Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations

Biogenesis of EVs in Trypanosomatids

Trypanosomes are protozoan parasites responsible for human diseases such as Chagas disease, African trypanosomiasis, and leishmaniasis. These organisms' growth in various environments and exhibit multiple morphological stages, while adapting their surface components. They acquire and release materials extensively to get nutrients and manage interactions with the extracellular environment. They acquire and utilize proteins, lipids, and carbohydrates for growth via using membrane transport and endocytosis. Endocytosis takes place through distinct membrane areas known as the flagellar pocket and cytostome, depending on the parasite species and its developmental stage. Some forms establish a complex endocytic system to either store or break down the absorbed materials. In contrast, membrane transport facilitates the uptake of small molecules like amino acids, carbohydrates, and iron via particular receptors on the plasma membrane. Concurrently, these parasites secrete various molecules such as proteins, enzymes, nucleic acids, and glycoconjugates either in soluble form or enclosed in extracellular vesicles, which significantly contribute to their parasitic behavior. These activities require exocytosis through a secretory pathway in certain membrane domains such as the flagellum, flagellar pocket, and plasma membrane, which are controlled at various developmental stages. The main features of the endocytic and exocytic mechanisms, as well as the organelles involved, are discussed in this chapter along with their connection to the formation of exosomes and extracellular vesicles in the Tritryp species.

Assessing the Martini 3 protein model: A review of its path and potential

Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.

Elucidating the complex membrane binding of a protein with multiple anchoring domains using extHMMM

Membrane binding is a crucial mechanism for many proteins, but understanding the specific interactions between proteins and membranes remains a challenging endeavor. Coagulation factor Va (FVa) is a large protein whose membrane interactions are complicated due to the presence of multiple anchoring domains that individually can bind to lipid membranes. Using molecular dynamics simulations, we investigate the membrane binding of FVa and identify the key mechanisms that govern its interaction with membranes. Our results reveal that FVa can either adopt an upright or a tilted molecular orientation upon membrane binding. We further find that the domain organization of FVa deviates (sometimes significantly) from its crystallographic reference structure, and that the molecular orientation of the protein matches with domain reorganization to align the C2 domain toward its favored membrane-normal orientation. We identify specific amino acid residues that exhibit contact preference with phosphatidylserine lipids over phosphatidylcholine lipids, and we observe that mostly electrostatic effects contribute to this preference. The observed lipid-binding process and characteristics, specific to FVa or common among other membrane proteins, in concert with domain reorganization and molecular tilt, elucidate the complex membrane binding dynamics of FVa and provide important insights into the molecular mechanisms of protein-membrane interactions. An updated version of the HMMM model, termed extHMMM, is successfully employed for efficiently observing membrane bindings of systems containing the whole FVa molecule.

Membranes are functionalized by a proteolipid code

Membranes are protein and lipid structures that surround cells and other biological compartments. We present a conceptual model wherein all membranes are organized into structural and functional zones. The assembly of zones such as receptor clusters, protein-coated pits, lamellipodia, cell junctions, and membrane fusion sites is explained to occur through a protein-lipid code. This challenges the theory that lipids sort proteins after forming stable membrane subregions independently of proteins.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Deterministic early endosomal maturations emerge from a stochastic trigger-and-convert mechanism

Endosomal maturation is critical for robust and timely cargo transport to specific cellular compartments. The most prominent model of early endosomal maturation involves a phosphoinositide-driven gain or loss of specific proteins on individual endosomes, emphasising an autonomous and stochastic description. However, limitations in fast, volumetric imaging long hindered direct whole cell-level measurements of absolute numbers of maturation events. Here, we use lattice light-sheet imaging and bespoke automated analysis to track individual very early (APPL1-positive) and early (EEA1-positive) endosomes over the entire population, demonstrating that direct inter-endosomal contact drives maturation between these populations. Using fluorescence lifetime, we show that this endosomal interaction is underpinned by asymmetric binding of EEA1 to very early and early endosomes through its N- and C-termini, respectively. In combination with agent-based simulation which supports a 'trigger-and-convert' model, our findings indicate that APPL1- to EEA1-positive maturation is driven not by autonomous events but by heterotypic EEA1-mediated interactions, providing a mechanism for temporal and population-level control of maturation.

Comprehensive classification of proteins based on structures that engage lipids by COMPOSEL

Structures can now be predicted for any protein using programs like AlphaFold and Rosetta, which rely on a foundation of experimentally determined structures of architecturally diverse proteins. The accuracy of such artificial intelligence and machine learning (AI/ML) approaches benefits from the specification of restraints which assist in navigating the universe of folds to converge on models most representative of a given protein's physiological structure. This is especially pertinent for membrane proteins, with structures and functions that depend on their presence in lipid bilayers. Structures of proteins in their membrane environments could conceivably be predicted from AI/ML approaches with user-specificized parameters that describe each element of the architecture of a membrane protein accompanied by its lipid environment. We propose the Classification Of Membrane Proteins based On Structures Engaging Lipids (COMPOSEL), which builds on existing nomenclature types for monotopic, bitopic, polytopic and peripheral membrane proteins as well as lipids. Functional and regulatory elements are also defined in the scripts, as shown with membrane fusing synaptotagmins, multidomain PDZD8 and Protrudin proteins that recognize phosphoinositide (PI) lipids, the intrinsically disordered MARCKS protein, caveolins, the β barrel assembly machine (BAM), an adhesion G-protein coupled receptor (aGPCR) and two lipid modifying enzymes - diacylglycerol kinase DGKε and fatty aldehyde dehydrogenase FALDH. This demonstrates how COMPOSEL communicates lipid interactivity as well as signaling mechanisms and binding of metabolites, drug molecules, polypeptides or nucleic acids to describe the operations of any protein. Moreover COMPOSEL can be scaled to express how genomes encode membrane structures and how our organs are infiltrated by pathogens such as SARS-CoV-2.

The critical role of the endolysosomal system in cerebral ischemia

Cerebral ischemia is a serious disease that triggers sequential pathological mechanisms, leading to significant morbidity and mortality. Although most studies to date have typically focused on the lysosome, a single organelle, current evidence supports that the function of lysosomes cannot be separated from that of the endolysosomal system as a whole. The associated membrane fusion functions of this system play a crucial role in the biodegradation of cerebral ischemia-related products. Here, we review the regulation of and the changes that occur in the endolysosomal system after cerebral ischemia, focusing on the latest research progress on membrane fusion function. Numerous proteins, including N-ethylmaleimide-sensitive factor and lysosomal potassium channel transmembrane protein 175, regulate the function of this system. However, these proteins are abnormally expressed after cerebral ischemic injury, which disrupts the normal fusion function of membranes within the endolysosomal system and that between autophagosomes and lysosomes. This results in impaired "maturation" of the endolysosomal system and the collapse of energy metabolism balance and protein homeostasis maintained by the autophagy-lysosomal pathway. Autophagy is the final step in the endolysosomal pathway and contributes to maintaining the dynamic balance of the system. The process of autophagosome-lysosome fusion is a necessary part of autophagy and plays a crucial role in maintaining energy homeostasis and clearing aging proteins. We believe that, in cerebral ischemic injury, the endolysosomal system should be considered as a whole rather than focusing on the lysosome. Understanding how this dynamic system is regulated will provide new ideas for the treatment of cerebral ischemia.

Specific interactions of peripheral membrane proteins with lipids: what can molecular simulations show us?

Peripheral membrane proteins (PMPs) can reversibly and specifically bind to biological membranes to carry out functions such as cell signalling, enzymatic activity, or membrane remodelling. Structures of these proteins and of their lipid-binding domains are typically solved in a soluble form, sometimes with a lipid or lipid headgroup at the binding site. To provide a detailed molecular view of PMP interactions with the membrane, computational methods such as molecular dynamics (MD) simulations can be applied. Here, we outline recent attempts to characterise these binding interactions, focusing on both intracellular proteins, such as phosphatidylinositol phosphate (PIP)-binding domains, and extracellular proteins such as glycolipid-binding bacterial exotoxins. We compare methods used to identify and analyse lipid-binding sites from simulation data and highlight recent work characterising the energetics of these interactions using free energy calculations. We describe how improvements in methodologies and computing power will help MD simulations to continue to contribute to this field in the future.