Dataset Information

TMM@: a web application for the analysis of transmembrane helix mobility.

ABSTRACT: BACKGROUND: To understand the mechanism by which a protein transmits a signal through the cell membrane, an understanding of the flexibility of its transmembrane (TM) region is essential. Normal Mode Analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. It has been widely used to study transmembrane channels and pumps. It relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant. In particular NMA can be used to study dynamics of TM regions, but no tool making this approach available for non-experts, has been available so far. RESULTS: We developed the web-application TMM@ (TransMembrane alpha-helical Mobility analyzer). It uses NMA to characterize the propensity of transmembrane alpha-helices to be displaced. Starting from a structure file at the PDB format, the server computes the normal modes of the protein and identifies which helices in the bundle are the most mobile. Each analysis is performed independently from the others and results can be visualized using only a web browser. No additional plug-in or software is required. For users who would like to further analyze the output data with their favourite software, raw results can also be downloaded. CONCLUSION: We built a novel and unique tool, TMM@, to study the mobility of transmembrane alpha-helices. The tool can be applied to for example membrane transporters and provides biologists studying transmembrane proteins with an approach to investigate which alpha-helices are likely to undergo the largest displacements, and hence which helices are most likely to be involved in the transportation of molecules in and out of the cell.

SUBMITTER: Skjaerven L

PROVIDER: S-EPMC1949839 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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TMM@: a web application for the analysis of transmembrane helix mobility.

Skjaerven Lars L Jonassen Inge I Reuter Nathalie N

BMC bioinformatics 20070702

<h4>Background</h4>To understand the mechanism by which a protein transmits a signal through the cell membrane, an understanding of the flexibility of its transmembrane (TM) region is essential. Normal Mode Analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. It has been widely used to study transmembrane channels and pumps. It relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) d ...[more]

PMID: 17601351

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Project description:We have previously studied the unfolding equilibrium of bacterioopsin in a single phase solvent, using Förster mechanism fluorescence resonance energy transfer (FRET) as a probe, from tryptophan donors to a dansyl acceptor. We observed an apparent unfolding transition in bacterioopsin perturbed by increasing ethanol concentrations [Nannepaga et al. (2004) Biochemistry 43, 50-59]. We have further investigated this transition and find that the unfolding is pH-dependent. We have now measured the apparent pK of acid-induced unfolding of bacterioopsin in 90% ethanol. When the acceptor is on helix B (Lys 41), the apparent pK for unfolding is 4.75; on the EF connecting loop (Cys 163), 5.15; and on helix G (Cys 222), 5.75. Five-helix proteolytic fragments are less stable. The apparent unfolding pKs are 5.46 for residues 72-248 (Cys 163) and 7.36 for residues 1-166 (Lys 41). When interpreted in terms of a simple equilibrium model for unfolding, the apparent pKs give relative free energies of unfolding in the range of -0.54 to -3.5 kcal/mol. The results suggest that the C-terminal helix of bacterioopsin is less stably folded than the N-terminal helices. We analyzed the pairwise helix-helix interaction surfaces of bacteriorhodopsin and three other seven-transmembrane-helix proteins on the basis of crystal structures. The results show that the interaction surfaces are smoother and the helix axis separations are closer in the amino-terminal two-thirds of the proteins compared with the carboxyl-terminal one-third. However, the F helix is important in stabilizing the folded structure, as shown by the instability of the 1-166 fragment. Considering the high-resolution crystal structure of bacteriorhodopsin, there are no obvious helix-helix interactions involving protein side chains which would be destabilized by protonation at the estimated pH of the unfolding transitions. However, a number of helix-bridging water molecules could become protonated, thereby weakening the helix-helix interactions.

Project description:Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology "cytoplasmic-transmembrane-extracellular" and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families.Here, we describe a new method "TMpro" to predict TM helices with high accuracy. To avoid overfitting to existing topologies, we have collapsed cytoplasmic and extracellular labels to a single state, non-TM. TMpro is a binary classifier which predicts TM or non-TM using multiple amino acid properties (charge, polarity, aromaticity, size and electronic properties) as features. The features are extracted from sequence information by applying the framework used for latent semantic analysis of text documents and are input to neural networks that learn the distinction between TM and non-TM segments. The model uses only 25 free parameters. In benchmark analysis TMpro achieves 95% segment F-score corresponding to 50% reduction in error rate compared to the best methods not requiring an evolutionary profile of a protein to be known. Performance is also improved when applied to more recent and larger high resolution datasets PDBTM and MPtopo. TMpro predictions in membrane proteins with unusual or disputed TM structure (K+ channel, aquaporin and HIV envelope glycoprotein) are discussed.TMpro uses very few free parameters in modeling TM segments as opposed to the very large number of free parameters used in state-of-the-art membrane prediction methods, yet achieves very high segment accuracies. This is highly advantageous considering that high resolution transmembrane information is available only for very few proteins. The greatest impact of TMpro is therefore expected in the prediction of TM segments in proteins with novel topologies. Further, the paper introduces a novel method of extracting features from protein sequence, namely that of latent semantic analysis model. The success of this approach in the current context suggests that it can find potential applications in other sequence-based analysis problems.http://linzer.blm.cs.cmu.edu/tmpro/ and http://flan.blm.cs.cmu.edu/tmpro/

Dataset Information

TMM@: a web application for the analysis of transmembrane helix mobility.

Publications

TMM@: a web application for the analysis of transmembrane helix mobility.

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