Transmembrane dimers of type 1 receptors sample alternate configurations: MD simulations using coarse grain Martini 3 versus AlphaFold2 Multimer

Simulation of Neurotrophin Receptor Transmembrane Helix Interactions Reveals Active States and Distinct Signaling Mechanisms

Neurotrophin (NT) receptor signaling regulates neuronal survival, axonal and dendritic network maintenance, differentiation, and synaptic plasticity. Signaling is initiated by binding of NT to the extracellular domain of NT receptor dimers, leading to activation of the receptor and signal propagation intracellularly. How this activating signal is mediated by the single-pass transmembrane (TM) helical domain of the receptor and what the relation between domain sequence and signaling mechanism is remain unclear. The structure and dynamics of the TM domain of the receptor dimers in the active and inactive states for intracellular signaling are still elusive, with NMR structures capturing only a single state. Here, we carried out unbiased and enhanced sampling molecular dynamics simulations of the TM domain dimers of the wild-type p75, TrkA and TrkB NT receptors and selected mutants in micelle and bilayer lipid environments at atomistic and coarse-grained levels of representation. The coarse-grained simulations enabled exploration of multiple states of the TM domain dimers and revealed the influence of the lipid environment on the TM helix arrangements. From the simulations, we identify active and inactive TM helix arrangements of the p75 and TrkA receptors that are supported by experimental data and suggest two different signaling mechanisms through the C-terminal regions of the TM helices. For TrkB, a single dominant but less energetically stable arrangement of the TM domain dimer is observed. These findings have implications for mechanistic studies of NT receptor signaling and the design of neuroprotective drugs to stabilize specific states of the TM domain of the receptors.

Cholesterol-dependent dimerization and conformational dynamics of EphA2 receptors from coarse-grained and all-atom simulations

The EphA2 transmembrane receptor regulates cellular growth, differentiation, and motility, and its overexpression in various cancers makes it a potential biomarker for clinical cancer management. EphA2 signaling occurs through ligand-induced dimerization where the transmembrane (TM) and juxtamembrane (JM) domains play crucial roles in stabilizing the dimer conformations, thereby facilitating signal transduction. Electrostatic interactions between basic JM residues and signaling lipids (PIP2 and PIP3) regulate phosphorylation while cholesterol's potential role in modulating EphA2 activation remains unclear. To investigate this, we modeled the TM-full JM peptide of EphA2 and employed coarse-grain and all-atom simulations to investigate its dimerization in cholesterol-rich and cholesterol-deficient membranes. Our findings reveal that cholesterol stabilizes specific TM dimers and TM-JM interactions with PIP2, highlighting the importance of membrane composition in EphA2 dimerization, oligomerization, and clustering. These insights enhance our understanding of lipid-mediated regulation of EphA2 and its implications in receptor signaling and cancer progression.

Cholesterol-Dependent Dimerization and Conformational Dynamics of EphA2 Receptors: Insights from Coarse-Grained and All-Atom Simulations

The EphA2 transmembrane receptor regulates cellular growth, differentiation, and motility, and its overexpression in various cancers makes it a potential biomarker for clinical cancer management. EphA2 signaling occurs through ligand-induced dimerization, where the transmembrane (TM) and juxtamembrane (JM) domains play crucial roles in stabilizing the dimer conformations and thereby facilitating signal transduction. Electrostatic interactions between basic JM residues and signaling lipids (PIP2 and PIP3) regulate phosphorylation while Cholesterol's potential role in modulating EphA2 activation remains unclear. To investigate this, we modeled the TM-full JM peptide of EphA2 and employed coarse-grain and all-atom simulations to investigate its dimerization in cholesterol-rich and cholesterol-deficient membranes. Our findings reveal that cholesterol stabilizes specific TM dimers and TM-JM interactions with PIP2, highlighting the importance of membrane composition in EphA2 dimerization, oligomerization, and clustering. These insights enhance our understanding of lipid-mediated regulation of EphA2 and its implications in receptor signaling and cancer progression.

Bacterial polysaccharide lyase family 33: Specificity from an evolutionarily conserved binding tunnel

Acidic glycans are essential for the biology of multicellular eukaryotes. To utilize them, microbial life including symbionts and pathogens has evolved polysaccharide lyases (PL) that cleave their 1,4 glycosidic linkages via a β-elimination mechanism. PL family 33 (PL33) enzymes have the unusual ability to target a diverse range of glycosaminoglycans (GAGs), as well as the bacterial polymer, gellan gum. In order to gain more detailed insight into PL33 activities we recombinantly expressed 10 PL33 members derived from all major environments and further elucidated the detailed biochemical and biophysical properties of five, showing that their substrate specificity is conferred by variations in tunnel length and topography. The key amino acids involved in catalysis and substrate interactions were identified, and employing a combination of complementary biochemical, structural, and modeling approaches, we show that the tunnel topography is induced by substrate binding to the glycan. Structural and bioinformatic analyses revealed that these features are conserved across several lyase families as well as in mammalian GAG epimerases.

Lys716 in the transmembrane domain of yeast mitofusin Fzo1 modulates anchoring and fusion

Outer mitochondrial membrane fusion, a vital cellular process, is mediated by mitofusins. However, the underlying molecular mechanism remains elusive. We have performed extensive multiscale molecular dynamics simulations to predict a model of the transmembrane (TM) domain of the yeast mitofusin Fzo1. Coarse-grained simulations of the two TM domain helices, TM1 and TM2, reveal a stable interface, which is controlled by the charge status of residue Lys716. Atomistic replica-exchange simulations further tune our model, which is confirmed by a remarkable agreement with an independent AlphaFold2 (AF2) prediction of Fzo1 in complex with its fusion partner Ugo1. Furthermore, the presence of the TM domain destabilizes the membrane, even more if Lys716 is charged, which can be an asset for initiating fusion. The functional role of Lys716 was confirmed with yeast experiments, which show that mutating Lys716 to a hydrophobic residue prevents mitochondrial fusion.

A Computational Approach in the Systematic Search of the Interaction Partners of Alternatively Spliced TREM2 Isoforms

Alzheimer's disease is the most common form of dementia, characterized by the pathological accumulation of amyloid-beta (Aβ) plaques and tau neurofibrillary tangles. Triggering receptor expressed on myeloid cells 2 (TREM2) is increasingly recognized as playing a central role in Aβ clearance and microglia activation in AD. The TREM2 gene transcriptional product is alternatively spliced to produce three different protein isoforms. The canonical TREM2 isoform binds to DAP12 to activate downstream pathways. However, little is known about the function or interaction partners of the alternative TREM2 isoforms. The present study utilized a computational approach in a systematic search for new interaction partners of the TREM2 isoforms by integrating several state-of-the-art structural bioinformatics tools from initial large-scale screening to one-on-one corroborative modeling and eventual all-atom visualization. CD9, a cell surface glycoprotein involved in cell-cell adhesion and migration, was identified as a new interaction partner for two TREM2 isoforms, and CALM, a calcium-binding protein involved in calcium signaling, was identified as an interaction partner for a third TREM2 isoform, highlighting the potential role of cell adhesion and calcium regulation in AD.

Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane

The accurate calculation of equilibrium constants for protein-protein association is of fundamental importance to quantitative biology and remains an outstanding challenge for computational biophysics. Traditionally, equilibrium constants have been computed from one-dimensional free energy surfaces derived from sampling along a single collective variable. Importantly, recent advances in enhanced sampling methodology have facilitated the characterization of multidimensional free energy landscapes, often exposing multiple thermodynamically important minima missed by more restrictive sampling methods. A key to the effectiveness of this multidimensional sampling approach is the identification of collective variables that effectively define the configurational space of dissociated and associated states. Here we present the application of two machine learning methods for the unbiased determination of collective variables for enhanced sampling and analysis of protein-protein association. Our results both validate prior work, based on intuition derived collective variables, and demonstrate the effectiveness of the machine learning methods for the identification of collective variables for association reactions in complex biomolecular systems.

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.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

The role of structural heterogeneity in the homodimerization of transmembrane proteins

The equilibrium association of transmembrane proteins plays a fundamental role in membrane protein function and cellular signaling. While the study of the equilibrium binding of single pass transmembrane proteins has received significant attention in experiment and simulation, the accurate assessment of equilibrium association constants remains a challenge to experiment and simulation. In experiment, there remain wide variations in association constants derived from experimental studies of the most widely studied transmembrane proteins. In simulation, state-of-the art methods have failed to adequately sample the thermodynamically relevant structures of the dimer state ensembles using coarse-grained models. In addition, all-atom force fields often fail to accurately assess the relative free energies of the dimer and monomer states. Given the importance of this fundamental biophysical process, it is essential to address these shortcomings. In this work, we establish an effective computational protocol for the calculation of equilibrium association constants for transmembrane homodimer formation. A set of transmembrane protein homodimers, used in the parameterization of the MARTINI v3 force field, are simulated using metadynamics, based on three collective variables. The method is found to be accurate and computationally efficient, providing a standard to be used in the future simulation studies using coarse-grained or all-atom models.