Oligomerization of peripheral membrane proteins provides tunable control of cell surface polarity

Asymmetric distributions of peripheral membrane proteins define cell polarity across all kingdoms of life. These asymmetries are shaped by membrane binding, diffusion and transport. Theoretical studies have revealed a general requirement for non-linear positive feedback to spontaneously amplify and/or stabilize asymmetries against dispersion by diffusion and dissociation. But how specific molecular sources of non-linearity shape polarization dynamics remains poorly understood. Here we study how oligomerization of peripheral membrane proteins shapes polarization dynamics in simple feedback circuits. We show that size dependent binding avidity and mobility of membrane bound oligomers endow polarity circuits generically with several key properties. Size-dependent binding avidity confers a form of positive feedback in which the effective rate constant for subunit dissociation decreases with increasing subunit density. This combined with additional weak linear positive feedback is sufficient for spontaneous emergence of stably polarized states. Size-dependent oligomer mobility makes symmetry-breaking and stable polarity more robust with respect to variation in subunit diffusivities and cell sizes, and slows the approach to a final stable spatial distribution, allowing cells to "remember" polarity boundaries imposed by transient external cues. Together, these findings reveal how oligomerization of peripheral membrane proteins can provide powerful and highly tunable sources of non-linear feedback in biochemical circuits that govern cell-surface polarity. Given its prevalence and widespread involvement in cell polarity, we speculate that self-oligomerization may have provided an accessible path to evolving simple polarity circuits.

Membrane-Mediated Interactions Between Protein Inclusions

Integral or peripheral membrane proteins, or protein oligomers often get close to each other on cell membranes and carry out biological tasks in a collective manner. In addition to electrostatic and van der Waals interactions, those proteins also experience membrane-mediated interactions, which may be necessary for their functionality. The membrane-mediated interactions originate from perturbation of lipid membranes by the presence of protein inclusions, and have been the subject of intensive research in membrane biophysics. Here we review both theoretical and numerical studies of such interactions for membrane proteins and for nanoparticles bound to lipid membranes.

Lysine acetylation regulates the interaction between proteins and membranes

Lysine acetylation regulates the function of soluble proteins in vivo, yet it remains largely unexplored whether lysine acetylation regulates membrane protein function. Here, we use bioinformatics, biophysical analysis of recombinant proteins, live-cell fluorescent imaging and genetic manipulation of Drosophila to explore lysine acetylation in peripheral membrane proteins. Analysis of 50 peripheral membrane proteins harboring BAR, PX, C2, or EHD membrane-binding domains reveals that lysine acetylation predominates in membrane-interaction regions. Acetylation and acetylation-mimicking mutations in three test proteins, amphiphysin, EHD2, and synaptotagmin1, strongly reduce membrane binding affinity, attenuate membrane remodeling in vitro and alter subcellular localization. This effect is likely due to the loss of positive charge, which weakens interactions with negatively charged membranes. In Drosophila, acetylation-mimicking mutations of amphiphysin cause severe disruption of T-tubule organization and yield a flightless phenotype. Our data provide mechanistic insights into how lysine acetylation regulates membrane protein function, potentially impacting a plethora of membrane-related processes.

Basic Residue Clusters in Intrinsically Disordered Regions of Peripheral Membrane Proteins: Modulating 2D Diffusion on Cell Membranes

A large number of peripheral membrane proteins transiently interact with lipids through a combination of weak interactions. Among them, electrostatic interactions of clusters of positively charged amino acid residues with negatively charged lipids play an important role. Clusters of charged residues are often found in intrinsically disordered protein regions, which are highly abundant in the vicinity of the membrane forming what has been called the disordered boundary of the cell. Beyond contributing to the stability of the lipid-bound state, the pattern of charged residues may encode specific interactions or properties that form the basis of cell signaling. The element of this code may include, among others, the recognition, clustering, and selective release of phosphatidyl inositides, lipid-mediated protein-protein interactions changing the residence time of the peripheral membrane proteins or driving their approximation to integral membrane proteins. Boundary effects include reduction of dimensionality, protein reorientation, biassing of the conformational ensemble of disordered regions or enhanced 2D diffusion in the peri-membrane region enabled by the fuzzy character of the electrostatic interactions with an extended lipid membrane.

Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning

Motivation: Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane binding domains of peripheral membrane proteins are usually not known. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Results: Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating residues. We utilize available experimental data in the literature for training 21 machine learning classifiers and a voting classifier. Evaluation of the ensemble classifier accuracy produced a macro-averaged F1 score = 0.92 and an MCC = 0.84 for predicting correctly membrane-penetrating residues on unknown proteins of an independent test set. Availability and implementation: The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

Peripheral Membrane Proteins: Promising Therapeutic Targets across Domains of Life

Membrane proteins can be classified into two main categories—integral and peripheral membrane proteins—depending on the nature of their membrane interaction. Peripheral membrane proteins are highly unique amphipathic proteins that interact with the membrane indirectly, using electrostatic or hydrophobic interactions, or directly, using hydrophobic tails or GPI-anchors. The nature of this interaction not only influences the location of the protein in the cell, but also the function. In addition to their unique relationship with the cell membrane, peripheral membrane proteins often play a key role in the development of human diseases such as African sleeping sickness, cancer, and atherosclerosis. This review will discuss the membrane interaction and role of periplasmic nitrate reductase, CymA, cytochrome c, alkaline phosphatase, ecto-5’-nucleotidase, acetylcholinesterase, alternative oxidase, type-II NADH dehydrogenase, and dihydroorotate dehydrogenase in certain diseases. The study of these proteins will give new insights into their function and structure, and may ultimately lead to ground-breaking advances in the treatment of severe diseases.

Developing Nanodisc-ID for label-free characterizations of membrane proteins

Membrane proteins (MPs) influence all aspects of life, such as tumorigenesis, immune response, and neural transmission. However, characterization of MPs is challenging, as it often needs highly specialized techniques inaccessible to many labs. We herein introduce nanodisc-ID that enables quantitative analysis of membrane proteins using a gel electrophoresis readout. By leveraging the power of nanodiscs and proximity labeling, nanodisc-ID serves both as scaffolds for encasing biochemical reactions and as sensitive reagents for detecting membrane protein-lipid and protein-protein interactions. We demonstrate this label-free and low-cost tool by characterizing a wide range of integral and peripheral membrane proteins from prokaryotes and eukaryotes.

Prediction of amphipathic helix—Membrane interactions with Rosetta

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on oppo site faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.

A flat embedding method for transmission electron microscopy reveals an unknown mechanism of tetracycline

Transmission electron microscopy of cell sample sections is a popular technique in microbiology. Currently, ultrathin sectioning is done on resin-embedded cell pellets, which consumes milli- to deciliters of culture and results in sections of randomly orientated cells. This is problematic for rod-shaped bacteria and often precludes large-scale quantification of morphological phenotypes due to the lack of sufficient numbers of longitudinally cut cells. Here we report a flat embedding method that enables observation of thousands of longitudinally cut cells per single section and only requires microliter culture volumes. We successfully applied this technique to Bacillus subtilis, Escherichia coli, Mycobacterium bovis, and Acholeplasma laidlawii. To assess the potential of the technique to quantify morphological phenotypes, we monitored antibiotic-induced changes in B. subtilis cells. Surprisingly, we found that the ribosome inhibitor tetracycline causes membrane deformations. Further investigations showed that tetracycline disturbs membrane organization and localization of the peripheral membrane proteins MinD, MinC, and MreB. These observations are not the result of ribosome inhibition but constitute a secondary antibacterial activity of tetracycline that so far has defied discovery.