A framework for protein and membrane interactions

We introduce the BioBeta Framework, a meta-model for both protein-level and membrane-level interactions of living cells. This formalism aims to provide a formal setting where to encode, compare and merge models at different abstraction levels; in particular, higher-level (e.g. membrane) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions. A BioBeta specification provides a protein signature together a set of protein reactions, in the spirit of the kappa-calculus. Moreover, the specification describes when a protein configuration triggers one of the only two membrane interaction allowed, that is "pinch" and "fuse". In this paper we define the syntax and semantics of BioBeta, analyse its properties, give it an interpretation as biobigraphical reactive systems, and discuss its expressivity by comparing with kappa-calculus and modelling significant examples. Notably, BioBeta has been designed after a bigraphical metamodel for the same purposes. Hence, each instance of the calculus corresponds to a bigraphical reactive system, and vice versa (almost). Therefore, we can inherith the rich theory of bigraphs, such as the automatic construction of labelled transition systems and behavioural congruences

Bigraphical models for protein and membrane interactions

We present a bigraphical framework suited for modeling biological systems both at protein level and at membrane level. We characterize formally bigraphs corresponding to biologically meaningful systems, and bigraphic rewriting rules representing biologically admissible interactions. At the protein level, these bigraphic reactive systems correspond exactly to systems of kappa-calculus. Membrane-level interactions are represented by just two general rules, whose application can be triggered by protein-level interactions in a well-de\"ined and precise way. This framework can be used to compare and merge models at different abstraction levels; in particular, higher-level (e.g. mobility) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions. As examples, we formalize in our framework the vesiculation and the phagocytosis processes

Thermodynamic competition between membrane protein oligomeric states

Self-assembly of protein monomers into distinct membrane protein oligomers provides a general mechanism for diversity in the molecular architectures, and resulting biological functions, of membrane proteins. We develop a general physical framework describing the thermodynamic competition between different oligomeric states of membrane proteins. Using the mechanosensitive channel of large conductance as a model system, we show how the dominant oligomeric states of membrane proteins emerge from the interplay of protein concentration in the cell membrane, protein-induced lipid bilayer deformations, and direct monomer-monomer interactions. Our results suggest general physical mechanisms and principles underlying regulation of protein function via control of membrane protein oligomeric state.Comment: 7 pages, 5 figure

Insights into membrane protein–lipid interactions from free energy calculations

Integral membrane proteins are regulated by specific interactions with lipids from the surrounding bilayer. The structures of protein–lipid complexes can be determined through a combination of experimental and computational approaches, but the energetic basis of these interactions is difficult to resolve. Molecular dynamics simulations provide the primary computational technique to estimate the free energies of these interactions. We demonstrate that the energetics of protein–lipid interactions may be reliably and reproducibly calculated using three simulation-based approaches: potential of mean force calculations, alchemical free energy perturbation, and well-tempered metadynamics. We employ these techniques within the framework of a coarse-grained force field and apply them to both bacterial and mammalian membrane protein–lipid systems. We demonstrate good agreement between the different techniques, providing a robust framework for their automated implementation within a pipeline for annotation of newly determined membrane protein structures

Curvature-driven feedback on aggregation-diffusion of proteins in lipid bilayers

Membrane bending is an extensively studied problem from both modeling and experimental perspectives because of the wide implications of curvature generation in cell biology. Many of the curvature generating aspects in membranes can be attributed to interactions between proteins and membranes. These interactions include protein diffusion and formation of aggregates due to protein-protein interactions in the plane of the membrane. Recently, we developed a model that couples the in-plane flow of lipids and diffusion of proteins with the out-of-plane bending of the membrane. Building on this work, here, we focus on the role of explicit aggregation of proteins on the surface of the membrane in the presence of membrane bending and diffusion. We develop a comprehensive framework that includes lipid flow, membrane bending, the entropy of protein distribution, along with an explicit aggregation potential and derive the governing equations for the coupled system. We compare this framework to the Cahn-Hillard formalism to predict the regimes in which the proteins form patterns on the membrane. We demonstrate the utility of this model using numerical simulations to predict how aggregation and diffusion, when coupled with curvature generation, can alter the landscape of membrane-protein interactions.Comment: 20 pages, 9 figure

Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation

Background Outer membrane proteins (OMPs) of Pasteurella multocida have various functions related to virulence and pathogenesis and represent important targets for vaccine development. Various bioinformatic algorithms can predict outer membrane localization and discriminate OMPs by structure or function. The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome. Results In the present study, we used 10 different predictors classified into three groups (subcellular localization, transmembrane β-barrel protein and lipoprotein predictors) to identify putative OMPs from two available P. multocida genomes: those of avian strain Pm70 and porcine non-toxigenic strain 3480. Predicted proteins in each group were filtered by optimized criteria for consensus prediction: at least two positive predictions for the subcellular localization predictors, three for the transmembrane β-barrel protein predictors and one for the lipoprotein predictors. The consensus predicted proteins were integrated from each group into a single list of proteins. We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction. As a result, we were able to confidently predict 98 putative OMPs from the avian strain genome and 107 OMPs from the porcine strain genome with 83% overlap between the two genomes. Conclusions The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence. Our approach can be applied to investigate the outer membrane proteomes of other Gram-negative bacteria

Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian inference: application to the multi-scale dynamics of glycine receptors in the neuronal membrane

Protein mobility is conventionally analyzed in terms of an effective diffusion. Yet, this description often fails to properly distinguish and evaluate the physical parameters (such as the membrane friction) and the biochemical interactions governing the motion. Here, we present a method combining high-density single-molecule imaging and statistical inference to separately map the diffusion and energy landscapes of membrane proteins across the cell surface at ~100 nm resolution (with acquisition of a few minutes). When applying these analytical tools to glycine neurotransmitter receptors (GlyRs) at inhibitory synapses, we find that gephyrin scaffolds act as shallow energy traps (~3 kBT) for GlyRs, with a depth modulated by the biochemical properties of the receptor-gephyrin interaction loop. In turn, the inferred maps can be used to simulate the dynamics of proteins in the membrane, from the level of individual receptors to that of the population, and thereby, to model the stochastic fluctuations of physiological parameters (such as the number of receptors at synapses). Overall, our approach provides a powerful and comprehensive framework with which to analyze biochemical interactions in living cells and to decipher the multi-scale dynamics of biomolecules in complex cellular environments.Comment: 23 pages, 4 figure

Molecular Model of the Microvillar Cytoskeleton and Organization of the Brush Border

BACKGROUND. Brush border microvilli are ~1-µm long finger-like projections emanating from the apical surfaces of certain, specialized absorptive epithelial cells. A highly symmetric hexagonal array of thousands of these uniformly sized structures form the brush border, which in addition to aiding in nutrient absorption also defends the large surface area against pathogens. Here, we present a molecular model of the protein cytoskeleton responsible for this dramatic cellular morphology. METHODOLOGY/PRINCIPAL FINDINGS. The model is constructed from published crystallographic and microscopic structures reported by several groups over the last 30+ years. Our efforts resulted in a single, unique, self-consistent arrangement of actin, fimbrin, villin, brush border myosin (Myo1A), calmodulin, and brush border spectrin. The central actin core bundle that supports the microvillus is nearly saturated with fimbrin and villin cross-linkers and has a density similar to that found in protein crystals. The proposed model accounts for all major proteinaceous components, reproduces the experimentally determined stoichiometry, and is consistent with the size and morphology of the biological brush border membrane. CONCLUSIONS/SIGNIFICANCE. The model presented here will serve as a structural framework to explain many of the dynamic cellular processes occurring over several time scales, such as protein diffusion, association, and turnover, lipid raft sorting, membrane deformation, cytoskeletal-membrane interactions, and even effacement of the brush border by invading pathogens. In addition, this model provides a structural basis for evaluating the equilibrium processes that result in the uniform size and structure of the highly dynamic microvilli.Boston University (Graduate Student Research Fellowship); National Institutes of Health (GM62886