Difference between revisions of "Sequence-based predictions HEXA"

Revision as of 16:58, 11 August 2011

General Information

Secondary Structure Prediction

To analyse the secondary structure of our protein we used different methods. In our analysis we used PSIPRED, Jpred3 and DSSP. In the analysis section of this page we want to compare these three methods to see if the methods gave similar results or if they differ extremely.

[Here] you can find some general information about these methods.

Prediction of disordered regions

After analysing the secondary structure, we also want to have a look at disordered regions in this protein. Therefore, we used different methods. We used DISOPRED, POODLE in several variations, IUPred and Meta-Disorder. As before, with the the secondary structure prediction methods we want to compare the different methods and variants, if the predictions are similar. Therefore, we also want to decided which methods seems to be the best one for our purpose.

To get more insight in the methods and the theory behind them we also offer you an [general information page].

Prediction of transmembrane helices and signal peptides

The third big analysis section is the prediction of transmembrane helices and signal peptides. We merged the prediction of transmembrane helices and signal peptides in one section, because there are several prediction methods which can predict both and therefore we looked at both predictions in this section.

Therefore we used several methods, some which only predict transmembrane helices, some which only predict signal peptides and some combined methods.

To have a closer look at the different methods we again provide an [information page.]

Prediction of GO Terms

The last section is about the analysis of GO Terms. As before, we used several methods and compared them to each other.

Again we also provide an [general information page] about the GO Term methods, we used in our analysis.

Secondary Structure prediction

Results

The detailed output of the different prediction methods can be found [here]

Here we only present a short summary of the output of the different methods.

Predicted Helices

method	#helices
PSIPRED	14
Jpred3	14
DSSP	16

Predicted Beta-Sheets

method	#sheets
PSIPRED	15
Jpred3	15
DSSP	0

Comparison of the different methods

To determine how succesful our secondary structure prediction with PSIPRED and Jpred are, we had to compare it with the secondary structure assignment of DSSP. First of all, DSSP assigns no beta-sheets whereas both prediction methods predict some beta-sheets. Therefore the main comparison in this case refers to the alpha-helices.

For PSIPRED the prediction of the alpha-helices was good. In the most cases the alpha-helices of DSSP und PSIPRED corrspond. There is only one helix which is predicted by PSIPRED which is not assigned as helix by DSSP. Furthermore there are three helices which are allocated as helices by DSSP which were not predicted by PSIPRED. The most of these helices which were presented only in one output are very small ones.

For Jpred3 the prediction of the alpha-helices was sufficiently good. In the most cases it agrees with DSSP. There are only two helices which are predicted by Jpred and which are not also assigned by DSSP. In contrary there are three small helics which are allocated to an alpha-helices by DSSP but are not predicted by Jpred. There is another special case where DSSP assignes two helices which are separated by a turn and Jpred predicts there only one big helix.

All in all, the prediction of the helices is probably good because they correspond mostly with the assignmet of DSSP. The only negative aspect is, that both prediction methods predict a lot of sheets which were not assigned by DSSP at all.

Prediction of disordered regions

Before we start with the analysis of 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 any disordered regions. Another possibility to find out if the protein has disordered regions, is to check in UniProt, if there is an entry for DisProt.

Results

The detailed results of the different methods can be found [here]

In this section, we only want to give a summary of the output of the different methods.

method	#disordered regions in the protein	#disordered regions on the brink
Disopred	0	2
POODLE-I	3	2
POODLE-L	0	0
POODLE-S (B-factors)	3	2
POODLE-S (missing residues)	4	2
IUPred (short)	0	2
IUPred (long)	0	0
IUPred (structural information)	0	0
Meta-Disorder	0	0

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 positive 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 is known 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 shows the same result as 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 one 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.

Comparison of the different methods

We decided to compare the results of the different methods. Therefore, we count how many residues are predicted as disordered, which is wrong in our case.

	methods
	Disopred	POODLE-I	POODLE-L	POODLE-S (missing)	POODLE-S (B-factor)	IUPred (short)	IUPred (long)	IUPred (structure)	Meta-Disorder
#wrong predicted residues	5	23	0	47	24	3	0	0	0

POODLE-L, IUPred(long) and IUPred(structure) predict the disordered regions correct. The baddest prediction result gave POODLE-S (B-factor) which predicts 47 residues as disordered, followed by POODLE-S (missing) (24 wrong predicted residues) and POODLE-I (23 wrong predicted residues).

Prediction of transmembrane alpha-helices and signal peptides

Because most of the proteins we used in this practical are not membrane proteins, we got five additional proteins for the transmembrane and signal peptide 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]

The detailed output for the different organism and the different prediction methods can be found here:

Results

Transmembrane Helices

	TMHMM			Phobius			PolyPhobius			OCTOPUS			SPOCTOPUS
protein	start position	end position	location	start position	end position	location	start position	end position	location	start position	end position	location	start position	end position	location
HEXA HUMAN	1	529	outside	23	529	outside	20	520	outside	1	2	inside
										3	23	TM helix
										24	529	outside	22	529	outside
BACR HALSA	1	22	outside							1	22	outside	1	22	outside
	23	42	TM Helix	23	42	TM helix	22	43	TM helix	23	43	TM helix	23	43	TM helix
	43	54	inside	43	53	inside	44	54	inside	44	54	inside	44	54	inside
	55	77	TM Helix	54	76	TM helix	55	77	TM helix	55	75	TM helix	55	75	TM helix
	78	91	outside	77	95	outside	78	94	outside	76	95	outside	76	95	outside
	92	114	TM Helix	96	114	TM helix	95	114	TM helix	96	116	TM helix	96	116	TM helix
	115	120	inside	115	120	inside	115	120	inside	117	121	inside	117	120	inside
	121	143	TM Helix	121	142	TM helix	121	141	TM helix	122	142	TM helix	121	141	TM helix
	144	147	outside	143	147	outside	142	147	outside	143	147	outside	142	147	outside
	148	170	TM Helix	148	169	TM helix	148	166	TM helix	148	168	TM helix	148	168	TM helix
	171	189	inside	170	189	inside	167	186	inside	169	185	inside	169	185	inside
	190	212	TM Helix	190	212	TM helix	187	205	TM helix	186	206	TM helix	186	206	TM helix
	213	262	outside	213	217	outside	206	215	outside	207	216	outside	207	216	outside
				218	237	TM helix	216	237	TM helix	217	237	TM helix	217	237	TM helix
				238	262	inside	238	262	inside	238	262	inside	238	262	inside
RET4 HUMAN										1	1	inside
										2	23	TM helix
	1	201	outside	19	201	outside	19	201	outside	24	201	outside	20	201	outside
INSL5 HUMAN										1	1	inside
										2	32	TM helix
	1	135	outside	23	135	outside	23	135	outside	33	135	outside	24	135	outside
LAMP1 HUMAN	1	10	inside							1	10	inside
	11	33	TM Helix							11	31	TM helix
	34	383	outside	29	381	outside	29	381	outside	32	383	outside	30	383	outside
	384	406	TM Helix	382	405	TM helix	382	405	TM helix	384	404	TM helix	384	404	TM helix
	407	417	inside	406	417	outside	406	417	outside	405	417	outside	405	417	outside
A4 HUMAN										1	5	outside
										6	11	R
	1	700	outside	18	700	outside	18	700	outside	12	701	outside	19	701	outside
	701	723	TM Helix	701	723	TM helix	701	723	TM helix	702	722	TM helix	702	722	TM helix
	724	770	inside	724	770	inside	724	770	inside	723	770	inside	723	770	inside

On the table above, you can see the summary of the results of the different methods which predict transmembrane helices.

Signal Peptide

	Phobius		PolyPhobius		SPOCTOPUS		TargetP	SignalP
protein	start position	end position	start position	end position	start position	end position	location	start position	end position
HEXA HUMAN	1	22	1	19	7	21	secretory pathway	1	22
BACR HALSA	no prediction available						secretory pathway	1	38
RET4 HUMAN	1	18	1	18	6	19	secretory pathway	1	18
INSL5 HUMAN	1	22	1	22	6	23	secretory pathway	1	22
LAMP1 HUMAN	1	28	1	28	12	29	secretory pathway	1	28
A4 HUMAN	1	17	1	17	5	18	secretory pathway	1	15

In the last table there is a list with the results of the prediction of the signal peptides created by different methods.

Comparison of the different methods

We decided to split the comparison of the methods, because it is unfair to directly compare a method which can not predict a signal peptide and a method which predicts signal peptides. Therefore, we split the comparison in one comparison for transmembrane helices, one for signal peptides and one for the combination of both.

Comparison of transmembrane helix prediction

Here we compared TMHMM, OCTOPUS and the transmembrane predictions of SPOCTOPUS, Phobius and PolyPhobius. In this comparison we skipped the first residues which are signal peptides, because all only-transmembrane prediction methods predicted these region as transmembrane helices, which is wrong.
For this comparison we counted the wrong predicted transmembrane residues, the wrong predicted outside located residues and the wrong predicted inside residues.


		methods
		TMHMM	Phobius	PolyPhobius	OCTOPUS	SPOCTOPUS	Transmembrane protein
HEXA_HUMAN	#wrong transmembrane	0	0	0	0	0	no
	#wrong outside	0	0	0	0	0
	#wrong insde	0	0	0	0	0
	#wrong sum	0	0	0	0	0
	%wrong predicted	0%	0%	0%	0%	0%
BACR_HALSA	#wrong transmembrane	24	20	12	16	11	yes (7 transmembrane helices)
	#wrong outside	46	5	3	4	6
	#wrong inside	4	4	2	0	0
	#wrong sum	74	29	17	20	17
	%wrong predicted	29%	11%	6%	8%	6%

RET4_HUMAN	#wrong transmembrane	0	0	0	5	0	no
	#wrong outside	0	0	0	0	0
	#wrong inside	0	0	0	0	0
	#wrong sum	0	0	0	5	0
	%wrong predicted	0%	0%	0%	2%	0%

INSL5_HUMAN	#wrong transmembrane	0	0	0	10	0	no
	#wrong outside	0	0	0	0	0
	#wrong inside	0	0	0	0	0
	#wrong sum	0	0	0	10	0
	%wrong predicted	0%	0%	0%	8%	0%

LAMP1_HUMAN	#wrong transmembrane	5	3	4	3	1	yes (single-spanning)
	#wrong outside	2	0	0	1	1
	#wrong inside	0	0	0	1	1
	#wrong sum	7	3	4	5	3
	%wrong predicted	2%	0%	1%	1%	0%

A4_HUMAN	#wrong transmembrane	0	0	0	0	0	yes (single-spanning)
	#wrong outside	1	1	1	1	2
	#wrong inside	0	0	0	1	1
	#wrong sum	1	1	1	2	3
	%wrong predicted	0%	0%	0%	0%	0%
Average number of wrong predicted residues
		13.6	5.5	3.6	7	3.8

TMHMM is the baddest prediction method. This can also be seen at the example of BACR_HALSA, because TMHMM is the only prediction method, which do not recognize the 7 transmembrane helices. SPOCTOPUS and PolyPhobius are the best prediction methods.

In general the prediction of transmembrane helices works quite good and almost all predictions are very close to the real protein.

Comparison of signal peptide prediction

Now we compared TargetP and SignalP which can only predict signal peptides. Furthermore we compared SPOCTOPUS, Phobius and PolyPhobius. TargetP does not predict the start and end position of the signal peptide, instead it predicts only the location of the protein.


		methods
		real position	Phobius	PolyPhobius	SPOCTOPUS	TargetP	SignalP
HEXA_HUMAN	stop position	22	22	19	21	no prediction	22
	#wrong residues		0	3	3	no prediction	0
	location	secretory pathway	secretory pathway	secretory pathway	no prediction	secretory pathway	no prediction
BACR_HALSA	stop position	not available	no prediction	no prediction	no prediction	no prediction	no consensus prediction
	#wrong predicted	not available	not available	not available	not available	no prediction	not available
	location	membrane	not available	not available	not available	secretory pathway	non-signal peptide

RET4_HUMAN	stop position	18	18	18	19	no prediction	18
	#wrong predicted		0	0	1	no prediction	0
	location	secretory pathway	secretory pathway	secretory pathway	no prediction	secretory pathway	no prediction

INSL5_HUMAN	stop position	22	22	22	22	no prediction	22
	#wrong residues		0	0	0	no prediction	0
	location	secretory pathway	secretory pathway	secretory pathway	no prediction	secretory pathway	no prediction

LAMP1_HUMAN	stop position	28	28	28	29	no prediction	28
	#wrong residues		0	0	1	no prediction	0
	location	transmembrane helix	secretory pathway	secretory pathway	no prediction	secretory pathway	no prediction

A4_HUMAN	stop position	17	17	17	18	no prediction	17
	#wrong residues		0	0	1	no prediction	0
	location	transmembrane helix	secretory pathway	secretory pathway	no prediction	secretory pathway	secretory pathway
Average number of wrong prediction
	sum of wrong predicted residues		0	3	2	no prediction	0
	#right predicted locations / #predicted locations		3/5	3/5	no prediction	3/5	no prediction

SPOCTOPUS and SignalP do not predict the location of the protein, they only predict the start and stop position of the signal peptide. Furthermore, SignalP predicts if it is a signal peptide or not. In contrast, TargetP only predicts the location of the protein, not the start and stop position of the signal peptide. Only Phobius and PolyPhobius predict both.
Therefore, it is difficult to compare the different methods. First of all, Phobius and PolyPhobius have more power than the other prediction methods, because they predict both. In average they predict the location and also the position as good as the other prediction methods. None of the methods could predict the transmembrane proteins, all methods predict them as proteins of the secretory pathway. Therefore, it is useful to use Phobius or PolyPhobius, because they predict more than the other methods. Furthermore, both methods can also predict transmembrane helices. The results of Phobius were a litte bit better than the results of PolyPhobius.
We also wanted to mention, that SignalP gave you the possibility to choose between the prediction for eukaryotes, gram-positive bacteria and gram-negative bacteria. In our analyse we also analysied BACR_HALSA, which is an archaea protein. We tested all three prediction methods for this protein and all three methods failed. BACR_HALSA don't posses a signal peptide, but every method predicts one. Only the eukaryotic prediction method recogniced a signal anchor for BACR_HALSA, whereas the other two methods could not give a prediction of the location.

Comparison of the combined methods

The last thing, which we wanted to compare, was the combined methods. SPOCTOPUS, Phobius and PolyPhobius can predict transmembrane helices as well as signal peptides. Therefore we combined our two further comparisons.


		methods
		Phobius	PolyPhobius	SPOCTOPUS
HEXA_HUMAN	#wrong predicted residues (TM)	0	0	0
	#wrong predicted residues (SP)	0	3	2
	location	right	right	no prediction
BACR_HALSA	#wrong predicted residues (TM)	29	17	17
	#wrong predicted residues (SP)	n.a.	n.a.	n.a.
	location	n.a	n.a	no prediction

RET4_HUMAN	#wrong predicted residues (TM)	0	0	0
	#wrong predicted residues (SP)	0	0	0
	location	right	right	no prediction

INSL5_HUMAN	#wrong predicted residues (TM)	0	0	0
	#wrong predicted residues (SP)	0	0	1
	location	right	right	no prediction

LAMP1_HUMAN	#wrong predicted residues (TM)	3	4	3
	#wrong predicted residues (SP)	0	0	0
	location	wrong	wrong	no prediction

A4_HUMAN	#wrong predicted residues (TM)	0	0	0
	#wrong predicted residues (SP)	1	1	3
	location	wrong	wrong	no prediction
Average
	avg(#wrong predicted residues (TM))	5.3	3.5	3.3
	avg(#wrong predicted residues (SP))	0.1	0.6	1
	#location (right predicted) / #location(predicted)	3/5	3/5	no prediction

In general, PolyPhobius gave the best results. Although it predicts the singal peptide stop position a little bit badder than Phobius, the transmembrane prediction is significant bettern than by Phobius. The predictions of SPOCTOPUS are also good, but sadly SPOCTOPUS does not predict the location of the protein.
Therefore, it seems a good choice to use PolyPhobius, which is in average the best method for transmembrane and signal peptide prediction.

Prediction of GO terms

Before we start with out analysis, we decided to check the GO annotations for the six sequences:

HEXA_HUMAN
Process	skeletal system development
	carbohydrate metabolic process
	ganglioside catabolic process
	lysosome organization
	sensory perception of sound
	locomotory behavior
	adult walking behavior
	lipid storage
	sexual reproduction
	glycosaminoglycan metabolic process
	myelination
	cell morphogenesis involved in neuron differentiation
	neuromuscular process controlling posture
	neuromuscular process controlling balance
Function	catalytic activity
	hydrolase activity, hydrolyzing O-glycosyl compounds
	beta-N-acetylhexosaminidase activity
	protein binding
	hydrolase activity
	hydrolase activity, acting on glycosyl bonds
	cation binding
	protein heterodimerization activity
Component	lysosome
Component	membrane
BACR_HALSA
Process	transport
	ion transport
	phototransduction
	proton transport
	protein-chromophore linkage
	response to stimulus
Function	receptor activity
	ion channel activity
	photoreceptor activity
Component	plasma membrane
	membrane
	integral to membrane
RET4_HUMAN
Process	eye development
	gluconeogenesis
	transport
	spermatogenesis
	heart development
	visual perception
	male gonad development
	embryo development
	maintenance of gastrointestinal epithelium
	lung development
	positive regulation of insulin secretion
	response to retinoic acid
	response to insulin stimulus
	retinol transport
	retinol metabolic process
	glucose homeostasis
	response to ethanol
	embryonic organ morphogenesis
	embryonic skeletal system development
	cardiac muscle tissue development
	female genitalia morphogenesis
	detection of light stimulus involved in visual perception
	positive regulation of immunoglobulin secretion
	retina development in camera-type eye
	negative regulation of cardiac muscle cell proliferation
	embryonic retina morphogenesis in camera-type eye
	uterus development
	vagina development
	urinary bladder development
	heart trabecula formation
Function	transporter activity
	binding
	retinoid binding
	protein binding
	retinal binding
	retinol binding
	retinol transporter activity
Component	extracellular region
Component	extracellular space
INSL5_HUMAN
Process	biological_process
Function	hormone activity
Component	cellular_component
Component	extracellular region
LAMP1_HUMAN
Process	autophagy
Component	membrane fraction
	lysosome
	lysosomal membrane
	endosome
	late endosome
	multivesicular body
	plasma membrane
	integral to plasma membrane
	external side of plasma membrane
	cell surface
	endosome membrane
	membrane
	integral to membrane
	vesicle
	sarcolemma
	melanosome
A4_HUMAN
Process	G2 phase of mitotic cell cycle
	suckling behavior
	platelet degranulation
	mRNA polyadenylation
	regulation of translation
	protein phosphorylation
	cellular copper ion homeostasis
	endocytosis
	apoptosis
	induction of apoptosis
	cell adhesion
	regulation of epidermal growth factor receptor activity
	Notch signaling pathway
	axonogenesis
	blood coagulation
	mating behavior
	locomotory behavior
	axon cargo transport
	cell death
	adult locomotory behavior
	visual learning
	negative regulation of peptidase activity
	positive regulation of peptidase activity
	axon midline choice point recognition
	neuron remodeling
	dendrite development
	platelet activation
	extracellular matrix organization
	forebrain development
	neuron projection development
	ionotropic glutamate receptor signaling pathway
	regulation of multicellular organism growth
	innate immune response
	negative regulation of neuron differentiation
	positive regulation of mitotic cell cycle
	positive regulation of transcription from RNA polymerase II promoter
	collateral sprouting in absence of injury
	regulation of synapse structure and activity
	neuromuscular process controlling balance
	synaptic growth at neuromuscular junction
	neuron apoptosis
	smooth endoplasmic reticulum calcium ion homeostasis
Function	DNA binding
	serine-type endopeptidase inhibitor activity
	receptor binding
	binding
	protein binding
	peptidase activator activity
	peptidase inhibitor activity
	acetylcholine receptor binding
	identical protein binding
	metal ion binding
	PTB domain binding
Component	extracellular region
	membrane fraction
	cytoplasm
	Golgi apparatus
	plasma membrane
	integral to plasma membrane
	coated pit
	cell surface
	membrane
	integral to membrane
	synaptosome
	axon
	platelet alpha granule lumen
	cytoplasmic vesicle
	neuromuscular junction
	ciliary rootlet
	neuron projection
	dendritic spine
	dendritic shaft
	intracellular membrane-bounded organelle
	apical part of cell
	synapse
	perinuclear region of cytoplasm
	spindle midzone

A detailed list of the GO annotation terms of each protein can be found [here].

GOPET

We tried to predict the GO annotations with GOPET for our six different proteins.

HEXA_HUMAN

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. 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

BACR_HALSA

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. 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

RET4_HUMAN

Result of the GOPET prediction for RET4_HUMAN

The method only predicts functional GO terms. RET4_HUMAN has 7 annotated GO functions. The methods predicts 8 GO function terms. Therefore we decided to check if all predictions are correct.

GO term	confidence	prediction term	prediction GOid
binding	90%	right	right
retiniod binding	81&	right	right
lipid binding	80%	wrong	wrong
retional binding	78%	right	right
transporter activity	78%	right	right
retinal binding	78%	right	right
lipid transport activity	69%	wrong	wrong
high-density lipoprotein particle binding	60%	wrong	wrong

INSL5_HUMAN

The method only predicts functional GO terms. INSL5_HUMAN has 1 annotated GO functions. The methods predicts also 1 GO function terms. Therefore we decided to check if all predictions are correct.

GO term	confidence	prediction term	prediction GOid
hormone activity	80%	right	right

LAMP1_HUMAN

Result of the GOPET prediction for LAMP1_HUMAN

The method only predicts functional GO terms. LAMP1_HUMAN has 0 annotated GO functions. The methods predicts 2 GO function terms. Therefore the predictions are wrong.

A4_HUMAN

Result of the GOPET prediction for A4_HUMAN

The method only predicts functional GO terms. A4_HUMAN has 11 annotated GO functions. The methods predicts 13 GO function terms. Therefore we decided to check if all predictions are correct.

GO term	confidence	prediction term	prediction GOid
endopeptidase inhibitor activity	87%	right	wrong
serine-type endopeptidase inhibitor activity	86%	right	right
plasmin inhibitor activity	83%	wrong	wrong
trypsin inhibitor activtiy	83%	wrong	wrong
peptidase inhibitor activity	82%	right	right
binding	79%	right	right
protein binding	74%	right	right
metal ion binding	73%	right	right
DNA binding	71%	right	right
heparin binding	70%	wrong	right
zinc ion binding	69%	wrong	wrong
copper ion binding	69%	wrong	wrong
iron ion binding	67%	wrong	wrong

Pfam

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.

HEXA_HUMAN