Determining Peptide Partitioning Properties via Computer Simulation

The transfer of polypeptide segments into lipid bilayers to form transmembrane helices represents the crucial first step in cellular membrane protein folding and assembly. This process is driven by complex and poorly understood atomic interactions of peptides with the lipid bilayer environment. The lack of suitable experimental techniques that can resolve these processes both at atomic resolution and nanosecond timescales has spurred the development of computational techniques. In this review, we summarize the significant progress achieved in the last few years in elucidating the partitioning of peptides into lipid bilayer membranes using atomic detail molecular dynamics simulations. Indeed, partitioning simulations can now provide a wealth of structural and dynamic information. Furthermore, we show that peptide-induced bilayer distortions, insertion pathways, transfer free energies, and kinetic insertion barriers are now accurate enough to complement experiments. Further advances in simulation methods and force field parameter accuracy promise to turn molecular dynamics simulations into a powerful tool for investigating a wide range of membrane active peptide phenomena

Discrimination of outer membrane proteins with improved performance

<p>Abstract</p> <p>Background</p> <p>Outer membrane proteins (OMPs) perform diverse functional roles in Gram-negative bacteria. Identification of outer membrane proteins is an important task.</p> <p>Results</p> <p>This paper presents a method for distinguishing outer membrane proteins (OMPs) from non-OMPs (that is, globular proteins and inner membrane proteins (IMPs)). First, we calculated the average residue compositions of OMPs, globular proteins and IMPs separately using a training set. Then for each protein from the test set, its distances to the three groups were calculated based on residue composition using a weighted Euclidean distance (WED) approach. Proteins from the test set were classified into OMP versus non-OMP classes based on the least distance. The proposed method can distinguish between OMPs and non-OMPs with 91.0% accuracy and 0.639 Matthews correlation coefficient (MCC). We then improved the method by including homologous sequences into the calculation of residue composition and using a feature-selection method to select the single residue and di-peptides that were useful for OMP prediction. The final method achieves an accuracy of 96.8% with 0.859 MCC. In direct comparisons, the proposed method outperforms previously published methods.</p> <p>Conclusion</p> <p>The proposed method can identify OMPs with improved performance. It will be very helpful to the discovery of OMPs in a genome scale.</p

Structure of the NheA Component of the Nhe Toxin from Bacillus cereus: Implications for Function

The structure of NheA, a component of the Bacillus cereus Nhe tripartite toxin, has been solved at 2.05 Å resolution using selenomethionine multiple-wavelength anomalous dispersion (MAD). The structure shows it to have a fold that is similar to the Bacillus cereus Hbl-B and E. coli ClyA toxins, and it is therefore a member of the ClyA superfamily of α-helical pore forming toxins (α-PFTs), although its head domain is significantly enlarged compared with those of ClyA or Hbl-B. The hydrophobic β-hairpin structure that is a characteristic of these toxins is replaced by an amphipathic β-hairpin connected to the main structure via a β-latch that is reminiscent of a similar structure in the β-PFT Staphylococcus aureus α-hemolysin. Taken together these results suggest that, although it is a member of an archetypal α-PFT family of toxins, NheA may be capable of forming a β rather than an α pore

Spontaneous charged lipid transfer between lipid vesicles

An assay to study the spontaneous charged lipid transfer between lipid vesicles is described. A donor/acceptor vesicle system is employed, where neutrally charged acceptor vesicles are fluorescently labelled with the electrostatic membrane probe Fluoresceinphosphatidylethanolamine (FPE). Upon addition of charged donor vesicles, transfer of negatively charged lipid occurs, resulting in a fluorescently detectable change in the membrane potential of the acceptor vesicles. Using this approach we have studied the transfer properties of a range of lipids, varying both the headgroup and the chain length. At the low vesicle concentrations chosen, the transfer follows a first-order process where lipid monomers are transferred presumably through the aqueous solution phase from donor to acceptor vesicle. The rate of transfer decreases with increasing chain length which is consistent with energy models previously reported for lipid monomer vesicle interactions. Our assay improves on existing methods allowing the study of a range of unmodified lipids, continuous monitoring of transfer and simplified experimental procedures

MemBrain: Improving the Accuracy of Predicting Transmembrane Helices

Prediction of transmembrane helices (TMH) in α helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available. Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches. While these predictors perform reasonably well in identifying the number of TMHs in a protein, they are generally inaccurate in predicting the ends of TMHs, or TMHs of unusual length. To improve the accuracy of TMH detection, we developed a machine-learning based predictor, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold. MemBrain demonstrates an overall improvement of about 20% in prediction accuracy, particularly, in predicting the ends of TMHs and TMHs that are shorter than 15 residues. It also has the capability to detect N-terminal signal peptides. The MemBrain predictor is a useful sequence-based analysis tool for functional and structural characterization of helical membrane proteins; it is freely available at http://chou.med.harvard.edu/bioinf/MemBrain/

Membrane Partitioning: “Classical” and “Nonclassical” Hydrophobic Effects

The free energy of transfer of nonpolar solutes from water to lipid bilayers is often dominated by a large negative enthalpy rather than the large positive entropy expected from the hydrophobic effect. This common observation has led to the idea that membrane partitioning is driven by the “nonclassical” hydrophobic effect. We examined this phenomenon by characterizing the partitioning of the well-studied peptide melittin using isothermal titration calorimetry (ITC) and circular dichroism (CD). We studied the temperature dependence of the entropic (−TΔS) and enthalpic (ΔH) components of free energy (ΔG) of partitioning of melittin into lipid membranes made of various mixtures of zwitterionic and anionic lipids. We found significant variations of the entropic and enthalpic components with temperature, lipid composition and vesicle size but only small changes in ΔG (entropy–enthalpy compensation). The heat capacity associated with partitioning had a large negative value of about −0.5 kcal mol−1 K−1. This hallmark of the hydrophobic effect was found to be independent of lipid composition. The measured heat capacity values were used to calculate the hydrophobic-effect free energy ΔGhΦ, which we found to dominate melittin partitioning regardless of lipid composition. In the case of anionic membranes, additional free energy comes from coulombic attraction, which is characterized by a small effective peptide charge due to the lack of additivity of hydrophobic and electrostatic interactions in membrane interfaces [Ladokhin and White J Mol Biol 309:543–552, 2001]. Our results suggest that there is no need for a special effect—the nonclassical hydrophobic effect—to describe partitioning into lipid bilayers

Arginine in Membranes: The Connection Between Molecular Dynamics Simulations and Translocon-Mediated Insertion Experiments

Several laboratories have carried out molecular dynamics (MD) simulations of arginine interactions with lipid bilayers and found that the energetic cost of placing arginine in lipid bilayers is an order of magnitude greater than observed in molecular biology experiments in which Arg-containing transmembrane helices are inserted across the endoplasmic reticulum membrane by the Sec61 translocon. We attempt here to reconcile the results of the two approaches. We first present MD simulations of guanidinium groups alone in lipid bilayers, and then, to mimic the molecular biology experiments, we present simulations of hydrophobic helices containing single Arg residues at different positions along the helix. We discuss the simulation results in the context of molecular biology results and show that the energetic discrepancy is reduced, but not eliminated, by considering free energy differences between Arg at the interface and at the center of the model helices. The reduction occurs because Arg snorkeling to the interface prevents Arg from residing in the bilayer center where the energetic cost of desolvation is highest. We then show that the problem with MD simulations is that they measure water-to-bilayer free energies, whereas the molecular biology experiments measure the energetics of partitioning from translocon to bilayer, which raises the fundamental question of the relationship between water-to-bilayer and water-to-translocon partitioning. We present two thermodynamic scenarios as a foundation for reconciliation of the simulation and molecular biology results. The simplest scenario is that translocon-to-bilayer partitioning is independent of water-to-bilayer partitioning; there is no thermodynamic cycle connecting the two paths

Cryo-EM structure of lysenin pore elucidates membrane insertion by an aerolysin family protein

Lysenin from the coelomic fluid of the earthworm Eisenia fetida belongs to the aerolysin family of small β-pore-forming toxins (β-PFTs), some members of which are pathogenic to humans and animals. Despite efforts, a high-resolution structure of a channel for this family of proteins has been elusive and therefore the mechanism of activation and membrane insertion remains unclear. Here we determine the pore structure of lysenin by single particle cryo-EM, to 3.1 Å resolution. The nonameric assembly reveals a long β-barrel channel spanning the length of the complex that, unexpectedly, includes the two pre-insertion strands flanking the hypothetical membrane-insertion loop. Examination of other members of the aerolysin family reveals high structural preservation in this region, indicating that the membrane-insertion pathway in this family is conserved. For some toxins, proteolytic activation and pro-peptide removal will facilitate unfolding of the pre-insertion strands, allowing them to form the β-barrel of the channel

MtSNPscore: a combined evidence approach for assessing cumulative impact of mitochondrial variations in disease

Human mitochondrial DNA (mtDNA) variations have been implicated in a broad spectrum of diseases. With over 3000 mtDNA variations reported across databases, establishing pathogenicity of variations in mtDNA is a major challenge. We have designed and developed a comprehensive weighted scoring system (MtSNPscore) for identification of mtDNA variations that can impact pathogenicity and would likely be associated with disease. The criteria for pathogenicity include information available in the literature, predictions made by various in silico tools and frequency of variation in normal and patient datasets. The scoring scheme also assigns scores to patients and normal individuals to estimate the cumulative impact of variations. The method has been implemented in an automated pipeline and has been tested on Indian ataxia dataset (92 individuals), sequenced in this study, and other publicly available mtSNP dataset comprising of 576 mitochondrial genomes of Japanese individuals from six different groups, namely, patients with Parkinson's disease, patients with Alzheimer's disease, young obese males, young non-obese males, and type-2 diabetes patients with or without severe vascular involvement. MtSNPscore, for analysis can extract information from variation data or from mitochondrial DNA sequences. It has a web-interface http://bioinformatics.ccmb.res.in/cgi-bin/snpscore/Mtsnpscore.pl webcite that provides flexibility to update/modify the parameters for estimating pathogenicity