.-.-) 0.031 0.091 right
Hydrolase (EC 3.-.-.-) 0.057 0.180 right
Lyase (EC 4.-.-.-) 0.020 0.430 right
Isomerase (EC 5.-.-.-) 0.010 0.321 right
Ligase (EC 6.-.-.-) 0.017 0.625 right
Gene ontology category
Gene ontology category Probability Odd score Prediction
Signal transducer 0.258 1.205 wrong
Receptor 0.355 2.087 right
Hormone 0.001 0.206 right
Structural protein 0.006 0.200 right
Transporter 0.440 => 4.036 => right
Ion channel 0.010 0.169 wrong
Volatge-gated ion channel 0.004 0.172 right
Cation channel 0.078 1.689 right
Transcription 0.026 0.205 right
Transcription regulation 0.028 0.226 right
Stress response 0.012 0.139 right
Immune response 0.011 0.128 right
Growth factor 0.010 0.727 right
Metal ion transport 0.049 0.106 right

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