Sequence-based predictions HEXA

General Information

Prediction of disordered regions

• DISOPRED

Authors: Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT.

Year: 2004

Source: [Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.]

Description:

This method is based on a neuronal network which was trained on high resolution X-ray structures from PDB. Disordered regions are regions, which appears in the sequence record, but their electrons are missing from electronic density map. This approach can also failed, because missing electrons can also arise because of the cristallization process. The method runs first a PsiBlast search against a filtered sequence database. Next, a profile for each residue is calculated and classified by using the trained neuronal network.

Prediction:

As a prediction result you get a file with the predicted disordered region, the precision and recall. Furthermore you can a more detailed output. There you see the sequence, and the predictions and also numbers above the sequence (from 0 to 9 which shows you how likly your prediction is)

Input:

If you run disopred on the console, you have to define the location of your database. The program needs as input your sequence in a file with fasta format.

• POODLE

Prediction of order and disorder by machine-learning

Authors: S. Hirose, K. Shimizu, S. Kanai, Y. Kuroda and T. Noguchi

Year: 2007

There exist three different variants of POODLE.

The first variant is called POODLE-L which predicts mainly long disorder region with a length more than 40.

Source: [POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions.]

The next variant is called POODLE-S, which predicts mainly short disorder regions.

Source: [POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.]

The last variant is called POODLE-I, which integrates structal information predictors.

Source: [POODLE-I: Disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach]

There exists als another variant called POODLE-W, which compares different sequences and predicts which sequence is the most disordered one, but this method wasn't used in our analysis.

Description:

POODLE is also a machine learning based method. This method based on a 2-level SVM (Support Vector Machine).

We describe here the POODLE-L in detail, but all POODLE variants use the same principle. The method was trained on disordered proteins and proteins with no disoredered regions. On the first level, the SVM predicts the probability of a 40-residue sequence segment to be disordered. If the algorithm found such a disordered regions, the second level of the SVM use the output from the first level and predicts the probability to be disordered for each amino acid.

Output:

The result of this method is a file with the single amino acids, the prediction if it is ordered or not and the probability for the state. Furtheremore, you get a graphical view of the result.

Input:

We used the POODLE webserver for our analysis. We paste our sequence in fasta format in the input window and chose the POODLE variant.

Prediction of transmembrane helices and signal peptides

• TMHMM (transmembrane helices hidden markov model)

Authors: E. L.L. Sonnhammer, G. von Heijne, and A. Krogh
Year: 1998
Source: A hidden Markov model for predicting transmembrane helices in protein sequences.

Description:
TMHMM is a hidden markov model-based prediction methode for transmembrane helices in proteins. The HMM consists of three different main locations (core, cap, loop) and seven different states (cytoplasmic loop, cytoplasmic cap, helix core, non-cytoplasmic cap, short non-cytoplasmic loop, long non-cytoplasmic loop and globular domain).

Prediction:
This method search for a given protein sequence in FASTA-format the best path through the hidden markov model. There are two output possibilities, the short one and the long one. The long output format gives additional statistic information (i.e. expected numbers of amino acids in transmembrane helices).

Input:
The method only needs the protein sequence in FASTA-format for the prediction.

Prediction of disordered regions

Before we start to analyse the results of the different methods, we checked, if our protein has one or more disoredered regions. Therefore, we search our protein in the DisProt database and didn't found it, so our protein doesn't have disordered regions. Another possibility to find out if the protein has disordered regions, is only to check in the UniProt entry, if there is an entry for DisProt.

• Disopred

Disopred predicts two disordered regions in our protein. The first region is at the beginning of the protein (first two residues) and the second region is at the end (last three regions). This prediction is wrong, because it is normal, that the electrons from the first and the last amino acids lack in the electron density map. So, our protein Hexosamidase A has no disordered regions.

Result of the Disopred prediction. * shows that this amino acid belongs to a disordered regions, whereas . signs for a non-disordered region.

• POODLE

We decided to test several POODLE variants and to compare the results.

POODLE-I

POODLE-I predicted five disordered regions:

start position	end position	length
1	2	2
14	19	6
83	89	7
105	109	5
527	529	3

POODLE-L

POODLE-L found no disordered regions. Therefore, there is no disordered region with a length more than 40aa in our protein.

POODLE-S (High B-factor residues) This POODLE-S variant searches for high B-facto values in the crystallography, which implies uncertainty in the assignment of the atom positions.

POODLE-S predicted five disordered regions:

start position	end position	length
0	2	2
13	19	7
83	88	6
105	109	5
526	529	4

POODLE-S (missing residues)

POODLE-S (missing residues) predicts regions as disordered, if there is a amino acid in the sequence record, but not on the electron density map.

Poodle-S found 6 disordered regions.

start position	end position	length
17	18	2
53	61	9
78	109	33
153	153	1
280	280	1
345	345	1

Graphical Output:

Prediction of POODLE-S (High B-factor residues)

Prediction of POODLE-S (missing residues)

Prediction of POODLE-I

Prediction of POODLE-L

Comparison of the different POODLE variants: POODLE-L doesn't find any disordered regions. This is the result we expected, because our protein doesn't posses any disordered regions.

Both POODLE-S variants found several short disordered regions, which is a false result. Interesstingly, there seems to be more missing electrons in the electron density map, than residues with high B-factor value.

POODLE-I found the same result as POODLE-S with high B-factor, which was expected, because POODLE-I combines POODLE-L and POODLE-S (high B-factor).

Therefore, the predictions of short disordered regions are wrong results. Only the prediction of POODLE-L is correct.

In general, these predictions are used, if nothing else is available about the protein. Therefore, normally we don't know, that the prediction is wrong. Because of that, we want to trust the result and we want to check if the disordered regions overlap with the functionally important residues, because it seems that disordered regions are functionally very important. We check this for POODLE-S with missing residues and POODLE-I, because POODLE-S with high B-factor values show the same result als POODLE-I.

functional residues			disordered
residue position	amino acid	function	POODLE-S (missing)	POODLE-I
323	E	active site	ordered	ordered
115	N	Glycolysation	ordered	ordered
157	N	Glycolysation	ordered	ordered
259	N	Glycolysation	ordered	ordered
58 (connected with 104)	C	Disulfide bond	disordered	ordered
104 (connected with 58)	C	Disulfide bond	disordered	ordered
277 (connected with 328)	C	Disulfide bond	ordered	ordered
328 (connected with 277)	C	Disulfide bond	ordered	ordered
505 (connected with 522)	C	Disulfide bond	ordered	ordered
522 (connected with 505)	C	Disulfide bond	ordered	ordered

As you can see in the table above, only on disulfide bond is located in a disordered region, all other functionally important residues are located in ordered regions. This is a further good hint, that the predictions are wrong.

Prediction of transmembrane alpha-helices and signal peptides

Because most of the proteins we used in this practicum aren't membrane proteins, we got five additional proteins for the transmembrane and signal peptides analyses.

Additional proteins:

name	organism	location	transmembrane protein	sequence
BACR_HALSA	Halobacterium salinarium (Archaea)	Cell membrane	Multi-pass membrane protein	[P02945.fasta]
RET4_HUMAN	Human (Homo sapiens)	extracellular space	No	[P02753.fasta]
INSL5_HUMAN	Human (Homo sapiens)	extracellular region	No	[Q9Y5Q6.fasta]
LAMP1_HUMAN	Human (Homo sapiens)	Cell membrane	Single-pass membrane protein	[P11279.fasta]
A4_HUMAN	Human (Homo sapiens)	Cell membrane	Single-pass membrane protein	[P05067.fasta]

TMHMM

We analysed the six sequences with TMHMM.

• Hexosamidase A

TODO

• BACR_HALSA

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. We decided to compare the TMHMM prediction with the real occuring transmembrane helices in BACR_HALSA:

Especially in the beginning is the prediction really very good. 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 a different for the function if there are 6 or 7 helices, but in general the prediction of TMHMM was quite good.

• RET4_HUMAN

Prediction of TMHMM for the transmembrane helices of RET4_HUMAN

start position	end position	location
1	201	outside

TMHMM predicts no transmembrane helices. The whole protein is loacted in the extracellular space.

The TMHMM prediction is completly right. Therefore, you can see TMHMM can also predict, that a protein is not a transmembrane protein.

• INSL5_HUMAN

Prediction of TMHMM for the transmembrane helices of INSL5_HUMAN

start position	end position	location
1	135	outside

TMHMM predicts no transmembrane helices. The whole protein is loacted in the extracellular space.

The TMHMM prediction is again completly right.

• LAMP1_HUMAN

Prediction of TMHMM for the transmembrane helices of LAMP1_HUMAN

start position	end position	location
1	10	inside
11	33	TM Helix
34	383	outside
384	406	TM Helix
407	417	inside

TMHMM predicts two transmembrane helices, which are divided by a very long loop which is loacted at the extracellular space.

The prediction of TMHMM is quite good. Only at the beginning of the protein TMHMM predicts one wrong transmembrane helix, but the rest of the prediction is correct.

• A4_HUMAN

Prediction of TMHMM for the transmembrane helices of A4_HUMAN

start position	end position	location
1	700	outside
701	723	TM Helix
724	770	inside

TMHMM predicts on transmembrane helix at the end of the protein. As we already know is A4_HUMAN a single-spanning transmembrane protein and therefore the numbers of transmembrane helices is right predicted. As next step we compared the position of the transmembrane helix.

The result of the TMHMM prediction is pretty well. Except of the first residues at the beginning and the exact start position of the transmembrane helix, the prediction is correct.