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MotivationMany membrane peripheral proteins have evolved to transiently interact with the surface of (curved) lipid bilayers. Currently, methods toquantitativelypredict 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 theirde novodesign.ResultsHere, 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, PPM, and MODA, but with a wider applicability regarding protein diversity, and the added feature to distinguish curvature sensing from general membrane binding.AvailabilityWe made these tools available as a web server, coined Protein-Membrane Interaction predictor (PMIpred), which can be accessed athttps://pmipred.fkt.physik.tu-dortmund.de.

The membrane-protein interface in lipid nanoparticles (LNPs) is important for their in vivo behavior. Better understanding may assist to evolve current drug delivery methods to more precise, cell- or tissue-specific nanomedicine. Previously, we demonstrated how phase separation can drive liposomes to cell specific accumulation in vivo, through the selective recognition of phase-separated liposomes by triacylglycerol lipases (TGLs). This exemplified how liposome morphology can determine the preferential interaction of nanoparticles with biologically relevant proteins. Here, we investigate in detail the lipase-induced morphological changes of phase separated liposomes - which bear a lipid droplet in their bilayer - and unravel how lipase recognizes and binds to the particles at a molecular level. We find that phase separated liposomes undergo selective lipolytic degradation of their lipid droplet while overall nanoparticle integrity remains intact. Next, we combined MD simulations and in vitro experiments to identify the Tryptophan-rich loop of the lipase – a region which is involved endogenously in lipoprotein binding – as the region through which the enzyme binds to the particle. We demonstrate that this preferential binding is due to the lipid packing defects induced on the membrane by phase separation. These findings are a significant example of selective LNP – protein communication and interaction, aspects that may further the control of the in vivo behavior of lipid nanoparticles.

The membrane-protein interface in lipid nanoparticles (LNPs) is important for their in vivo behavior. Better understanding may assist to evolve current drug delivery methods to more precise, cell- or tissue-specific nanomedicine. Previously, we demonstrated how phase separation can drive liposomes to cell specific accumulation in vivo, through the selective recognition of phase-separated liposomes by triacylglycerol lipases (TGLs). This exemplified how liposome morphology can determine the preferential interaction of nanoparticles with biologically relevant proteins. Here, we investigate in detail the lipase-induced morphological changes of phase separated liposomes - which bear a lipid droplet in their bilayer - and unravel how lipase recognizes and binds to the particles at a molecular level. We find that phase separated liposomes undergo selective lipolytic degradation of their lipid droplet while overall nanoparticle integrity remains intact. Next, we combined MD simulations and in vitro experiments to identify the Tryptophan-rich loop of the lipase – a region which is involved endogenously in lipoprotein binding – as the region through which the enzyme binds to the particle. We demonstrate that this preferential binding is due to the lipid packing defects induced on the membrane by phase separation. These findings are a significant example of selective LNP – protein communication and interaction, aspects that may further the control of the in vivo behavior of lipid nanoparticles.

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.

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, whilst 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.TOC Graphic

The occurrence of linear cholesterol-recognition motifs in alpha-helical transmembrane domains has long been debated. Here, we demonstrate the ability of a genetic algorithm guided by coarse-grained molecular dynamics simulations—a method coined evolutionary molecular dynamics (Evo-MD)—to directly resolve the sequence which maximally attracts cholesterol for single-pass alpha-helical transmembrane domains (TMDs). We illustrate that the evolutionary landscape of cholesterol attraction in membrane proteins is characterized by a sharp, well-defined global optimum. Surprisingly, this optimal solution features an unusual short hydrophobic block, consisting of typically only eight small hydrophobic amino acids, surrounded by three successive lysines. Owing to the membrane thickening effect of cholesterol, cholesterol-enriched ordered phases favor TMDs characterized by a long rather than a too short hydrophobic length (a negative hydrophobic mismatch). However, this short hydrophobic pattern evidently offers a pronounced net advantage for the attraction of free cholesterol in both coarse-grained and atomistic simulations. We illustrate that optimal cholesterol attraction is in fact based on the superposition of two distinct thermodynamic attraction mechanisms. In addition, we explore the evolutionary occurrence and feasibility of these two mechanisms by analyzing existing databases of membrane proteins and through the direct expression of analogous short hydrophobic sequences in live cell assays. The puzzling sequence variability of proposed linear cholesterol-recognition motifs is indicative of a sub-optimal membrane-mediated attraction of cholesterol which markedly differs from ligand binding based on shape compatibility.Significance StatementOur work demonstrates how a synergy between evolutionary algorithms and high-throughput coarse-grained molecular dynamics can yield fundamentally new insights into the evolutionary fingerprints of protein-mediated lipid sorting. We illustrate that the evolutionary landscape of cholesterol attraction in isolated transmembrane domains is characterized by a well-defined global optimum. In contrast, sub-optimal attraction of cholesterol is associated with a diverse solution space and features a high sequence variability despite acting on the same unique molecule. The contrasting physicochemical nature of the resolved attraction optimum suggests that cholesterol attraction via linear motifs does not pose a dominant pressure on the evolution of transmembrane proteins.

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.

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 β2-adrenergic receptor (β2AR) 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 dru...

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.

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 β

Contains fulltext : 173139.pdf (Publisher’s version ) (Open Access) 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-kappaB and IRF pathways.