Prediction of transmembrane alpha-helices and signal peptides HEXA HUMAN

TMHMM

First of all, we analysed the protein with TMHMM.

Figure 1: Prediction of TMHMM for the transmembrane helices of HEXA_HUMAN

start position	end position	location
1	529	outside

TMHMM predicts no transmembrane helix at all, which can be seen on Figure 1. The whole protein is located at the extracellular space. To evaluate this result, we compared the data from [UniProt] with our prediction.

Figure 2: Comparison between real occurring transmembrane helices and the TMHMM result.

As you can see above (Figure 2), the TMHMM prediction result is completely right, expect of the signal peptide, which can't be predicted by TMHMM.

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Phobius and PolyPhobius

Next, we used Phobius and also PolyPhobius to predict the transmembrane helices and also the signal peptide.

Figure 3: Prediction of Phobius for the transmembrane helices and signal peptides of HEXA_HUMAN

Figure 4: Prediction of PolyPhobius for the transmembrane helices and signal peptides of HEXA_HUMAN

Phobius			PolyPhobius
start position	end position	prediction	start position	end position	prediction
Signal peptide prediction
1	5	N-Region	1	5	N-Region
6	17	H-Region	6	15	H-Region
18	22	C-Region	16	19	C-Region
Summary signal peptide
1	22	Signal Peptide	1	19	Signal Peptide
Transmembrane helices prediction
23	529	outside	20	520	outside

Both methods do not predict a transmembrane helix (compare Figure 3 and Figure 4), which is correct, because HEXA_HUMAN is located at the lysosmal space. We compared the results of Phobius and PolyPhobius with the real protein.

Comparison with the real structure of the protein:

Figure 5: Comparison between the prediction of Phobius and the real protein

Figure 6: Comparison between the prediction of PolyPhobius and the real protein

The prediction of Phobius (Figure 5) is a little bit better than the PolyPhobius prediction (Figure 6), because Phobius predicts the beginning and the end of the signal peptide totally correct, whereas PolyPhobius cuts two residues of the signal peptide.

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OCTOPUS and SPOCTOPUS

As next tools we used OCTOPUS and SPOCTOPUS to predict the transmembrane helices and the signal peptide again.

Figure 7: Prediction of OCTOPUS for the transmembrane helices of HEXA_HUMAN

Figure 8: Prediction of SPOCTOPUS for the transmembrane helices of HEXA_HUMAN

OCTOPUS			SPOCTOPUS
start position	end position	prediction	start position	end position	prediction
1	2	inside	1	6	N-terminal of a signal peptide
3	23	TM helix	7	21	signal peptide
24	529	outside	22	529	outside

The results of these two predictions differ. OCTOPUS predicts a transmembrane helix (Figure 7), whereas SPOCTOPUS predicts at the same location a signal peptide (Figure 8).
To check which method predicted right, we compared the protein and the prediction.

Comparison with the real structure of the protein:

Figure 9: Comparison between the prediction of OCTOPUS and the real protein

Figure 10: Comparison between the prediction of SPOCTOPUS and the real protein

SPOCTOPUS gave us the better result (Figure 10), because SPOCTOPUS recognizes the signal peptide, whereas OCTOPUS predicts a transmembrane helix instead (Figure 9).

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TargetP

All of our proteins are proteins from human and archaea, so therefore we only use the non-plant option of TargetP.

Location	Probability
mitochondrial targeting SP	0.214
secretory pathway SP	0.877
other	0.009

TargetP predicts a secretory pathway signal peptide for this protein, which is correct.



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SignalP

For our analysis we used the Hidden Markov Model based and also the neuronal network based prediction.
The prediction with the Hidden Markov Model used three different scores. The S-score which is the score for the signal peptide, the C-score which is the score for the cleavage site and the Y-score which is a combination of the S-score and the C-score and is used to predict the cleavage site, because the Y-score is more precise than the C-score.

Result of the neuronal network

Signal peptide		Clevage site
start position	end position	start position	end position	prediction
1	22	22	23	signal peptide

Result of the Hidden Markov Model

prediction	signal peptide probability	signal anchor probability	cleavage site start	cleavage site end
signal peptide	1.000	0.000	22	23

Figure 11: Result of the SignalP method based on the neuronal network

Figure 12: Result of the SignalP method based on the hidden markov model

Both methods (Figure 11 and Figure 12) predict the same start and end position of the cleavage site and also both methods predict a signal peptide, which is correct because HEXA_HUMAN takes part at the secretory pathway.

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