Secondary Structure Prediction BCKDHA

1. Secondary structure prediction

PSIPRED

Basic information

author: David Jones (University College London)
year:1998
version: 2

References

[PSIPRED Server]
[Overview of prediction methods]
[History of the PSIPRED]

Theory

PSIPRED uses neuronal networks which has a single hidden layer and a feed-forward back-propagation architecture. FOr the online prediction on the server it is enough to enter a amino acid sequence. Since PSIPRED uses a very stringent cross validation method to evaluate the performance it reaches an average Q3 score of 80.7%.

Algorithm

The predicition is splitted into three different steps. In the first step sequence profiles are generated by using a position specific scoring matrix from PSI-BLAST as input for the neuronal network. In the next step the secondary structure is predicted. In the last step the output of the secundary structure prediction is filtered.

What is predicted

PSIPRED predicts the secondary structure

Features

There are three different options:
- Mask low complexity regions
- Mask transmembrane helices
- Mask coiled-coil regions

It is also possible to get an email with the results of PSIPRED.

Required information

PSIPRED requires the output of PSI-BLAST (Position Specific Iterated - BLAST) as input data.

Prediction

start	end	structural element
1	1	C
2	16	H
17	17	C
18	25	H
26	77	C
78	82	E
83	98	C
99	124	H
125	136	C
137	146	H
147	152	C
153	155	E
156	159	C
160	166	H
167	170	C
171	178	H
179	212	C
213	125	H
126	130	C
231	236	E
237	242	C
243	256	H
257	259	C
260	265	E
266	276	C
277	278	H
279	282	C
283	287	H
288	296	C
297	298	E
299	300	C
301	318	H
319	323	C
324	329	E
330	347	C
348	356	H
357	360	C
361	370	H
371	375	C
377	399	H
400	404	C
405	413	H
414	417	C
418	434	H
435	445	C

legend: A=alpha helix, E=beta strand, C=coil

PSIPRED has predicted 23 coils, 16 alpha helices and 6 beta sheets.

Jpred3

Basic information

author: Cole C, Barber JD & Barton GJ (Bioinformatics and Computational Biology Research, University of Dundee)
year: 1998
version: 3

References

Jpred3 Server
About Jpred
FAQ

Theory

Jpred is using the neural network called Jnet to predict the secondary structure of a protein sequence or multiple alignment of protein sequences. The prediction accuracy for secondary strctures lies above 81%. Additionally Jpred makes predictions on Solvent Accessibility and Coiled-coil regions. It predicts wether a residue is burried or exposed by using the several cut-off values.

What is predicted

Jpred3 predicts secondary structure from the sequence or the multiple alignment.
It also predicts the relative solvent accessibility

Features

Jpred3 has two different modes: - single sequence - multiple alignment

Required information

Jpred3 needs a protein sequence or multiple alignment of protein sequences as input.

Multiple sequence: The first sequence has to be the target sequence since the alignment is modified so that the first sequence do not have any gaps. The alignemt has to be in the MSF or in the BLC format. Single sequence: For the sequence a multiple alignment is constructed with PSI-BLAST (3 iteratoins).

Prediction

1u5b (e-value:0)			1umd (e-value:6e-58)			1qs0 (e-value:1e-57)			3dv0 (e-value:2e-51)
EMBL-EBI			EMBL-EBI			EMBL-EBI			EMBL-EBI
UniProt			UniProt			UniProt			UniProt
position	structural element		position	structural element		position	structural element		position	structural element
						10–19	alpha helix
						24-26	alpha helix
			35-60	alpha helix		36–38	alpha helix
61-64	beta strand					44–47	alpha helix		44–69	alpha helix
						48–51	alpha helix
						67–69	alpha helix
			74-83	alpha helix		74–99	alpha helix
									83–91	alpha helix
91-93	alpha helix		89-92	beta strand
99-124	alpha helix		98-104	alpha helix		102–104	beta strand		98–100	beta strand
			108-116	alpha helix		110–112	turn		106–111	alpha helix
			122-125	turn		113–122	alpha helix		116–124	alpha helix
127-129	beta strand					127–130	beta strand		127–130	alpha helix
			135-138	turn
138-146	alpha helix		144-147	beta strand		136–141	alpha helix
152-154	beta strand		150-162	alpha helix		146–154	alpha helix		147 – 161	alpha helix
161-166	alpha helix					160–163	turn
171-179	alpha helix		169-173	beta strand		173–175	alpha helix		168–173	beta strand
			176-179	alpha helix		175–178	alpha helix
185-188	turn		181-193	alpha helix		182–185	beta strand		180–191	alpha helix
198-201	turn		196-204	beta strand		186–200	alpha helix		196–202	beta strand
						207–212	beta strand		204–206	beta strand
209-211	turn					211–213	alpha helix
212-226	alpha helix		222-227	alpha helix		214–217	alpha helix		221–226	alpha helix
232-237	beta strand		232-236	beta strand		219–231	alpha helix		231–235	beta strand
240-242	alpha helix		240-255	alpha helix		235–241	beta strand		239–254	alpha helix
244-255	alpha helix					243–245	beta strand
						250–253	alpha helix
						254–257	turn
260-266	beta strand		261-267	beta strand		262–266	alpha helix		260–265	beta strand
268-270	beta strand
						270–275	beta strand
275-277	alpha helix
280-282	beta strand					279 – 294	alpha helix
285-287	alpha helix					285–292	alpha helix
289-291	alpha helix
294-299	beta strand		296-305	alpha helix		300–305	beta strand		296–306	alpha helix
303-320	beta strand		306-308	turn		318–320	alpha helix
324-329	beta strand		312-334	alpha helix		326–329	alpha helix		312–334	alpha helix
			341-345	alpha helix		335–345	alpha helix		341–346	alpha helix
			348-351	beta strand
360-368	alpha helix		354-366	alpha helix		351–373	alpha helix		354–366	alpha helix
369-372	turn
376-399	alpha helix					378–380	beta strand
						388–390	alpha helix
						391–396	beta strand
405-408	alpha helix					399–406	alpha helix
412-415	beta strand
418-434	alpha helix
435-437	alpha helix
440-442	alpha helix

1u5b	1umd	1qs0	3dv0
e-value:0	e-value:6e-58	e-value:1e-57	e-value:2e-51

DSSP

1. line: Sequence
2. line: structral elements
3. line: if a residue is involved in symmetrie contacts it is labeled with a star
4. line: if a residue is solvent accessible it is labeled with an "A"

Letter code for the secundary structure elements:

- H (blue): alpha
- 3 (yellow): residue in isolated beta-bridge
- T (red): hydrogen bonded turn
- S (green): bend

File:Output.pdf

2. Prediction of disordered regions

DISOPRED

The disordered regions in BCKDHA are predicted by DISOPRED in the beginning and in the end of the protein.

POODLE

POODLE-S (Missing residues)

POODLE-S (which predicts short disordered regions) with the option "Missing residues" predicted the disordered regions between the positions 1-56, 341-345 and 420-423. This is also shown in the plot above.

Detailed sequence with disordered region probability: File:PoodleSMissingResiduesOut.pdf

The probability can reach from 0 to 1. Where 0 means there is no disordered region and 1 that there is a disordered region.

POODLE-S (High B-Factor residues)

POODLE-S (which predicts short disordered regions) with the option "High B-Factor residues" predicted the disordered regions between the positions 6-9, 15-57, 93, 95-96, 340-354 and 379-402. This is also shown in the plot above.

Detailed sequence with disordered region probability: File:PoodleSFactorBOut.pdf

The probability can reach from 0 to 1. Where 0 means there is no disordered region and 1 that there is a disordered region.

POODLE-W

The regions which could be disordered regions but poodle is not sure are bordered by blue squares and the disordered regions are bordered by red squares.

Detailed sequence with disordered region: File:PoodleWDOSeq.pdf
0=ordered regions
5=perhaps disordered regions
9=disordered regions

IUPred

Prediction type: long disorder

Detailed sequence with disordered region probability: File:LongSeqOut.pdf

Prediction type: short disorder

Detailed sequence with disordered region probability: File:ShortSeqOut.pdf

Prediction type: structured regions

With the option "structured regions" there was no prediction of disordered regions.
Only the command "Unkown globular domains: 1-445" appeared.

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3. Prediction of transmembrane alpha-helices and signal peptides

In the following section different tools for predicting transmembrane helices and signal peptides are tested. As the BCKDHA protein isn't a transmembrane protein, additional proteins were used for the transmembrane and signal peptide analysis:

name	location	transmembrane protein	function	reference
BACR_HALSA	Cell membrane	yes	ion transport	P02945
INSL5_HUMAN	extracellular region	no	hormone	Q9Y5Q6
LAMP1_HUMAN	Cell membrane, Lysosome membrane, Endosome membrane	yes	Presents carbohydrate ligands to selectins	P11279
A4_HUMAN	Cell membrane	yes	Protease Inhibitor	P05067
RET4_HUMAN	extracellular space	no	Transport	P02753

Transmembrane topology and signal peptides are features that are likely to be conserved during evolution.

TMHMM

• TMHMM was developed by Sonnhammer, Heijne and Krogh in 1998 <ref> E.L. Sonnhammer, Heijne and A. Krogh, A hidden Markov model for predicting transmembrane helices in protein sequences, Proc Int Conf Intell Syst Mol Biol.(1998)</ref>
• TMHMM predicts transmembrane helices in proteins.
• TMHMM is a membrane topology prediction method based on a hidden Markov model.

Phobius and Polyphobius

• Phobius was developed by Käll et al <ref> "A Combined Transmembrane Topology and Signal Peptide Prediction Method", Journal of Mol. Biology,338(5):1027-1036, 2004 </ref>
• combined prediction of transmembrane regions and signal peptids
• Required input information: only sequence in FASTA-Format (20 amino acids and B, Z, X are recognized)
• As transmembrane topology and signal peptides are likely to be conserved during evolution, Polyphobius was established <ref>Käll et al., "An HMM posterior decoder for sequence feature prediction that includes homology information", Bioinformatics, 21 (Suppl 1):i251-i257, 2005</ref>, which includes information from homologous sequences to the query.
• Required input: 2 Options: Query Sequence in FASTA-Format, which is then blasted agains uniprot_trembl or upload of an alignment in FASTA-Format which provides information about homologs.

BACR_HALSA
	Phobius	Polyphobius
	sp|P02945|BACR_HALSA TOPO_DOM 1 22 NON CYTOPLASMIC. TRANSMEM 23 42 TOPO_DOM 43 53 CYTOPLASMIC. TRANSMEM 54 76 TOPO_DOM 77 95 NON CYTOPLASMIC. TRANSMEM 96 114 TOPO_DOM 115 120 CYTOPLASMIC. TRANSMEM 121 142 TOPO_DOM 143 147 NON CYTOPLASMIC. TRANSMEM 148 169 TOPO_DOM 170 189 CYTOPLASMIC. TRANSMEM 190 212 TOPO_DOM 213 217 NON CYTOPLASMIC. TRANSMEM 218 237 TOPO_DOM 238 262 CYTOPLASMIC.	sp|P02945|BACR_HALSA TOPO_DOM 1 21 NON CYTOPLASMIC. TRANSMEM 22 43 TOPO_DOM 44 54 CYTOPLASMIC. TRANSMEM 55 77 TOPO_DOM 78 94 NON CYTOPLASMIC. TRANSMEM 95 114 TOPO_DOM 115 120 CYTOPLASMIC. TRANSMEM 121 141 TOPO_DOM 142 147 NON CYTOPLASMIC. TRANSMEM 148 166 TOPO_DOM 167 186 CYTOPLASMIC. TRANSMEM 187 205 TOPO_DOM 206 215 NON CYTOPLASMIC. TRANSMEM 216 237 TOPO_DOM 238 262 CYTOPLASMIC.

INSL5_HUMAN
	Phobius	Polyphobius
	sp|Q9Y5Q6|INSL5_HUMAN SIGNAL 1 22 REGION 1 5 N-REGION REGION 6 17 H-REGION REGION 18 22 C-REGION TOPO_DOM 23 135 NON CYTOPLASMIC	sp|Q9Y5Q6|INSL5_HUMAN SIGNAL 1 22 REGION 1 4 N-REGION REGION 5 16 H-REGION REGION 17 22 C-REGION TOPO_DOM 23 135 NON CYTOPLASMIC

LAMP1_HUMAN
	Phobius	Polyphobius
	sp|P11279|LAMP1_HUMAN SIGNAL 1 28 REGION 1 10 N-REGION REGION 11 22 H-REGION REGION 23 28 C-REGION TOPO_DOM 29 381 NON CYTOPLASMIC TRANSMEM 382 405 TOPO_DOM 405 417 CYTOPLASMIC	sp|P11279|LAMP1_HUMAN SIGNAL 1 28 REGION 1 9 N-REGION REGION 10 22 H-REGION REGION 23 28 C-REGION TOPO_DOM 29 381 NON CYTOPLASMIC TRANSMEM 382 405 TOPO_DOM 405 417 CYTOPLASMIC

A4_HUMAN
	Phobius	Polyphobius
	sp|P05067|A4_HUMAN SIGNAL 1 17 REGION 1 1 N-REGION REGION 2 12 H-REGION REGION 13 17 C-REGION TOPO_DOM 18 700 NON CYTOPLASMIC TRANSMEM 701 723 TOPO_DOM 724 770 CYTOPLASMIC	sp|P05067|A4_HUMAN SIGNAL 1 17 REGION 1 3 N-REGION REGION 4 12 H-REGION REGION 13 17 C-REGION TOPO_DOM 18 700 NON CYTOPLASMIC TRANSMEM 701 723 TOPO_DOM 724 770 CYTOPLASMIC

RET4_HUMAN
	Phobius	Polyphobius
	sp|P02753|RET4_HUMAN SIGNAL 1 18 REGION 1 2 N-REGION REGION 3 13 H-REGION REGION 14 18 C-REGION TOPO_DOM 19 201 NON CYTOPLASMIC	sp|P02753|RET4_HUMAN SIGNAL 1 18 REGION 1 3 N-REGION REGION 4 13 H-REGION REGION 14 18 C-REGION TOPO_DOM 19 201 NON CYTOPLASMIC

For the BCKDHA-protein Phobius predicted a signal peptide with about 90% probability at the beginning of the sequence. The predicted signal peptide is 34 amino acids long. This matches the information given on Uniprot, which says, that BCKDHA contains a 45bp long signal peptide for the transfer into the mitochondrion. The rest of the amino acid is a non cytoplasmic protein sequence. No part of the protein is predicted to be transmembrane spanning. This is also true, as BCKDHA is a protein located in the mitochondrion matrix according to Uniprot.

BCKDHA
	Phobius	Polyphobius
	sp|P12694|ODBA_HUMAN (BCKDHA) Signal 1 34 Region 1 16 N-Region Region 17 25 H-Region Region 26 34 C-Region TOPO_DOM 35 445 non cytoplasmic	OBDA_HUMAN (BCKDHA) TOPO_DOM 1 445 Non cytoplasmic

Considering the information given on Uniprot, Polyphobius performed worse than Phobius on the BCKDHA-protein sequence. It predicted no signal sequence at the beginning of the protein sequence. There is a low probability for the amino acids between position 1-45 to be a signal sequence, but all in all the whole sequenc is predicted to be a non cytoplasmic protein.

OCTOPUS and SPOCTOPUS

• OCTOPUS was developed by Viklund and Elofsson in 2008 <ref>Håkan Viklund and Arne Elofsson, "Improving topology prediction by two-track ANN-based preference scores and an extended topological grammar", Bioinformatics (2008)</ref>
• OCTOPUS (obtainer of correct topologies for uncharacterized sequences) uses a combination of hidden Markov models and artificial neural networks.
• It creates a sequence profile by doing a BLAST search to obtain homologous sequences. The profile is used as input for a neural network that predicts the probability for each residue to be located in a transmembrane(M), interface (I), close loop (L), or globular loop (G) environment as well as the preference to be inside (i) or outside (o) of the membrane. A hidden Markov model is used to calculate the most likely Protein Topology.
• Required input: Protein Sequence in FASTA-Format
• SPOCTOPUS (Viklund et al., 2008<ref>Viklund et al., "A combined predictor of signal peptides and membrane protein topology", Bioinformatics (2008)</ref>) is an extension of OCTOPUS which also predicts signal peptides. A neural network is used to predict a signal peptide preference score. The signal peptide's location is determined by a hidden Markov model. The output contains the information retrieved by OCTOPUS as well as the probabilty if a residue is predicted to be N-terminal of a signal peptide (n) or in a signal peptide (S).
• Required input information: Protein sequence in FASTA-Format

BACR_HALSA
OCTOPUS
SPOCTOPUS
INSL5_HUMAN
OCTOPUS
SPOCTOPUS
LAMP1_HUMAN
OCTOPUS
SPOCTOPUS
A4_HUMAN
OCTOPUS
SPOCTOPUS
RET4_HUMAN
OCTOPUS
SPOCTOPUS
BCKDHA
OCTOPUS
SPOCTOPUS

SignalP

• SignalP was established by Nielsen et al. in 1997<ref>Nielsen et al., "Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites", Protein Engineering, 10:1-6, 1997</ref>
• SignalP is neural network based. It identifies signal peptides and cleavage sites.

TargetP

• TargetP was developed by Emanuelsson et al. in 2002 <ref> Emanuelsson et al., "Predicting subcellular localization of proteins based on their N-terminal amino acid sequence", J. Mol. Biol., 200: 1005-1016, 2002</ref>
• TargetP predicts the subcellular location of eukaryotic proteins. additionally: cleavage site predictions
• This method is neural network based. The prediction is based on the N-terminal presequences: chloroplast transit peptide(cTP), mitochondiral targeting peptide (mTP) or secretory pathway signal peptide (SP)
• Required input information: Sequence(s) in FASTA format, organism group

The TargetP prediction results can be seen in the following table:

The ODBA_HUMAN (BCKDHA) is predicted to be located in the mitochondrion, which is true according to Uniprot.

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4. Prediction of GO terms

GOPET

GOPET results fot BCKDHA:

GOid	Aspect	Confidence	GOTerm
GO:0003824	F	97%	catalytic activity
Go:0016491	F	96%	oxidoreductase activity
GO:0016624	F	95%	oxidoredusctase activity acting on the aldehyde or oxo group of donors disulfide as acceptor
GO:0003863	F	90%	3-methyl-2-oxobutanoate dehydrogenase 2-methylpropanoyl-transferring activity
GO:0004739	F	89%	pyruvate dehydrogenase acetyl-transferring activity
GO:0004738	F	78%	pyruvat dehydrogenase activity
GO:0003826	F	77%	alpha-ketoacid dehydrogenase activity
GO:0047101	F	75%	2-oxoisovalerate dehydrogenase acylting activity
GO:0008677	F	65%	2-dehydropantoate 2-reductase activity
GO:0019152	F	63%	acetoin dehydrogenase activity
GO:0030955	F	63%	potassium ion binding
GO:0016616	F	62%	oxidoreductase activity acting on the CH-OH group of donors NAD or NADP as acceptor
GO:0046872	F	62%	metal ion binding

GOPET results for A4_HUMAN

GOid	Aspect	Confidence	GOTerm
GO:0004866	F	87%	endopeptidase inhibitor activity
GO:0004867	F	86%	serine-type endopeptidase inhibitor activity
GO:0030568	F	83%	plasmin inhibitor activity
GO:0030304	F	83%	trypsin inhibitor activity
GO:0030414	F	82%	peptidase inhibitor activity
GO:0005488	F	79%	binding
GO:0005515	F	74%	protein binding
GO:0046872	F	73%	metal ion binding
GO:0003677	F	71%	DNA binding
GO:0008201	F	70%	heparin binding
GO:0008270	F	69%	zinc ion binding
GO:0005507	F	69%	copper ion binding
GO:0005506	F	67%	iron ion binding

GOPET results for BACR_HALSA:

GOid	Aspect	Confidence	GOterm
GO:0005216	F	77%	ion channel activiy
GO:0008020	F	75%	G-protein coupled photoreceptor activity
GO:0015078	F	60%	hydrogen ion transmembrane transporter activity

GOPET results for INSL5_HUMAN:

GOid	Aspect	Confidence	GOterm
GO:0005179	F	80%	hormone activity

GOPET results for LAMP1_HUMAN:

GOid	Aspect	Confidence	GOterm
GO:0004812	F	60%	aminoacyl-tRNA ligase activity
GO:0005524	F	60%	ATP binding

GOPET results for RET4_HUMAN:

GOid	Aspect	Confidence	GOterm
GO:0005488	F	90%	binding
GO:0005501	F	81%	retinoid binding
GO:0008289	F	80%	lipid binding
GO:0019841	F	78%	retinol binding
GO:0005215	F	78%	transporter activity
GO:0016918	F	78%	retinal binding
GO:0005319	F	69%	lipid transporter activity
GO:0008035	F	60%	high-density lipoprotein particle binding

Pfam

• Pfam was established by Finn et al. in 2008. It is described in <ref>Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR, Sonnhammer EL, Bateman A (2008). "The Pfam protein families database.". Nucleic Acids Res 36 (Database issue): D281–8</ref>

Query	Cellular Component	Molecular function	Biological Process
BCKDHA		GO:0016624 (oxidoreductase activity, acting on the aldehyde or oxo group of donors, disulfide as acceptor)	GO:0008152 (metabolic process)
A4_HUMAN	GO:0016021 (integral to membrane)	GO:0005488 (binding)
BACR_HALSA	GO:0016020 (membrane)	GO:0005216 (ion channel activity)	GO: 0006811 (ion transport)
INSL5_HUMAN	GO:0005576 (extracellular region)	GO:0005179 (hormone activity)
LAMP1_HUMAN	GO:0016020 (membrane)
RET4_HUMAN		GO:0005488 (binding)

References

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