The posterior-Viterbi: a new decoding algorithm for hidden Markov models

Background: Hidden Markov models (HMM) are powerful machine learning tools successfully applied to problems of computational Molecular Biology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the class labeling, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the automaton grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusions: We show that PV decoding performs better than other algorithms first on toy models and then on the computational biological problem of the prediction of the topology of beta-barrel membrane proteins.Comment: 23 pages, 3 figure

A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

BACKGROUND: Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. RESULTS: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. CONCLUSION: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology

Prediction of β-barrel membrane proteins by searching for restricted domains

BACKGROUND: The identification of beta-barrel membrane proteins out of a genomic/proteomic background is one of the rapidly developing fields in bioinformatics. Our main goal is the prediction of such proteins in genome/proteome wide analyses. RESULTS: For the prediction of beta-barrel membrane proteins within prokaryotic proteomes a set of parameters was developed. We have focused on a procedure with a low false positive rate beside a procedure with lowest false prediction rate to obtain a high certainty for the predicted sequences. We demonstrate that the discrimination between beta-barrel membrane proteins and other proteins is improved by analyzing a length limited region. The developed set of parameters is applied to the proteome of E. coli and the results are compared to four other described procedures. CONCLUSION: Analyzing the beta-barrel membrane proteins revealed the presence of a defined membrane inserted beta-barrel region. This information can now be used to refine other prediction programs as well. So far, all tested programs fail to predict outer membrane proteins in the proteome of the prokaryote E. coli with high reliability. However, the reliability of the prediction is improved significantly by a combinatory approach of several programs. The consequences and usability of the developed scores are discussed

Outer membrane proteins can be simply identified using secondary structure element alignment

<p>Abstract</p> <p>Background</p> <p>Outer membrane proteins (OMPs) are frequently found in the outer membranes of gram-negative bacteria, mitochondria and chloroplasts and have been found to play diverse functional roles. Computational discrimination of OMPs from globular proteins and other types of membrane proteins is helpful to accelerate new genome annotation and drug discovery.</p> <p>Results</p> <p>Based on the observation that almost all OMPs consist of antiparallel β-strands in a barrel shape and that their secondary structure arrangements differ from those of other types of proteins, we propose a simple method called SSEA-OMP to identify OMPs using secondary structure element alignment. Through intensive benchmark experiments, the proposed SSEA-OMP method is better than some well-established OMP detection methods.</p> <p>Conclusions</p> <p>The major advantage of SSEA-OMP is its good prediction performance considering its simplicity. The web server implements the method is freely accessible at <url>http://protein.cau.edu.cn/SSEA-OMP/index.html</url>.</p

Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at

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

PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins

PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually contains a membrane barrel, and a four-state per-residue labeling of upward- and downward-facing strands, periplasmic hairpins and extracellular loops. While most users submit individual proteins known to contain TMBs, some groups submit entire proteomes to screen for potential TMBs. Response time is about 4 min for a 500-residue protein. PROFtmb is a profile-based Hidden Markov Model (HMM) with an architecture mirroring the structure of TMBs. The per-residue accuracy on the 8-fold cross-validated testing set is 86% while whole-protein discrimination accuracy was 70 at 60% coverage. The PROFtmb web server includes all source code, training data and whole-proteome predictions from 78 Gram-negative bacterial genomes and is available freely and without registration at

A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins

BACKGROUND: Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. RESULTS: In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. CONCLUSION: We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins

HHomp—prediction and classification of outer membrane proteins

Outer membrane proteins (OMPs) are the transmembrane proteins found in the outer membranes of Gram-negative bacteria, mitochondria and plastids. Most prediction methods have focused on analogous features, such as alternating hydrophobicity patterns. Here, we start from the observation that almost all β-barrel OMPs are related by common ancestry. We identify proteins as OMPs by detecting their homologous relationships to known OMPs using sequence similarity. Given an input sequence, HHomp builds a profile hidden Markov model (HMM) and compares it with an OMP database by pairwise HMM comparison, integrating OMP predictions by PROFtmb. A crucial ingredient is the OMP database, which contains profile HMMs for over 20 000 putative OMP sequences. These were collected with the exhaustive, transitive homology detection method HHsenser, starting from 23 representative OMPs in the PDB database. In a benchmark on TransportDB, HHomp detects 63.5% of the true positives before including the first false positive. This is 70% more than PROFtmb, four times more than BOMP and 10 times more than TMB-Hunt. In Escherichia coli, HHomp identifies 57 out of 59 known OMPs and correctly assigns them to their functional subgroups. HHomp can be accessed at http://toolkit.tuebingen.mpg.de/hhomp

TMBETA-GENOME: database for annotated β-barrel membrane proteins in genomic sequences

We have developed the database, TMBETA-GENOME, for annotated β-barrel membrane proteins in genomic sequences using statistical methods and machine learning algorithms. The statistical methods are based on amino acid composition, reside pair preference and motifs. In machine learning techniques, the combination of amino acid and dipeptide compositions has been used as main attributes. In addition, annotations have been made using the criterion based on the identification of β-barrel membrane proteins and exclusion of globular and transmembrane helical proteins. A web interface has been developed for identifying the annotated β-barrel membrane proteins in all known genomes. The users have the feasibility of selecting the genome from the three kingdoms of life, archaea, bacteria and eukaryote, and five different methods. Further, the statistics for all genomes have been provided along with the links to different algorithms and related databases. It is freely available at