Prediction of transmembrane alpha-helices and signal peptides BACR HALSA

TMHMM

First of all, we used TMHMM to predict the transmembrane helices in this protein.

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

start position	end position	location
1	22	outside
23	42	TM Helix
43	54	inside
55	77	TM Helix
78	91	outside
92	114	TM Helix
115	120	inside
121	143	TM Helix
144	147	outside
148	170	TM Helix
171	189	inside
190	212	TM Helix
213	262	outside

TMHMM predicts six transmembrane helices for BACR_HALSA, which can be seen on Figure 1. We decided to compare the TMHMM prediction with the real occurring transmembrane helices in BACR_HALSA:

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

Especially at the beginning is the prediction very good, which can be seen on Figure 2. There is almost 100% overlap between predicted and real helices. Only in the end of the protein lacks one transmembrane helix in the TMHMM prediction. Therefore, in real there are 7 transmembrane helices, whereas TMHMM only predicts 6. This is really bad, because it is different for the function if there are 6 or 7 helices, but in general the prediction of TMHMM was quite good.

Phobius and PolyPhobius

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

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

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

Phobius			PolyPhobius
start position	end position	prediction	start position	end position	prediction
Signal peptide prediction
No prediction available
Transmembrane helices prediction
23	42	TM helix	22	43	TM helix
43	53	inside	44	54	inside
54	76	TM helix	55	77	TM helix
77	95	outside	78	94	outside
96	114	TM helix	95	114	TM helix
115	120	inside	115	120	inside
121	142	TM helix	121	141	TM helix
143	147	outside	142	147	outside
148	169	TM helix	148	166	TM helix
170	189	inside	167	186	inside
190	212	TM helix	187	205	TM helix
213	217	outside	206	215	outside
218	237	TM helix	216	237	TM helix
238	262	inside	238	262	inside

Both methods do not predict a signal peptide (compare Figure 3 and Figure 4), but both recognize, that this protein is a transmembrane protein with seven helices. The predictions only differ at the beginning and the end of the helix positions, but the differences between these two predictions is only about 1 to 3 residues.

To evaluate the predictions, we compared the predictions with the real occurring transmembrane helices (Figure 5 and Figure 6).

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

OCTOPUS and SPOCTOPUS

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

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

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

OCTOPUS			SPOCTOPUS
start position	end position	prediction	start position	end position	prediction
1	22	outside	1	22	outside
23	43	TM helix	23	43	TM helix
44	54	inside	44	54	inside
55	75	TM helix	55	75	TM helix
76	95	outside	76	95	outside
96	116	TM helix	96	116	TM helix
117	121	inside	117	120	inside
122	142	TM helix	121	141	TM helix
143	147	outside	142	147	outside
148	168	TM helix	148	168	TM helix
169	185	inside	169	185	inside
186	206	TM helix	186	206	TM helix
207	216	outside	207	216	outside
217	237	TM helix	217	237	TM helix
238	262	inside	238	262	inside

Both methods have a very similar result (compare Figure 7 and Figure 8), which is identical with the exception of some residues. Both predicted the seven transmembrane helices, which is a very good result.

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

Next, we compared the prediction of these two methods with the real structure of the protein. As we can see in Figure 9 and Figure 10, the prediction and the real structure agree most of the time.

TargetP

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

Location	Probability
mitochondiral targeting SP	0.019
secretory pathway SP	0.897
other	0.562

TargetP predicts that this protein contains a secretory pathway signal peptide. The probability for this signal peptide is very high, although the result is wrong, because BACR_HALSA is a transmembrane protein.

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.

BACR_HALSA is an archaea protein. SignalP gave the possibility to predict eukaryotic or bacteria (gram-positive and gram-negative) signal peptides. Therefore, we decided to use all three possible prediction methods and to compare the results with the real signal peptide.

eukaryotes

Result of the neuronal network

Signal peptide		Clevage site
start position	end position	start position	end position	prediction
1	38	38	39	signal peptide

Result of the hidden markov model

prediction	signal peptide probability	signal anchor probability	cleavage site start	cleavage site end
signal peptide	0.017	0.859	15	16

Figure 11: Result of the SignalP method based on the neuronal network for BACR_HALSA with the prediction method for eukaryotes

Figure 12: Result of the SignalP method based on the hidden markov model for BACR_HALSA with the prediction method for eukaryotes

gram-negative bacteria

Result of the neuronal network

Signal peptide		Clevage site
start position	end position	start position	end position	prediction
1	42	42	43	no signal peptide

Result of the hidden markov model

prediction	signal peptide probability	signal anchor probability	cleavage site start	cleavage site end
Non-secretory protein	0.000	0.000

Figure 13: Result of the SignalP method based on the neuronal network for BACR_HALSA with the prediction method for gram-negative bacteria

Figure 14: Result of the SignalP method based on the hidden markov model for BACR_HALSA with the prediction method for gram-negative bacteria

gram-positive bacteria

Result of the neuronal network

Signal peptide		Clevage site
start position	end position	start position	end position	prediction
1	33	33	34	no signal peptide

Result of the hidden markov model

prediction	signal peptide probability	signal anchor probability	cleavage site start	cleavage site end
Non-secretoy protein	0.000	0.000

Figure 15: Result of the SignalP method based on the neuronal network for BACR_HALSA with the prediction method for gram-positive bacteria

Figure 16: Result of the SignalP method based on the Hidden Markov Model for BACR_HALSA with the prediction method for gram-positive bacteria

Only the eukaryotic prediction method predicts a signal peptide (Figure 11 and Figure 12), whereas the both methods (Figure 13 – Figure 16) for bacteria predict, that this protein has no signal peptide. Otherwise, only the eukaryotic prediction method predict the protein as a signal anchor, which is correct, because BACR_HALSA is a transmembrane protein. Therefore, it seemed, that the eukaryotic prediction method suited better for BACR_HALSA