Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders

Influence of antimicrobial peptides on the bacterial membrane curvature and vice versa

Many factors contribute to bacterial membrane stabilization, including steric effects between lipids, membrane spontaneous curvature, and the difference in the number of neighboring molecules. This forum provides an overview of the physicochemical properties associated with membrane curvature and how this parameter can be tuned to design more effective antimicrobial peptides.

SNARE chaperone Sly1 directly mediates close-range vesicle tethering

The essential Golgi protein Sly1 is a member of the Sec1/mammalian Unc-18 (SM) family of SNARE chaperones. Sly1 was originally identified through remarkable gain-of-function alleles that bypass requirements for diverse vesicle tethering factors. Employing genetic analyses and chemically defined reconstitutions of ER-Golgi fusion, we discovered that a loop conserved among Sly1 family members is not only autoinhibitory but also acts as a positive effector. An amphipathic lipid packing sensor (ALPS)-like helix within the loop directly binds high-curvature membranes. Membrane binding is required for relief of Sly1 autoinhibition and also allows Sly1 to directly tether incoming vesicles to the Qa-SNARE on the target organelle. The SLY1-20 mutation bypasses requirements for diverse tethering factors but loses this ability if the tethering activity is impaired. We propose that long-range tethers, including Golgins and multisubunit tethering complexes, hand off vesicles to Sly1, which then tethers at close range to initiate trans-SNARE complex assembly and fusion in the early secretory pathway.

Addressing Heterogeneity in Direct Analysis of Extracellular Vesicles and Their Analogs by Membrane Sensing Peptides as Pan-Vesicular Affinity Probes

Extracellular vesicles (EVs), crucial mediators of cell-to-cell communication, hold significant diagnostic potential due to their ability to concentrate protein biomarkers in bodily fluids. However, challenges in isolating EVs from biological specimens hinder their widespread use. The preferred strategy involves direct analysis, integrating isolation and analysis solutions, with immunoaffinity methods currently dominating. Yet, the heterogeneous nature of EVs poses challenges, as proposed markers may not be as universally present as thought, raising concerns about biomarker screening reliability. This issue extends to EV-mimics, where conventional methods may lack applicability. Addressing these challenges, the study reports on Membrane Sensing Peptides (MSP) as pan-vesicular affinity ligands for both EVs and their non-canonical analogs, streamlining capture and phenotyping through Single Molecule Array (SiMoA). MSP ligands enable direct analysis of circulating EVs, eliminating the need for prior isolation. Demonstrating clinical translation, MSP technology detects an EV-associated epitope signature in serum and plasma, distinguishing myocardial infarction from stable angina. Additionally, MSP allow analysis of tetraspanin-lacking Red Blood Cell-derived EVs, overcoming limitations associated with antibody-based methods. Overall, the work underlines the value of MSP as complementary tools to antibodies, advancing EV analysis for clinical diagnostics and beyond, and marking the first-ever peptide-based application in SiMoA technology.

Modeling membranes in situ

Molecular dynamics simulations of cellular membranes have come a long way-from simple model lipid bilayers to multicomponent systems capturing the crowded and complex nature of real cell membranes. In this opinionated minireview, we discuss the current challenge to simulate the dynamics of membranes in their native environment, in situ, with the prospect of reaching the level of whole cells and cell organelles using an integrative modeling framework.

The Evolutionary Origins and Ancestral Features of Septins

Septins are a family of membrane-associated cytoskeletal GTPases that play crucial roles in various cellular processes, such as cell division, phagocytosis, and organelle fission. Despite their importance, the evolutionary origins and ancestral function of septins remain unclear. In opisthokonts, septins form five distinct groups of orthologs, with subunits from multiple groups assembling into heteropolymers, thus supporting their diverse molecular functions. Recent studies have revealed that septins are also conserved in algae and protists, indicating an ancient origin from the last eukaryotic common ancestor. However, the phylogenetic relationships among septins across eukaryotes remained unclear. Here, we expanded the list of non-opisthokont septins, including previously unrecognized septins from rhodophyte red algae and glaucophyte algae. Constructing a rooted phylogenetic tree of 254 total septins, we observed a bifurcation between the major non-opisthokont and opisthokont septin clades. Within the non-opisthokont septins, we identified three major subclades: Group 6 representing chlorophyte green algae (6A mostly for species with single septins, 6B for species with multiple septins), Group 7 representing algae in chlorophytes, heterokonts, haptophytes, chrysophytes, and rhodophytes, and Group 8 representing ciliates. Glaucophyte and some ciliate septins formed orphan lineages in-between all other septins and the outgroup. Combining ancestral-sequence reconstruction and AlphaFold predictions, we tracked the structural evolution of septins across eukaryotes. In the GTPase domain, we identified a conserved GAP-like arginine finger within the G-interface of at least one septin in most algal and ciliate species. This residue is required for homodimerization of the single Chlamydomonas septin, and its loss coincided with septin duplication events in various lineages. The loss of the arginine finger is often accompanied by the emergence of the α0 helix, a known NC-interface interaction motif, potentially signifying the diversification of septin-septin interaction mechanisms from homo-dimerization to hetero-oligomerization. Lastly, we found amphipathic helices in all septin groups, suggesting that curvature-sensing is an ancestral trait of septin proteins. Coiled-coil domains were also broadly distributed, while transmembrane domains were found in some septins in Group 6A and 7. In summary, this study advances our understanding of septin distribution and phylogenetic groupings, shedding light on their ancestral features, potential function, and early evolution.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Phase-Separated Lipid-Based Nanoparticles: Selective Behavior at the Nano-Bio Interface

The membrane-protein interface on lipid-based nanoparticles influences their in vivo behavior. Better understanding may evolve current drug delivery methods toward effective targeted nanomedicine. Previously, the cell-selective accumulation of a liposome formulation in vivo is demonstrated, through the recognition of lipid phase-separation by triglyceride lipases. This exemplified how liposome morphology and composition can determine nanoparticle-protein interactions. Here, the lipase-induced compositional and morphological changes of phase-separated liposomes-which bear a lipid droplet in their bilayer- are investigated, and the mechanism upon which lipases recognize and bind to the particles is unravelled. The selective lipolytic degradation of the phase-separated lipid droplet is observed, while nanoparticle integrity remains intact. Next, the Tryptophan-rich loop of the lipase is identified as the region with which the enzymes bind to the particles. This preferential binding is due to lipid packing defects induced on the liposome surface by phase separation. In parallel, the existing knowledge that phase separation leads to in vivo selectivity, is utilized to generate phase-separated mRNA-LNPs that target cell-subsets in zebrafish embryos, with subsequent mRNA delivery and protein expression. Together, these findings can expand the current knowledge on selective nanoparticle-protein communications and in vivo behavior, aspects that will assist to gain control of lipid-based nanoparticles.

PMIpred: a physics-informed web server for quantitative protein-membrane interaction prediction

MOTIVATION

Many membrane peripheral proteins have evolved to transiently interact with the surface of (curved) lipid bilayers. Currently, methods to quantitatively predict sensing and binding free energies for protein sequences or structures are lacking, and such tools could greatly benefit the discovery of membrane-interacting motifs, as well as their de novo design.

RESULTS

Here, we trained a transformer neural network model on molecular dynamics data for >50 000 peptides that is able to accurately predict the (relative) membrane-binding free energy for any given amino acid sequence. Using this information, our physics-informed model is able to classify a peptide's membrane-associative activity as either non-binding, curvature sensing, or membrane binding. Moreover, this method can be applied to detect membrane-interaction regions in a wide variety of proteins, with comparable predictive performance as state-of-the-art data-driven tools like DREAMM, PPM3, and MODA, but with a wider applicability regarding protein diversity, and the added feature to distinguish curvature sensing from general membrane binding.

AVAILABILITY AND IMPLEMENTATION

We made these tools available as a web server, coined Protein-Membrane Interaction predictor (PMIpred), which can be accessed at https://pmipred.fkt.physik.tu-dortmund.de.