1 - 28 of 28 results

partiFold TMB

Investigates the folding landscape of TMBs (transmembrane b-barrels). PartiFold TMB estimates inter-beta-strand residue interaction probabilities and foresees contacts and per-residue X-ray crystal structure B-values. It also try conformations from the Boltzmann low energy ensemble. This tool allows to tackle a variety of structural prediction problems which were previously addressed by independent algorithms. It uses a model which can generating predictions for a broad range of molecular properties.

GeTFEP / General Transfer Free Energy Profile

Provides a derived method from a non-redundant set of β-barrel transmembrane proteins (TMBs). GeTFEP reflects the energetic cost of transferring an amino acid side-chain into certain depth of membrane within a general transmembrane protein (TMP) architecture. This transfer free energy profile is general and is applicable to α-helical transmembrane proteins (TMHs). It provides insights into the membrane insertion of TMBs, and can be used to predict functional and structural interesting sites of TMBs.

TMB-TFE / computation of Transfer Free Energies of TransMembrane Beta-barrel proteins

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.

BOMP / Beta-barrel Outer Membrane protein Predictor

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.

PredαTM and PredβTM / Transmembrane Region Predictors

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.

EnPPIpred / Enteropathogen Protein Protein Interactions Prediction

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).


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.


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.

MPEx / Membrane Protein Explorer

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.

BTMX / Beta barrel TransMembrane eXposure

Predicts the structural topology of transmembrane beta barrels (TMBs) by employing only the frequency profile of the amino acids in a given sequence. BTMX is a computational method based on a hidden Markov model (HMM). In contrast to the existing methods, this method predicts the exposure status of the residues in the membrane region and employs evolutionary information in the form of frequency profiles. TMBHMM can be used as a complementary tool to annotate those putative TMBs.


A k-nearest neighbor (K-NN) method for discriminating transmembrane beta-barrel (TMB) and non-TMB proteins. We start with a method that makes predictions based on a distance computed from residue composition and gradually improve the prediction performance by including homologous sequences and searching for a set of residues and di-peptides for calculating the distance. The final method achieves an accuracy of 97.1%, with 0.876 MCC, 86.4% sensitivity and 98.8% specificity.