Atypical cofilin signaling drives dendritic cell migration through the extracellular matrix via nuclear deformation.

To mount an adaptive immune response, dendritic cells must migrate to lymph nodes to present antigens to T cells. Critical to 3D migration is the nucleus, which is the size-limiting barrier for migration through the extracellular matrix. Here, we show that inflammatory activation of dendritic cells leads to the nucleus becoming spherically deformed and enables dendritic cells to overcome the typical 2- to 3-µm diameter limit for 3D migration through gaps in the extracellular matrix. We show that the nuclear shape change is partially attained through reduced cell adhesion, whereas improved 3D migration is achieved through reprogramming of the actin cytoskeleton. Specifically, our data point to a model whereby the phosphorylation of cofilin-1 at serine 41 drives the assembly of a cofilin-actomyosin ring proximal to the nucleus and enhances migration through 3D collagen gels. In summary, these data describe signaling events through which dendritic cells deform their nucleus and enhance their migratory capacity.

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.

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.

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

Proteins can specifically bind to curved membranes through curvature-induced hydrophobic lipid packing defects. The chemical diversity among such curvature "sensors" challenges our understanding of how they differ from general membrane "binders" that bind without curvature selectivity. Here, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations (Evo-MD) to resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and a neural network (NN) can enhance the identification and discovery of curvature sensing peptides and proteins. To this aim, we benchmark a physics-trained NN model against experimental data and show that we can correctly identify known sensors and binders. We illustrate that sensing and binding are phenomena that lie on the same thermodynamic continuum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors.

Azobenzene-Based Amino Acids for the Photocontrol of Coiled-Coil Peptides.

Coiled-coil peptides are high-affinity, selective, self-assembling binding motifs, making them attractive components for the preparation of functional biomaterials. Photocontrol of coiled-coil self-assembly allows for the precise localization of their activity. To rationally explore photoactivity in a model coiled coil, three azobenzene-containing amino acids were prepared and substituted into the hydrophobic core of the E3/K3 coiled-coil heterodimer. Two of the non-natural amino acids, APhe1 and APhe2, are based on phenylalanine and differ in the presence of a carboxylic acid group. These have previously been demonstrated to modulate protein activity. When incorporated into peptide K3, coiled-coil binding strength was affected upon isomerization, with the two variants differing in their most folded state. The third azobenzene-containing amino acid, APgly, is based on phenylglycine and was prepared to investigate the effect of amino acid size on photoisomerization. When APgly is incorporated into the coiled coil, a 4.7-fold decrease in folding constant is observed upon trans-to-cis isomerization─the largest difference for all three amino acids. Omitting the methylene group between azobenzene and α-carbon was theorized to both position the diazene of APgly closer to the hydrophobic amino acids and reduce the possible rotations of the amino acid, with molecular dynamics simulations supporting these hypotheses. These results demonstrate the ability of photoswitchable amino acids to control coiled-coil assembly through disruption of the hydrophobic interface, a strategy that should be widely applicable.

Efficient Quantification of Lipid Packing Defect Sensing by Amphipathic Peptides: Comparing Martini 2 and 3 with CHARMM36.

In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide's ability to concurrently induce excess leaflet area (tension) and elastic softening─a property we call the "characteristic area of sensing" (CHAOS)─and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide's propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors─aside from their biomedical and biophysical relevance─can provide valuable targets for the development of lipid force fields.

Structure-Based Discovery of Novel Ligands for the Orexin 2 Receptor.

The orexin receptors are peptide-sensing G protein-coupled receptors that are intimately linked with regulation of the sleep/wake cycle. We used a recently solved X-ray structure of the orexin receptor subtype 2 in computational docking calculations with the aim to identify additional ligands with unprecedented chemotypes. We found validated ligands with a high hit rate of 29% out of those tested, none of them showing selectivity with respect to the orexin receptor subtype 1. Furthermore, of the higher-affinity compounds examined, none showed any agonist activity. While novel chemical structures can thus be found, selectivity is a challenge owing to the largely identical binding pockets.

Membrane Thinning Induces Sorting of Lipids and the Amphipathic Lipid Packing Sensor (ALPS) Protein Motif.

Heterogeneities (e.g., membrane proteins and lipid domains) and deformations (e.g., highly curved membrane regions) in biological lipid membranes cause lipid packing defects that may trigger functional sorting of lipids and membrane-associated proteins. To study these phenomena in a controlled and efficient way within molecular simulations, we developed an external field protocol that artificially enhances packing defects in lipid membranes by enforcing local thinning of a flat membrane region. For varying lipid compositions, we observed strong thinning-induced depletion or enrichment, depending on the lipid's intrinsic shape and its effect on a membrane's elastic modulus. In particular, polyunsaturated and lysolipids are strongly attracted to regions high in packing defects, whereas phosphatidylethanolamine (PE) lipids and cholesterol are strongly repelled from it. Our results indicate that externally imposed changes in membrane thickness, area, and curvature are underpinned by shared membrane elastic principles. The observed sorting toward the thinner membrane region is in line with the sorting expected for a positively curved membrane region. Furthermore, we have demonstrated that the amphipathic lipid packing sensor (ALPS) protein motif, a known curvature and packing defect sensor, is effectively attracted to thinner membrane regions. By extracting the force that drives amphipathic molecules toward the thinner region, our thinning protocol can directly quantify and score the lipid packing sensing of different amphipathic molecules. In this way, our protocol paves the way toward high-throughput exploration of potential defect- and curvature-sensing motifs, making it a valuable addition to the molecular simulation toolbox.

Virtual Compound Libraries in Computer-Assisted Drug Discovery.

The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the past decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency and the need for more reliable vendors and accurate synthesis prediction tools. These challenges should be tackled by both the developers of virtual libraries and by their users, in order for the exploration of chemical space to live up to its potential.

Potent Metabolic Sialylation Inhibitors Based on C-5-Modified Fluorinated Sialic Acids.

Sialic acid sugars on mammalian cells regulate numerous biological processes, while aberrant expression of sialic acid is associated with diseases such as cancer and pathogenic infection. Inhibition of the sialic acid biosynthesis may therefore hold considerable therapeutic potential. To effectively decrease the sialic acid expression, we synthesized C-5-modified 3-fluoro sialic acid sialyltransferase inhibitors. We found that C-5 carbamates significantly enhanced and prolonged the inhibitory activity in multiple mouse and human cell lines. As an underlying mechanism, we have identified that carbamate-modified 3-fluoro sialic acid inhibitors are more efficiently metabolized to their active cytidine monophosphate analogues, reaching higher effective inhibitor concentrations inside cells.

Binding-Site Compatible Fragment Growing Applied to the Design of ß2-Adrenergic Receptor Ligands.

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the ß2-adrenergic receptor (ß2AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

Steering Siglec-Sialic Acid Interactions on Living Cells using Bioorthogonal Chemistry.

Sialic acid sugars that terminate cell-surface glycans form the ligands for the sialic acid binding immunoglobulin-like lectin (Siglec) family, which are immunomodulatory receptors expressed by immune cells. Interactions between sialic acid and Siglecs regulate the immune system, and aberrations contribute to pathologies like autoimmunity and cancer. Sialic acid/Siglec interactions between living cells are difficult to study owing to a lack of specific tools. Here, we report a glycoengineering approach to remodel the sialic acids of living cells and their binding to Siglecs. Using bioorthogonal chemistry, a library of cells with more than sixty different sialic acid modifications was generated that showed dramatically increased binding toward the different Siglec family members. Rational design reduced cross-reactivity and led to the discovery of three selective Siglec-5/14 ligands. Furthermore, glycoengineered cells carrying sialic acid ligands for Siglec-3 dampened the activation of Siglec-3+ monocytic cells through the NF-κB and IRF pathways.