Here is compilation of some of the commonly available tools for prediction of Beta Barrel Outer Membrane Proteins (OMPs)
1. Pred-TMBB
(2004): http://bioinformatics.biol.uoa.gr/PRED-TMBB/input.jsp
PRED-TMBB: a web server for predicting the topology of
beta-barrel outer membrane proteins. The beta-barrel outer membrane proteins
constitute one of the two known structural classes of membrane proteins.
Whereas there are several different web-based predictors for alpha-helical
membrane proteins, currently there is no freely available prediction method for
beta-barrel membrane proteins, at least with an acceptable level of accuracy.
We present here a web server (PRED-TMBB,
http://bioinformatics.biol.uoa.gr/PRED-TMBB) which is capable of predicting the
transmembrane strands and the topology of beta-barrel outer membrane proteins
of Gram-negative bacteria. The method is based on a Hidden Markov Model,
trained according to the Conditional Maximum Likelihood criterion. The model
was retrained and the training set now includes 16 non-homologous outer
membrane proteins with structures known at atomic resolution. The user may
submit one sequence at a time and has the option of choosing between three
different decoding methods. The server reports the predicted topology of a
given protein, a score indicating the probability of the protein being an outer
membrane beta-barrel protein, posterior probabilities for the transmembrane
strand prediction and a graphical representation of the assumed position of the
transmembrane strands with respect to the lipid bilayer. http://nar.oxfordjournals.org/content/32/suppl_2/W400.long
2. BOCTOPUS
(2012): http://boctopus.cbr.su.se/
BOCTOPUS: improved topology
prediction of transmembrane β barrel proteins
Transmembrane β barrel proteins (TMBs) are found in the
outer membrane of Gram-negative bacteria, chloroplast and mitochondria. They
play a major role in the translocation machinery, pore formation, membrane anchoring
and ion exchange. TMBs are also promising targets for antimicrobial drugs and
vaccines. Given the difficulty in membrane protein structure determination,
computational methods to identify TMBs and predict the topology of TMBs are
important. Results: Here, we present BOCTOPUS; an improved method for the
topology prediction of TMBs by employing a combination of support vector
machines (SVMs) and Hidden Markov Models (HMMs). The SVMs and HMMs account for
local and global residue preferences, respectively. Based on a 10-fold
cross-validation test, BOCTOPUS performs better than all existing methods,
reaching a Q3 accuracy of 87%. Further, BOCTOPUS predicted the correct number
of strands for 83% proteins in the dataset. BOCTOPUS might also help in reliable
identification of TMBs by using it as an additional filter to methods
specialized in this task. http://bioinformatics.oxfordjournals.org/content/28/4/516.long
3.
TBBpred
(2004): http://www.imtech.res.in/raghava/tbbpred/
Prediction of transmembrane regions
of β-barrel proteins using ANN- and SVM-based methods. This article describes a
method developed for predicting transmembrane β-barrel regions in membrane
proteins using machine learning techniques: artificial neural network (ANN) and
support vector machine (SVM). The ANN used in this study is a feed-forward
neural network with a standard back-propagation training algorithm. The
accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%,
when evolutionary information was added to a single sequence as a multiple
sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based
method using a primary sequence as input and achieved an accuracy of 77.4%. The
SVM model was modified by adding 36 physicochemical parameters to the amino
acid sequence information. Finally, ANN- and SVM-based methods were combined to
utilize the full potential of both techniques. The accuracy and Matthews
correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%,
80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were
trained and tested on a nonredundant data set of 16 proteins, and performance
was evaluated using “leave one out cross-validation” (LOOCV). http://onlinelibrary.wiley.com/doi/10.1002/prot.20092/abstract;jsessionid=F041C3CA2F5E53B83924D0D73D2832C7.f03t02
4.
BETAWARE
(2013): http://www.biocomp.unibo.it/~savojard/betawarecl/
BETAWARE: a machine-learning tool to detect and predict
transmembrane beta-barrel proteins in prokaryotes. The annotation of membrane
proteins in proteomes is an important problem of Computational Biology,
especially after the development of high-throughput techniques that allow fast
and efficient genome sequencing. Among membrane proteins, transmembrane β-barrels
(TMBBs) are poorly represented in the database of protein structures (PDB) and
difficult to identify with experimental approaches. They are, however,
extremely important, playing key roles in several cell functions and bacterial
pathogenicity. TMBBs are included in the lipid bilayer with a β-barrel
structure and are presently found in the outer membranes of Gram-negative
bacteria, mitochondria and chloroplasts. Recently, we developed two
top-performing methods based on machine-learning approaches to tackle both the
detection of TMBBs in sets of proteins and the prediction of their topology.
Here, we present our BETAWARE program that includes both approaches and can run
as a standalone program on a linux-based computer to easily address in-home
massive protein annotation or filtering. http://bioinformatics.oxfordjournals.org/content/29/4/504.abstract
5.
ConBBPRED
(2005): http://bioinformatics.biol.uoa.gr/ConBBPRED/index.jsp
Prediction of
the transmembrane strands and topology of β-barrel outer membrane proteins is
of interest in current bioinformatics research. Several methods have been
applied so far for this task, utilizing different algorithmic techniques and a
number of freely available predictors exist. The methods can be grossly divided
to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on
Support Vector Machines (SVMs). In this work, we compare the different
available methods for topology prediction of β-barrel outer membrane proteins.
We evaluate their performance on a non-redundant dataset of 20 β-barrel outer
membrane proteins of gram-negative bacteria, with structures known at atomic
resolution. Also, we describe, for the first time, an effective way to combine
the individual predictors, at will, to a single consensus prediction method. We
assess the statistical significance of the performance of each prediction
scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB
and PRED-TMBB, are currently the best predictors, according to either the
per-residue accuracy, the segments overlap measure (SOV) or the total number of
proteins with correctly predicted topologies in the test set. Furthermore, we
show that the available predictors perform better when only transmembrane
β-barrel domains are used for prediction, rather than the precursor full-length
sequences, even though the HMM-based predictors are not influenced
significantly. The consensus prediction method performs significantly better
than each individual available predictor, since it increases the accuracy up to
4% regarding SOV and up to 15% in correctly predicted topologies.
http://www.biomedcentral.com/1471-2105/6/7
6.
TMBETA-RBF
(2008): http://rbf.bioinfo.tw/~sachen/OMPpredict/TMBETADISC-RBF.php
TMBETA-NET: discrimination and prediction of membrane
spanning β-strands in outer membrane proteins. We have developed a web-server,
TMBETA-NET for discriminating outer membrane proteins and predicting their
membrane spanning β-strand segments. The amino acid compositions of globular
and outer membrane proteins have been systematically analyzed and a statistical
method has been proposed for discriminating outer membrane proteins. The
prediction of membrane spanning segments is mainly based on feed forward neural
network and refined with β-strand length. Our program takes the amino acid
sequence as input and displays the type of the protein along with
membrane-spanning β-strand segments as a stretch of highlighted amino acid
residues. Further, the probability of residues to be in transmembrane β-strand
has been provided with a coloring scheme. We observed that outer membrane
proteins were discriminated with an accuracy of 89% and their membrane spanning
β-strand segments at an accuracy of 73% just from amino acid sequence
information. The prediction server is available at http://psfs.cbrc.jp/tmbeta-net/
7.
TMB-HUNT
(2005): http://www.bioinformatics.leeds.ac.uk/betaBarrel/
TMB-Hunt: a web server to screen sequence sets for
transmembrane β-barrel proteins. TMB-Hunt is a program that uses a modified k-nearest
neighbour (k-NN) algorithm to classify protein sequences as
transmembrane β-barrel (TMB) or non-TMB on the basis of whole sequence amino
acid composition. By including differentially weighted amino acids,
evolutionary information and by calibrating the scoring, a discrimination
accuracy of 92.5% was achieved, as tested using a rigorous cross-validation
procedure. The TMB-Hunt web server, available at www.bioinformatics.leeds.ac.uk/betaBarrel,
allows screening of up to 10 000 sequences in a single query and provides
results and key statistics in a simple colour coded format. http://nar.oxfordjournals.org/content/33/suppl_2/W188.long
8.
TMBPro
(2008): suite of specialized predictors for predicting secondary structure,
beta-contacts, and tertiary structure of Transmembrane Beta-Barrel (TMB)
proteins. http://tmbpro.ics.uci.edu/ TMBpro:
secondary structure, β-contact and tertiary structure prediction of
transmembrane β-barrel proteins. Transmembrane β-barrel (TMB) proteins are
embedded in the outer membranes of mitochondria, Gram-negative bacteria and
chloroplasts. These proteins perform critical functions, including active
ion-transport and passive nutrient intake. Therefore, there is a need for
accurate prediction of secondary and tertiary structure of TMB proteins.
Traditional homology modeling methods, however, fail on most TMB proteins since
very few non-homologous TMB structures have been determined. Yet, because TMB
structures conform to specific construction rules that restrict the
conformational space drastically, it should be possible for methods that do not
depend on target-template homology to be applied successfully.Results: We
develop a suite (TMBpro) of specialized predictors for predicting secondary
structure (TMBpro-SS), β-contacts (TMBpro-CON) and tertiary structure
(TMBpro-3D) of transmembrane β-barrel proteins. We compare our results to the
recent state-of-the-art predictors transFold and PRED-TMBB using their
respective benchmark datasets, and leave-one-out cross-validation. Using the
transFold dataset TMBpro predicts secondary structure with per-residue accuracy
(Q2) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts
β-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB
dataset, TMBpro predicts secondary structure with Q2 of 88.3% and a correlation
coefficient of 0.75. All of these performance results exceed previously
published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro
predicts the tertiary structure of transmembrane segments with RMSD <6.0 Å
for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 Å, with a
GDT_TS score greater than 60.0. http://bioinformatics.oxfordjournals.org/content/24/4/513.long
9.
MCMBB
Markov Chain Model Beta Barrels (2004): http://athina.biol.uoa.gr/bioinformatics/mcmbb/
The task of
finding β-barrel outer membrane proteins of the gram-negative bacteria is of
greatimportance in current Bioinformatics research. We developed a
computational method, which discriminates β- barrel outer membrane proteins
from globular ones and, also, from α-helical membrane proteins. The methodis
based on a 1st order Markov Chain model, which captures the alternating pattern
of hydrophilic-hydrophobicresidues occurring in the membrane-spanning
beta-strands of beta-barrel outer membrane proteins. The modelachieves high
accuracy in discriminating outer membrane proteins, and could be used alone, or
in conjunctionwith other more sophisticated methods, already available http://www.academia.edu/316959/Finding_Beta-Barrel_Outer_Membrane_Proteins_With_a_Markov_Chain_Model
10.
TMB-KNN
(2008): http://cs.ndsu.nodak.edu/~chayan/Server/TMB_KNN.html
TMB-Hunt: a web server to screen sequence
sets for transmembrane β-barrel proteins
TMB-Hunt is a program that uses a modified k-nearest
neighbour (k-NN) algorithm to classify protein sequences as
transmembrane β-barrel (TMB) or non-TMB on the basis of whole sequence amino
acid composition. By including differentially weighted amino acids,
evolutionary information and by calibrating the scoring, a discrimination
accuracy of 92.5% was achieved, as tested using a rigorous cross-validation
procedure. The TMB-Hunt web server, available at www.bioinformatics.leeds.ac.uk/betaBarrel,
allows screening of up to 10 000 sequences in a single query and provides
results and key statistics in a simple colour coded format. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160145/
11.
transFold
(2006): super-secondary structure prediction of transmembrane β-barrel
proteins http://bioinformatics.bc.edu/clotelab/transFold/
transFold: a web server for predicting the structure and
residue contacts of transmembrane beta-barrels. Transmembrane β-barrel (TMB)
proteins are embedded in the outer membrane of Gram-negative bacteria,
mitochondria and chloroplasts. The cellular location and functional diversity
of β-barrel outer membrane proteins makes them an important protein class. At
the present time, very few non-homologous TMB structures have been determined
by X-ray diffraction because of the experimental difficulty encountered in
crystallizing transmembrane (TM) proteins. The transFold web server uses
pairwise inter-strand residue statistical potentials derived from globular
(non-outer-membrane) proteins to predict the supersecondary structure of TMB.
Unlike all previous approaches, transFold does not use machine learning methods
such as hidden Markov models or neural networks; instead, transFold employs
multi-tape S-attribute grammars to describe all potential conformations, and
then applies dynamic programming to determine the global minimum energy
supersecondary structure. The transFold web server not only predicts secondary
structure and TMB topology, but is the only method which additionally predicts
the side-chain orientation of transmembrane β-strand residues, inter-strand
residue contacts and TM β-strand inclination with respect to the membrane. The
program transFold currently outperforms all other methods for accuracy of
β-barrel structure prediction. Available at http://bioinformatics.bc.edu/clotelab/transFold.
http://nar.oxfordjournals.org/content/34/suppl_2/W189.full
12.
BOMP
(2004): http://services.cbu.uib.no/tools/bomp
BOMP: a
program to predict integral β-barrel outer membrane proteins encoded within
genomes of Gram-negative bacteria. This work describes the development of a
program that predicts whether or not a polypeptide sequence from a
Gram-negative bacterium is an integral β-barrel outer membrane protein. The
program, called the β-barrel Outer Membrane protein Predictor (BOMP), is based
on two separate components to recognize integral β-barrel proteins. The first
component is a C-terminal pattern typical of many integral β-barrel proteins.
The second component calculates an integral β-barrel score of the sequence
based on the extent to which the sequence contains stretches of amino acids
typical of transmembrane β-strands. The precision of the predictions was found
to be 80% with a recall of 88% when tested on the proteins with SwissProt annotated
subcellular localization in Escherichia coli K 12 (788
sequences) and Salmonella typhimurium (366 sequences). When
tested on the predicted proteome of E.coli, BOMP found 103 of a
total of 4346 polypeptide sequences to be possible integral β-barrel proteins.
Of these, 36 were found by BLAST to lack similarity (E-value score <
1e−10) to proteins with annotated subcellular localization in SwissProt. BOMP
predicted the content of integral β-barrels per predicted proteome of 10
different bacteria to range from 1.8 to 3%. BOMP is available at http://www.bioinfo.no/tools/bomp http://nar.oxfordjournals.org/content/32/suppl_2/W394.full
13. TMBETA-net (2004): http://psfs.cbrc.jp/tmbeta-net/
TMBETA-NET: discrimination and prediction of membrane spanning
beta-strands in outer membrane proteins. We have developed a web-server,
TMBETA-NET for discriminating outer membrane proteins and predicting their
membrane spanning beta-strand segments. The amino acid compositions of globular
and outer membrane proteins have been systematically analyzed and a statistical
method has been proposed for discriminating outer membrane proteins. The
prediction of membrane spanning segments is mainly based on feed forward neural
network and refined with beta-strand length. Our program takes the amino acid
sequence as input and displays the type of the protein along with
membrane-spanning beta-strand segments as a stretch of highlighted amino acid
residues. Further, the probability of residues to be in transmembrane
beta-strand has been provided with a coloring scheme. We observed that outer
membrane proteins were discriminated with an accuracy of 89% and their membrane
spanning beta-strand segments at an accuracy of 73% just from amino acid
sequence information. The prediction server is available at http://psfs.cbrc.jp/tmbeta-net/. http://nar.oxfordjournals.org/content/33/suppl_2/W164.long
14.
TMBB-DB
(2012): http://beta-barrel.tulane.edu/index.html
TMBB-DB: a
transmembrane β-barrel proteome database. We previously reported the
development of a highly accurate statistical algorithm for identifying β-barrel
outer membrane proteins or transmembrane β-barrels (TMBBs), from genomic
sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics, 26,
1965–1974). We have now applied this identification algorithm to all available
Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a
publicly available, searchable, up-to-date, database of all proteins in these
genomes. For each protein in the database, there is information on (i) β-barrel
membrane protein probability for identification of β-barrels, (ii) β-strand and
β-hairpin propensity for structure and topology prediction, (iii) signal
sequence score because most TMBBs are secreted through the inner membrane
translocon and, thus, have a signal sequence, and (iv) transmembrane α-helix
predictions, for reducing false positive predictions. This information is
sufficient for the accurate identification of most β-barrel membrane proteins
in these genomes. In the database there are nearly 50 000 predicted TMBBs (out
of 1.9 million total putative proteins). Of those, more than 15 000 are
‘hypothetical’ or ‘putative’ proteins, not previously identified as TMBBs. This
wealth of genomic information is not available anywhere else. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463127/
