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First, we tried to predict the GO terms with GOPET.

Figure 1: Result of the GOPET prediction for HEXA_HUMAN

The method only predicts functional GO terms. HEXA_HUMAN has 8 annotated GO functions. The methods predicts also 8 GO function terms, which can be seen on Figure 1. Therefore, we decided to check if all predictions are correct. We checked if the general term is correct and also if the GO number is correct.

GO term confidence prediction term prediction GOid
hexosamidase activity 97% right wrong
beta-N-acetylhexosamidase activity 96% right right
hydrolase activity 96% right right
hydrolase activity acting on glycosyl bonds 96% right right
hydrolase activity hydrolyzing O-glycosyl compounds 96% right right
catalytic activity 96% right right
hydrolase activity hydrolyzing N-glycosyl compounds 78% wrong wrong
protein heterodimerization activity 61% right right

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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 HEXA_HUMAN

Pfam found two significant Pfam-A matches:

Family E-Value GO id prediction
Glycosyl hydrolase family 20, domain 2 3.7e-43 GO:0004553 right
Glycosyl hydrolase family 20, catalytic domain 1.8e-84 GO:0005975 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 HEXA_HUMAN:

Functional category
Functional category Probability Odd score Prediction
Amino acid biosynthesis 0.161 7.331 wrong
Biosynthesis of cofactors 0.332 4.609 right
Cell envelope 0.804 => 13.186 => right
Cellular processes 0.110 1.506 right
Central intermediary metabolism 0.432 6.856 right
Engergy metabolism 0.113 1.259 right
Fatty acid metabolsim 0.019 1.427 right
Purines and Pyrimidines 0.519 2.136 wrong
Regulatory functions 0.018 0.111 right
Replication and transcription 0.073 0.271 right
Translation 0.040 0.904 right
Transport and binding 0.685 1.670 right
Enzyme/non-enzyme Probability Odd score Prediction
Enzyme 0.792 => 2.764 => right
Nonenzyme 0.208 0.292 right
Enyzme class
Enzyme class Probability Odd score Prediction
Oxidoreductase (EC 1.-.-.-) 0.143 0.685 right
Transferase (EC 2.-.-.-) 0.201 0.582 right
Hydrolase (EC 3.-.-.-) 0.329 1.039 wrong
Lyase (EC 4.-.-.-) 0.054 1.143 right
Isomerase (EC 5.-.-.-) 0.027 0.856 right
Ligase (EC 6.-.-.-) 0.085 => 1.661 => right
Gene ontology category
Gene ontology category Probability Odd score Prediction
Signal transducer 0.083 0.389 right
Receptor 0.105 0.617 right
Hormone 0.001 0.206 right
Structural protein 0.010 0.357 right
Transporter 0.024 0.222 right
Ion channel 0.018 0.310 right
Volatge-gated ion channel 0.002 0.082 right
Cation channel 0.010 0.218 right
Transcription 0.058 0.453 right
Transcription regulation 0.026 0.205 right
Stress response 0.004 0.500 right
Immune response 0.014 0.167 right
Growth factor 0.005 0.372 right
Metal ion transport 0.009 0.020 right

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