Transmembrane beta barrel detection software tools | Membrane protein data analysis
Transmembrane beta barrels are membrane proteins found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Transmembrane beta barrel prediction tools use amino acid sequences of protein and algorithms for prediction.
Predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. PROFtmb was tested on a representative set of known TMB and non-TMB proteins. It detects 50% of TMBs at 80% accuracy (z-score>=10) and 70% of TMBs at 35% accuracy (z-score >= 6). The tool uses a Hidden Markov Model (HMM) whose parameters are trained on a set of labelled sequence profiles, and which accepts sequence profiles as input for prediction.
A web server 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.
A program that uses a modified k-nearest neighbour (k-NN) algorithm to classify protein sequences as transmembrane beta-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.
A program that predicts whether or not a polypeptide sequence from a Gram-negative bacterium is an integral beta-barrel outer membrane protein. BOMP is based on two separate components to recognize integral beta-barrel proteins. The first component is a C-terminal pattern typical of many integral beta-barrel proteins. The second component calculates an integral beta-barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane beta-strands.
Predicts ß-turns in a protein from the amino acid sequence. It allows the user to predict turns in a protein using existing statistical algorithms, to predict the type of ß-turn such as Type I, I', II, II', VI, VIII and non-specific i.e., advanced prediction and to predict the consensus ß-turn in a protein.
Produces homology models using alignment and coordinate generation software that has been designed specifically for transmembrane proteins. Memoir includes a set of alternative conformations for each modelled loop with a multiple sequence alignment and several types of membrane proteins as α-helical and β-barrel. Memoir’s results include supplementary information that could be used in manual model re-finement.
Calculates Transfer free energies (TFEs) of transmembrane (TM) residues in Transmembrane beta-barrel proteins (TMBs) accurately, with which depth-dependent transfer free energy profiles can be derived. TMB-TFE is efficient and applicable to all bacterial TMBs regardless of the size of the protein. It does not require knowledge of experimentally solved or computationally predicted structures, as long as the sequences of the TM region can be determined.
An unsupervised method to predict the beta-sheet topology starting from the protein sequence and its secondary structure. BCov takes advantage of the sparse inverse covariance estimation to define beta-strand partner scores. Then an optimization based on integer programming is carried out to predict the beta-sheet connectivity.
A suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), beta-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane beta-barrel proteins. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD <6.0 A for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 A, with a GDT_TS score greater than 60.0.
A modular approach to the problem of predicting/assembling protein β-sheets in a chain. BETApro provides an integrated prediction of β-sheet architectures by predicting β-strand pairs, β-strand alignments and β-sheets assembly. The method can be combined with contact map prediction to generate more accurate contact maps, which in turn can be used in fold recognition and 3D reconstruction. Accurate β-residue and β-strand pairings may also provide strong constraints for improving ab initio sampling of tertiary structures and derive energy terms to help select near-native structures from decoys.
The prediction of beta-turns is an important element of protein secondary structure prediction. BetaTurns has been developed for the prediction of beta-turn types I, II, IV and VIII. For each turn type, two consecutive feed-forward back-propagation networks with a single hidden layer have been used where the first sequence-to-structure network has been trained on single sequences as well as on PSI-BLAST PSSM.
An improved method for the topology prediction of transmembrane beta barrel proteins (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.
Detects TransMembrane β-Barrel (TMBB) and predicts topology in prokaryotes. BetAware allows both prediction steps based on advanced machine-learning methods. For TMBB detection, BetAware exploits a machine learning approach based on N-to-1 Extreme Learning Machines, while TMBB topology prediction is carried-out using a probabilistic model based on Grammatical-Restrained Hidden Conditional Random Fields, a discriminative framework introduced to address sequence labelling tasks in Bioinformatics. BetAware is available online as a web server or can be downloaded for local use.
Predicts a large set of alternative topologies for a protein. TOBMODEL is an online pipeline appropriate for 3D model generation of putative transmembrane beta barrel proteins. It obtains predicted topologies from BOCTOPUS and generates a C-alpha protein data bank (PDB) file as an output. It also outputs prediction results in one text file and generates a graphical representation of the predicted topologies and svm probabilities.
Two independent algorithms, which predict the transmembrane regions of integral membrane proteins. PredαTM and PredβTM predict respectively the probable alpha helical transmembrane regions and the probable beta strand transmembrane regions present in a given protein sequence of an integral membrane protein. The underlying model is a SVM classifier trained on sequence data of transmembrane proteins with known structures. Given a protein sequence as input, the algorithms predict the probable transmembrane regions based on amino acid adjacency frequency and position specific preference of amino acids in the transmembrane regions.
Facilitates comprehensive protein sequence analysis. MESSA gathers structural and functional predictions for a protein of interest. It exploits a number of select tools to predict local sequence properties, such as secondary structure, structurally disordered regions, coiled coils, signal peptides and transmembrane helices. This application also detects homologous proteins and assigns the query to a protein family.
Assists users in identifying β-sheets in 3D structures. SheeP is available as both a web server and a standalone software which allows users to choose between five methods for the detection. It first determines secondary structure and then builds a sheet graph and a derived sheet map whose are modified and returned as an output result that contains graphical representation of maps for all detected sheets and scripts.
Detects the traces of β strands through the analysis of twist, an intrinsic nature of a β sheet. StrandTwister has two major components to (i) simplifies the voxels of a β sheet into a polynomial surface and (ii) identifies right-handed β twist from the polynomial surface model. It was tested using 39 β sheets, and the results show that this method detects the traces of β strands for major β sheets.
Provides a membrane protein explorer. MPEx provides an extendable framework for physical and biological hydropathy analyses and for b-barrel identification screening. It provides three analysis modes of operation that include (i) physical scale hydropathy analysis, (ii) translocon-scale hydropathy analysis and (iii) b-barrel analysis. It also offers two utility modes that include (i) Totalizer for estimating the binding free energies of peptides to phosphatidylcholine interfaces and (ii) Data-Buffer Overlay, which allows storage and graphical comparisons of different sliding-window plots.
A web server that predicts intra protein protein interaction of entheropathogen including Escherichia coli (E. coli), Salmonella Typhi, Shigella flexneri, Vibrio cholerae & Yersinia entrocolitica. EnPPIpred used different features for generating the models including - i) Domain-Domain Association (DDA) ii) Degree (No. of Interacting partners present in a PPIs networks) iii) Amino Acid Composition iv) Dipeptide Composition v) Hybrid Approach (DDA, degree and amino acid composition).