Task 3: Sequence-based predictions

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Contents

Task description

The full description of this task can be found here.


Task 3.1: Secondary structure prediction

Task 3.2: Prediction of disordered regions

Task 3.3: Prediction of transmembrane alpha-helices and signal peptides

Annotated sequence features

PAH

The phenylalanine-4-hydroxylase has no annotated signal peptide or transmembrane helices.

BACR_HALSA

The bacteriorhodopsin has the following annotated signal peptide and transmembrane helices:

Position Feature Name Description
1 - 13 Propeptide
14 – 23 Topological domain Extracellular
24 - 42 Transmembrane Helical; Name=Helix A
43 – 56 Topological domain Cytoplasmic
57 - 75 Transmembrane Helical; Name=Helix B
76 – 91 Topological domain Extracellular
92 - 109 Transmembrane Helical; Name=Helix C
110 – 120 Topological domain Cytoplasmic
121 - 140 Transmembrane Helical; Name=Helix D
141 – 147 Topological domain Extracellular
148 - 167 Transmembrane Helical; Name=Helix E
168 – 185 Topological domain Cytoplasmic
186 - 204 Transmembrane Helical; Name=Helix F
205 – 216 Topological domain Extracellular
217 - 236 Transmembrane Helical; Name=Helix G
237 – 262 Topological domain Cytoplasmic


RET4_HUMAN

The retinol-binding protein 4 has the following annotated signal peptide (no transmembrane helices are annotated):

Position Feature Name Description
1 - 18 Signal peptide

INSL5_HUMAN

The Insulin-like peptide INSL5 has the following annotated signal peptide (no transmembrane helices are annotated):

Position Feature Name Description
1 - 22 Signal peptide


LAMP1_HUMAN

The lysosome-associated membrane glycoprotein 1 has the following annotated signal peptide and transmembrane helices:

Position Feature Name Description
1 - 28 Signal peptide
29 – 382 Topological domain Lumenal
383 - 405 Transmembrane Helical;
406 – 417 Topological domain Cytoplasmic


A4_HUMAN

The Amyloid beta A4 protein has the following annotated signal peptide and transmembrane helices:

Position Feature Name Description
1 - 17 Signal peptide
18 – 699 Topological domain Extracellular
700 - 723 Transmembrane Helical;
724 – 770 Topological domain Cytoplasmic

General Questions to prediction of transmembrane alpha-helices and signal peptides

Why is the prediction of transmembrane helices and signal peptides grouped together here?

Methods which only predict transmembrane helices often predict signal peptides as transmembrane helices as well. The reason for this is that both, transmembrane helices and signal peptides consist mainly of hydrophobic residues. These false predictions lead to inaccurate topological features and thus to wrongly annotated function of a protein. To avoid these cases most recent methods couple their transmembrane prediction together with a signal peptide prediction.

Description of different signal peptides

Signalpeptides for the import to the endoplasmic reticulum (ER)

The import to the ER is usually required for the secretory pathway (to export proteins out of a cell). The import process can occur either co-translational (the nascent protein chain is translocated together with the ribosome) or post-translational (only the fully synthesized protein is transported to the ER). However, for both cases the SEC-pathway is mostly used.

The co-translational transport to the ER is done by the signal recognition particle (SRP). This particle recognizes the N-terminal signal-sequence of the nascent polypeptide chain and then transports it to the ER membrane where the complex, consisting of SRP, polypeptide chain and ribosome, is recognized by the ER membrane bound signal recognition particle receptor (SR). After this recognition the polypeptide chain is imported into the ER lumen via the SEC channel in an ATP dependent process.

The post-translational import to the ER lumen is done by chaperons which guide the polypeptide chain to the SEC channel which is then imported in an ATP dependent process.

However, not only the import to the ER lumen is possible, an import to the ER membrane is possible as well. So far, 5 different types of import to the ER membrane are known.

Type 1 requires an N-terminal signal sequence and an intrinsic stop transfer anchor sequence which will be the part which is inserted in the membrane.

Type 2 and 3 do not require a N-terminal signal sequence only a intrinsic signal anchor sequence is required. The difference between type 2 and 3 is that type 2 has positively charged residues before the signal anchor sequence (on the N-Terminal side) and type 3 has positively charged residues after the signal anchor sequence (on C-Terminal side). These charged residues of trans-membrane protein are always in the cytosol. Thus, type 2 inserted proteins have their N-terminal end residing in the cytosol whereas type 3 inserted proteins have a C-terminal end in the cytosol.

Type 4-A and 4-B insertion is also known as multipass membrane insertion. These proteins have not one trans-membrane helix like the proteins imported via Type 1,2 and 3, instead they have several trans-membrane helices. Hence, they consist of multiple internal stop-transfer anchor sequences and internal signal-anchor sequences. The difference between type 4-A and 4-B is that in type 4-A the N and C terminal ends are located in the cytosol whereas type 4-B import results in a N-terminal end residing in the ER lumen and a C-terminal end residing in the cytosol.

In addition to the N-terminal import of trans-membrane proteins there is also the possiblity for a C-terminal import. Obviously, these proteins are imported post-translation.

Signalpeptides for the import to the mitochondrion

There are several targets for import to the mitochondrion, proteins can be translocated to the matrix, the outer membrane, the inner membrane and the inter membrane space.

Proteins who are designated to be imported to the matrix of a mitochondrion have a N-terminal matrix-targeting sequence. This mitochondrial import to the matrix is assisted by chaperons (Hsc70) which guide the protein to the import pore complex of the mitochondrion. The import through the outer membrane is conducted by the TOM complex and the following import through the inner membrane is conducted by the TOM complex. After successful import to the matrix the signal sequence is cleaved off by proteolytic active enzymes.

Import to the inner membrane can occur in three ways. The first way is the TIM22 pathway, proteins using this pathway need internal targeting sequences. The next way is the stop transfer import, for this proteins need a stop transfer sequence and a N-terminal matrix targeting sequence. The third way is called conservative sorting proteins using this pathway have a N-terminal targeting sequence as well and in addition intrinsic Oxa1-targeting sequences which are recognized by Ox1-proteins which execute the import to the membrane.

Proteins imported to the outer membrane of a mitochondrion usually have PORTA domains which are recognized by the TOB/SAM complex.

Signalpeptides for the import to the chloroplast

Proteins heading to chloroplasts can target different parts of it. For example the stroma, inner and outer membrane, the thylakoids membrane or the thylakoids lumen.

Usually these protein have a N-terminal targeting sequence.

Signalpeptides for the import to the peroxisome

Peroxisomal proteins can be imported to the lumen or to the membrane. Proteins imported to the lumen have either a peroxisomal targeting signal at the C-termins (also known as PTS1) or a targeting sequence close to the N-terminus (also known as PTS2). Proteins imported to the membrane can have an intrinsic membrane peroxisomal targeting signal (mPTS). However, not all proteins have this mPTS. These proteins are imported to the ER and from there they bud off together with the mature peroxisome.

Signalpeptides for the import to the nucleus and the export form the nucleus

Proteins which are imported to the nucleus require a nuclear localisation signal (NLS) which is recognized by importin. The NLS containing protein is then imported via the nuclear pore complex (NPC) to the nucleoplasm.

Proteins which are exported from the nucleus require a nuclear export signal which is recognized by exportin, a protein which binds to the NES of the cargo protein. In addition to exportin a second component, known as Ran*GTP, is required to mediate the export through the NPC.

TMHMM

Details of the method

Author: Sonnhammer, Heijne & Krogh

Year: 1998

Reference: PubMed

Description
HMM architecture of TMHMM Disclaimer: This file is redistributed from [Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001 Jan 19;305(3):567-80.] . All rights belong to the creator.

This method is based on a hidden markov model (HMM). The authors of this method tried to model the 'grammar' of transmembrane proteins in order to predict the protein topology of transmembrane more accurate than methods who only e.g. rely on propensity values and do not consider the topological constraints of these class of proteins.

TMHMM defined for their HMM for each feature one or more states which present this feature. For example the transmembrane helix is modeled by three sub models. A model for the helix core, the cap of the helix which lies partly in the cytoplasm and the membrane and the cap which is partly in the membrane and cytoplasm. In addition to this helix model they also created sub models for the cytoplasmic loop and the non-cytoplasmic loop as well as a sub model for the globular region. Each sub model can reflect one or more states in the HMM model. For example the globular sub model only consists of one HMM state whereas the helix-core and caps are modeled by multiple HMM states.

The 'grammar' is incorporated to this HMM model by defining the possible transitions from one sub model to another one. For example it is only possible to change from a cytoplasmic loop region to a cytoplasmic cap region and then to the helix core and after that either to non-cytoplasmic short loop or long non-cytoplasmic loop and so on.

Predicted features

This methods predicts the transmembrane helix and whether this part is in the cytoplasm (in) or outside of it (out).

Required information for the prediction

User who want to use it just need their amino acid sequence of their query sequence. The transmission and emission probabilities are derived from 160 transmembrane protein sequences.


Execution

Before we could execute TMHMM we had to change all occurrences of "/usr/local/bin/" to "/usr/bin" in these files: tmhmm, tmhmm.ORIG and tmhmmformat.pl

Then we executed the following command to retrieve the results for all sequences:

  • tmhmm all.fa > task_33/tmhmm_out.txt

Results and discussion

PAH
Position Feature Name
1 - 452 outside


BACR_HALSA
Position Feature Name
1 - 22 outside
23 - 42 TMhelix
43 - 54 inside
55 - 77 TMhelix
78 - 91 outside
92 - 114 TMhelix
115 - 120 inside
121 - 143 TMhelix
144 - 147 outside
148 - 170 TMhelix
171 - 189 inside
190 - 212 TMhelix
213 - 262 outside
RET4_HUMAN
Position Feature Name
1 - 201 outside


INSL5_HUMAN
Position Feature Name
1 - 135 outside
LAMP1_HUMAN
Position Feature Name
1 - 10 inside
11 - 33 TMhelix
34 - 383 outside
384 - 406 TMhelix
407 - 417 inside
A4_HUMAN
Position Feature Name
1 - 700 outside
701 - 723 TMhelix
724 - 770 inside

Phobius

Details of the method

Author: Käll, Krogh, Sonnhammer

Year: 2004

Reference: PubMed

Description
HMM architecture of Phobius Disclaimer: This file is redistributed from [KKäll L, Krogh A, Sonnhammer EL. A combined transmembrane topology and signal peptide prediction method. J Mol Biol. 2004 May 14;338(5):1027-36.] . All rights belong to the creator.

Phobius is an HMM based prediction method to predict transmembrane helices as well as N-terminal signal peptides. More precisely, it is a combination of the two HMM models of TMHMM and SignalP which is merged into one HMM. This was done in order to overcome problems associated with transmembrane helix prediction: signale peptides are often wrongly predicted as transmembrane helices. The complete architecture can be seen in the figure.

Predicted features

Phobius predicts transmembrane helices, signal peptides and the topology of the loops (whether they are inside the cytoplasm or not).

Required information for the prediction

Users only has to enter the amino acid sequence of their query protein in FASTA format.

Execution

Phobious all in.png


Results and discussion

Phobious all out.png

PAH
BACR_HALSA
RET4_HUMAN
INSL5_HUMAN
LAMP1_HUMAN
A4_HUMAN

PolyPhobius

Details of the method

Author: Käll L, Krogh A, Sonnhammer EL

Year: 2005

Reference: PubMed

Description

PolyPhobius is also based on a HMM which constraints the possible transitions from one state to another in order to reflect the 'grammar' of transmembrane proteins. However, the difference to the ordinary Phobius is that it uses knowledge homologous sequences of the query sequences as well to make the prediction more accurate.

In order to do so it calculates for each sequence position for each label (e.g. transmembrane helix, in, out, etc...) for each homologous sequence the posterior label probability (PLP). The PLP is defined as "the probability of a label at a certain position in the sequence, given the sequence and the model" (quoted from "Käll L, Krogh A, Sonnhammer EL. An HMM posterior decoder for sequence feature prediction that includes homology information Bioinformatics. 2005 Jun;21 Suppl 1:i251-7."). Then a multiple sequence alignment (MSA) of all homologous sequences is build, for each position in the MSA a average PLP is calculated. This average PLP will be then be used by the optimal accuracy algorithm to predict the most likely sequences of states for a given query sequence and thus the topology of the transmembrane helices.

Predicted features

This method predicts the same features as the ordinary Phobius, which means transmembrane helices, the signal peptide and whether the connecting loops of transmembrane helices are inside or outside.

Required information for the prediction

User need the amino acid sequence of their protein in FASTA format. An additional option is to specify the homologous sequences manually. If that is not done PolyPhobius will search for homologous sequences by itself by using BLAST.

Execution

Polyphobius all in.png

Results and discussion

Polyphobius all out.png


PAH
BACR_HALSA
RET4_HUMAN
INSL5_HUMAN
LAMP1_HUMAN
A4_HUMAN

OCTOPUS

Details of the method

Author: Viklund H, Elofsson A.

Year: 2008

Reference: Bioinformatics

Description
Flowchart of OCTOPUS Disclaimer: This file is redistributed from [Viklund H, Elofsson A. OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics. 2008 Aug 1;24(15):1662-8. Epub 2008 May 12.] . All rights belong to the creator.

OCTOPUS basically uses two methods to predict the topology of transmembrane proteins: artificial neural networks (ANN) and hidden markov models (HMM). In a first step BLAST searches for homologous sequences of a input FASTA sequence. From the found homologous sequences a multiple sequence alignment is build from which a raw sequence profile and a sequence profile based on PSSM are extracted. These profiles are used for two sets of ANNs.

The first set of ANNs contains four separate ANNs which predict the residue preference for M (Membrane), I (Interface), L (Loop), G (Globular). In order to make the predictions for G and M more smooth the output of the first row of ANNs output is used for a second ANN as input.The second set of ANNs is taken to predict the residue preferences for the inside/outside residues.

Finally. the output of these two sets of ANNs are used to parameterize the OCTOPUS-HMM for the actual topological feature prediction. This HMM is needed to model the 'grammar' of trans membrane proteins, which simply means that only certain state transitions are allowed. For example, if we assume we are currently in the transmembrane state then it is only allowed to go into the loop state and so on and so forth.

The state sequences which fits best the input sequence is then calculated by the Viterbi algorithm.

Predicted features

Predicted features are inside/outside (i/o), transmembrane (M), TM hairpin (H), reentrant (R) or membrane dip (D)

Required information for the prediction

Only the amino acid sequence of the users protein is required.

Execution

Octopus pah in.png

Results and discussion

PAH

Octopus pah out.png

BACR_HALSA

Octopus bacr halsa out.png

RET4_HUMAN

Octopus RET4 HUMAN out.png

INSL5_HUMAN

Octopus INSL5 human out.png

LAMP1_HUMAN

Octopus LAMP1 HUMAN out.png

A4_HUMAN

Octopus a4 human out.png

SPOCTOPUS

Details of the method

Author: Viklund H, Bernsel A, Skwark M, Elofsson A.

Year: 2008

Reference: Bioinformatics

Description

SPOCTOPUS works the same way as OCTOPUS does. The only difference is that it includes a signal peptide prediction.

Predicted features

Predicted features are signal peptide, inside/outside (i/o), transmembrane (M), TM hairpin (H), reentrant (R) or membrane dip (D)


Required information for the prediction

Only the amino acid sequence of the query protein is required as input.

Execution

Octopus pah in.png

Results and discussion

PAH

Spoctopus pah out.png

BACR_HALSA

Spoctupus bacr halsa out.png

RET4_HUMAN

Spoctopus RET4 HUMAN out.png

INSL5_HUMAN

Spoctopus INSL5 human out.png

LAMP1_HUMAN

Spoctopus LAMP1 HUMAN out.png

A4_HUMAN

Spoctopus a4 human out.png

SignalP

Details of the method

Author: Henrik Nielsen, Jacob Engelbrecht, Søren Brunak and Gunnar von Heijne.

Year: 1997

Reference: PubMed

Description

This predictor takes two methods into account the first method used is a neural network the second is a hidden markov model.

There are two neural networks one which is predicting whether the first n amino acids belong to a signal peptide and the second network predicts the exact cleavage side positon.

In a later version of SignalP a hidden markov model (HMM) has been also build to predict signal peptides. However, this prediction is completely independent from the neural network prediction. This HMM models the N-terminal region of a signal peptide as well as the surrounding cleavage site.

Predicted features

Predicts the presence of signal peptidase I cleavage sites and whether the first n residues belong to a signal peptide.

Required information for the prediction

The amino acid sequence of the protein and whether this protein is from a eukaryote, gram-negative bacteria or gram-positive bacteria.

Execution

Before we could execute SignalP on our virtual machine we had to change the path of the signalp file to /apps/signalp-3.0

Then we executed for each protein the following commands:

  • signalp -format short -t euk PAH.fa > task_33/signalp_pah_out
  • signalp -format short -t euk A4_HUMAN.fa > task_33/signalp_a4_human_out
  • signalp -format short -t gram- BACR_HALSA.fa > task_33/signalp_bacr_halsa_out
  • signalp -format short -t euk LAMP1_HUMAN.fa > task_33/signalp_lamp1_human_out
  • signalp -format short -t euk RET4_HUMAN.fa > task_33/signalp_ret4_human_out
  • signalp -format short -t euk INSL5_HUMAN.fa > task_33/signalp_insl5_human_out

Results and discussion

PAH

SignalP predicted in both methods (HMM and NN) that there is no cleavage site for an signal peptide.

BACR_HALSA

SignalP predicted in both methods (HMM and NN) that there is no cleavage site for an signal peptide.

RET4_HUMAN
INSL5_HUMAN

SignalP predicted in both methods (HMM and NN) the cleavage site at positions 23.

LAMP1_HUMAN

SignalP predicted in both methods (HMM and NN) the cleavage site at positions 29.

A4_HUMAN

SignalP predicted in both methods (HMM and NN) the cleavage site at positions 18.

TargetP

Details of the method

Author: Henrik Nielsen, Jacob Engelbrecht, Søren Brunak and Gunnar von Heijne.

Year: 1997

Reference: PubMed

Description
Architecture of TargetP Disclaimer: This file is redistributed from Emanuelsson O, Nielsen H, Brunak S, von Heijne G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol. 2000 Jul 21;300(4):1005-16.] . All rights belong to the creator.

TargetP's prediction are based on trained neural networks. These neural networks are build up in a two layer setup. The first layer consists of three neural networks which are used to predict whether it is a chloroplast targeting sequence, a mitochondrial targeting sequence or a signal peptide. The output of this first layer is then used in the second layer neural network as input to make the final prediction. Then the decision unit decides whether the cutoffs are obeyed. The output is then one of three classes cTP/mTP/SP/other and a reliability class value (RC) which is an indicator for the predictions certainty.

However, if a non-plant protein is entered the prediction for cTP is not applied for obvious reasons.

Predicted features

Predicts the localization to the following targets: chloroplast, mitochondrion, ER/golgi/secreted, and "other".

Required information for the prediction

The amino acid sequence of the protein and whether this protein is from a plant or non-plant organism.

Execution

Targetp in.png

Results and discussion

Targetp out.png

PAH
BACR_HALSA
RET4_HUMAN
INSL5_HUMAN
LAMP1_HUMAN
A4_HUMAN

Task 3.4: Prediction of GO terms

Annotated sequence features

PAH

The phenylalanine-4-hydroxylase has the following annotated GO terms:

Class GO Identifier GO Name
Function GO:0003824 catalytic activity
Function GO:0004497 monooxygenase activity
Function GO:0004505 phenylalanine 4-monooxygenase activity
Function GO:0005506 iron ion binding
Component GO:0005829 cytosol
Process GO:0006558 L-phenylalanine metabolic process
Process GO:0006559 L-phenylalanine catabolic process
Process GO:0006571 tyrosine biosynthetic process
Process GO:0008152 metabolic process
Process GO:0008652 cellular amino acid biosynthetic process
Process GO:0009072 aromatic amino acid family metabolic process
Function GO:0016491 oxidoreductase activity
Function GO:0016597 amino acid binding
Function GO:0016714 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, reduced pteridine as one donor, and incorporation of one atom of oxygen
Process GO:0018126 protein hydroxylation
Process GO:0034641 cellular nitrogen compound metabolic process
Process GO:0042136 neurotransmitter biosynthetic process
Process GO:0042423 catecholamine biosynthetic process
Process GO:0042558 pteridine-containing compound metabolic process
Function GO:0042803 protein homodimerization activity
Process GO:0046146 tetrahydrobiopterin metabolic process
Function GO:0046872 metal ion binding
Function GO:0048037 cofactor binding
Process GO:0055114 oxidation-reduction process

BACR_HALSA

The bacteriorhodopsin has the following annotated GO terms:

Class GO Identifier GO Name
Function GO:0004872 receptor activity
Function GO:0005216 ion channel activity
Component GO:0005886 plasma membrane
Process GO:0006810 transport
Process GO:0006811 ion transport
Process GO:0007602 phototransduction
Function GO:0009881 photoreceptor activity
Process GO:0015992 proton transport
Component GO:0016020 membrane
Component GO:0016021 integral to membrane
Process GO:0018298 protein-chromophore linkage
Process GO:0050896 response to stimulus

RET4_HUMAN

The retinol-binding protein 4 has the following annotated GO terms:

Class GO Identifier GO Name
Process GO:0001654 eye development
Function GO:0005215 transporter activity
Function GO:0005488 binding
Function GO:0005501 retinoid binding
Function GO:0005515 protein binding
Component GO:0005576 extracellular region
Component GO:0005615 extracellular space
Process GO:0006094 gluconeogenesis
Process GO:0006810 transport
Process GO:0007283 spermatogenesis
Process GO:0007507 heart development
Process GO:0007601 visual perception
Process GO:0008584 male gonad development
Process GO:0009790 embryo development
Function GO:0016918 retinal binding
Function GO:0019841 retinol binding
Process GO:0030277 maintenance of gastrointestinal epithelium
Process GO:0030324 lung development
Process GO:0032024 positive regulation of insulin secretion
Process GO:0032526 response to retinoic acid
Process GO:0032868 response to insulin stimulus
Function GO:0034632 retinol transporter activity
Process GO:0034633 retinol transport
Process GO:0042572 retinol metabolic process
Process GO:0042574 retinal metabolic process
Process GO:0042593 glucose homeostasis
Process GO:0045471 response to ethanol
Process GO:0048562 embryonic organ morphogenesis
Process GO:0048706 embryonic skeletal system development
Process GO:0048738 cardiac muscle tissue development
Process GO:0048807 female genitalia morphogenesis
Process GO:0050896 response to stimulus
Process GO:0050908 detection of light stimulus involved in visual perception
Process GO:0051024 positive regulation of immunoglobulin secretion
Process GO:0060041 retina development in camera-type eye
Process GO:0060044 negative regulation of cardiac muscle cell proliferation
Process GO:0060059 embryonic retina morphogenesis in camera-type eye
Process GO:0060065 uterus development
Process GO:0060068 vagina development
Process GO:0060157 urinary bladder development
Process GO:0060347 heart trabecula formation

INSL5_HUMAN

The insulin-like peptide INSL5 has the following annotated GO terms:

Class GO Identifier GO Name
Function GO:0005179 hormone activity
Component GO:0005575 cellular_component
Component GO:0005576 extracellular region
Process GO:0008150 biological_process

LAMP1_HUMAN

The lysosome-associated membrane glycoprotein 1 has the following annotated GO terms:

Class GO Identifier GO Name
Component GO:0005624 membrane fraction
Component GO:0005764 lysosome
Component GO:0005765 lysosomal membrane
Component GO:0005768 endosome
Component GO:0005770 late endosome
Component GO:0005771 multivesicular body
Component GO:0005886 plasma membrane
Component GO:0005887 integral to plasma membrane
Process GO:0006914 autophagy
Component GO:0009897 external side of plasma membrane
Component GO:0009986 cell surface
Component GO:0010008 endosome membrane
Component GO:0016020 membrane
Component GO:0016021 integral to membrane
Component GO:0031982 vesicle
Component GO:0042383 sarcolemma
Component GO:0042470 melanosome

A4_HUMAN

The amyloid beta A4 protein has the following annotated GO terms:

Class GO Identifier GO Name
Process GO:0000085 G2 phase of mitotic cell cycle
Process GO:0001967 suckling behavior
Process GO:0002576 platelet degranulation
Function GO:0003677 DNA binding
Function GO:0004867 serine-type endopeptidase inhibitor activity
Function GO:0005102 receptor binding
Function GO:0005488 binding
Function GO:0005515 protein binding
Component GO:0005576 extracellular region
Component GO:0005624 membrane fraction
Component GO:0005737 cytoplasm
Component GO:0005794 Golgi apparatus
Component GO:0005886 plasma membrane
Component GO:0005887 integral to plasma membrane
Component GO:0005905 coated pit
Process GO:0006378 mRNA polyadenylation
Process GO:0006417 regulation of translation
Process GO:0006468 protein phosphorylation
Process GO:0006878 cellular copper ion homeostasis
Process GO:0006897 endocytosis
Process GO:0006915 apoptosis
Process GO:0006917 induction of apoptosis
Process GO:0007155 cell adhesion
Process GO:0007176 regulation of epidermal growth factor receptor activity
Process GO:0007219 Notch signaling pathway
Process GO:0007409 axonogenesis
Process GO:0007596 blood coagulation
Process GO:0007617 mating behavior
Process GO:0007626 locomotory behavior
Process GO:0008088 axon cargo transport
Function GO:0008201 heparin binding
Process GO:0008219 cell death
Process GO:0008344 adult locomotory behavior
Process GO:0008542 visual learning
Component GO:0009986 cell surface
Process GO:0010466 negative regulation of peptidase activity
Process GO:0010952 positive regulation of peptidase activity
Component GO:0016020 membrane
Component GO:0016021 integral to membrane
Process GO:0016199 axon midline choice point recognition
Process GO:0016322 neuron remodeling
Process GO:0016358 dendrite development
Function GO:0016504 peptidase activator activity
Component GO:0019717 synaptosome
Process GO:0030168 platelet activation
Process GO:0030198 extracellular matrix organization
Function GO:0030414 peptidase inhibitor activity
Component GO:0030424 axon
Process GO:0030900 forebrain development
Component GO:0031093 platelet alpha granule lumen
Process GO:0031175 neuron projection development
Component GO:0031410 cytoplasmic vesicle
Component GO:0031594 neuromuscular junction
Function GO:0033130 acetylcholine receptor binding
Process GO:0035235 ionotropic glutamate receptor signaling pathway
Component GO:0035253 ciliary rootlet
Process GO:0040014 regulation of multicellular organism growth
Function GO:0042802 identical protein binding
Component GO:0043005 neuron projection
Component GO:0043197 dendritic spine
Component GO:0043198 dendritic shaft
Component GO:0043231 intracellular membrane-bounded organelle
Process GO:0045087 innate immune response
Component GO:0045177 apical part of cell
Component GO:0045202 synapse
Process GO:0045665 negative regulation of neuron differentiation
Process GO:0045931 positive regulation of mitotic cell cycle
Process GO:0045944 positive regulation of transcription from RNA polymerase II promoter
Function GO:0046872 metal ion binding
Component GO:0048471 perinuclear region of cytoplasm
Process GO:0048669 collateral sprouting in absence of injury
Process GO:0050803 regulation of synapse structure and activity
Process GO:0050885 neuromuscular process controlling balance
Process GO:0051124 synaptic growth at neuromuscular junction
Component GO:0051233 spindle midzone
Process GO:0051402 neuron apoptosis
Function GO:0051425 PTB domain binding
Process GO:0051563 smooth endoplasmic reticulum calcium ion homeostasis

GOPET

Details of the method

Author: Vinayagam A, König R, Moormann J, Schubert F, Eils R, Glatting KH, Suhai S

Year: 2004

Reference: PubMed

Description
Flowchart of GOPET Disclaimer: This file is redistributed from [Vinayagam A, del Val C, Schubert F, Eils R, Glatting KH, Suhai S, König R. GOPET: a tool for automated predictions of Gene Ontology terms. BMC Bioinformatics. 2006 Mar 20;7:161.] . All rights belong to the creator.

The prediction of GO terms is based on support vector machine (SVM) predictions. The training of this SVM was done with 39,740 selected GO-annotated cDNA sequences. For each of this training sequence they extract all annotated GO terms. In a next step they search for homologous sequences with blast with a e-value < 0.01. Sequences which fulfill this condition are used to extract attributes: including sequence similarity meas- ures, such as e-value, bitscore, identity, coverage score, alignment length, GO-term frequency, GO-term relationships between homologues, the level of annotation within the GO hierarchy and annotation quality of the homologues.

These attributes are then assigned to each GO term found in the training sequence. The training of the SVM is then done by taking the GO term and its associated attributes to train the SVM.

After the training the SVM is capable to predict GO terms from unknown cDNA or protein sequences in the same fashion.


Predicted features

GOPET predicts the GO term together with a confidence value.

Required information for the prediction

The cDNA or amino acid sequence of the protein is required.

Execution

Gopet all in.png


Results and discussion

PAH

Gopet pah out.png

BACR_HALSA

Gopet bacr halsa out.png

RET4_HUMAN

Gopet ret4 human out.png

INSL5_HUMAN

Gopet insl5 human out.png

LAMP1_HUMAN

Gopet lamp1 human out.png

A4_HUMAN

Gopet a4 human out.png

Pfam

Details of the method

Author: Wellcome Trust Sanger Institute and Howard Hughes Janelia Farm Research Campus

Year: latest release in March 2011

Reference: Oxford Journals

Description

Pfam is a protein family sequence database. In order to build families a seed sequence alignment of homologous sequences is build which all belong to the same family. This alignment is then used to build a profile hidden markov model (HMM) which is then represent one family. These profile HMM can then be used to search in your query sequence or in sequence database for significant family matches. The tool used to do all this is HMMER3.

Predicted features

Pfam predicts protein families.

Required information for the prediction

The amino acid sequence of the protein.

Execution

Pfam all in.png


Results and discussion

Pfam all out.png


PAH
BACR_HALSA
RET4_HUMAN
INSL5_HUMAN
LAMP1_HUMAN
A4_HUMAN

ProtFun 2.2

Details of the method

Author: L. Juhl Jensen, R. Gupta, N. Blom, D. Devos, J. Tamames, C. Kesmir, H. Nielsen, H. H. Stærfeldt, K. Rapacki, C. Workman, C. A. F. Andersen, S. Knudsen, A. Krogh, A. Valencia and S. Brunak.

Year: 2002

Reference: PubMed

Description

The prediction of GO terms is based on a neural network. The training sequence set was obtained from looking for protein families and their assigned GO terms in the InterPro database and then mapping these InterPro domain matches to SWISS-PROT and TrEMBL to get the actual sequence information. In order to avoid over-fitting a homology reduction was performed afterwards. Then a set of 16 features for each sequence was derived which include features such as propeptide cleavage site predictions and subcellular compartment predictions from TargetP.

Then the training to the neural network was applied to find out the best weight for each feature and GO term. However, after extensive training they figured out that the method gives only reliable predictions to 14 GO categories and thus only these were selected to be predicted by the neural network.


Predicted features

ProtFun predicts the cellular role, whether the protein is a enzyme or not, the enzyme class and the Gene ontology category. The predicted gene ontology categories are :

  • Signal transducer
  • Receptor
  • Hormone
  • Structural protein
  • Transporter
  • Ion channel
  • Voltage-gated ion channel
  • Cation channel
  • Transcription
  • Transcription regulation
  • Stress response
  • Immune response
  • Growth factor
  • Metal ion transport


Required information for the prediction

Only the amino acid sequence of the protein is required.

Execution

Protfun all in.png


Results and discussion

PAH

Protfun pah out.png

BACR_HALSA

Protfun bacr halsa out.png

RET4_HUMAN

Protfun ret4 human out.png

INSL5_HUMAN

Protfun insl5 human out.png

LAMP1_HUMAN

Protfun lamp1 human out.png

A4_HUMAN

Protfun a4 human out.png