Dynamic protein interfaces and conformational landscapes of membrane protein complexes

TNF receptors: Structure-function relationships and therapeutic targeting strategies

Tumor necrosis factor receptors (TNFR1 and TNFR2) play key roles in mediating inflammatory response and cell death signaling, which are associated with autoimmune disorders, neurodegenerative diseases, and cancers. The structure-function relationships of TNF receptors and their ligands determine the activation or inhibition of downstream signaling pathways. Available crystal structures have provided critical insights into the therapeutic targeting strategies of TNF receptors and their signaling networks. In this review, we discuss the potential of targeting receptor-ligand and receptor-receptor interactions in a competitive manner as well as perturbing receptor conformational dynamics through an allosteric mechanism to modulate TNF receptor signaling. We propose that conformational states of TNF receptors can act as a molecular switch in determining their functions and are important therapeutic targets. The knowledge of the structure-function relationships of TNF receptors can be applied to translational high-throughput drug screening and design of novel receptor-specific modulators with enhanced pharmacological properties.

Enhanced Sampling Molecular Dynamics Simulations Reveal Transport Mechanism of Glycoconjugate Drugs through GLUT1

Glucose transporters GLUT1 belong to the major facilitator superfamily and are essential to human glucose uptake. The overexpression of GLUT1 in tumor cells designates it as a pivotal target for glycoconjugate anticancer drugs. However, the interaction mechanism of glycoconjugate drugs with GLUT1 remains largely unknown. Here, we employed all-atom molecular dynamics simulations, coupled to steered and umbrella sampling techniques, to examine the thermodynamics governing the transport of glucose and two glycoconjugate drugs (i.e., 6-D-glucose-conjugated methane sulfonate and 6-D-glucose chlorambucil) by GLUT1. We characterized the specific interactions between GLUT1 and substrates at different transport stages, including substrate recognition, transport, and releasing, and identified the key residues involved in these procedures. Importantly, our results described, for the first time, the free energy profiles of GLUT1-transporting glycoconjugate drugs, and demonstrated that H160 and W388 served as important gates to regulate their transport via GLUT1. These findings provide novel atomic-scale insights for understanding the transport mechanism of GLUT1, facilitating the discovery and rational design of GLUT1-targeted anticancer drugs.

Membrane Heteroreceptor Complexes as Second-Order Protein Modulators: A Novel Integrative Mechanism through Allosteric Receptor-Receptor Interactions

Bioluminescence and fluorescence resonance energy transfer (BRET and FRET) together with the proximity ligation method revealed the existence of G-protein-coupled receptors, Ionotropic and Receptor tyrosine kinase heterocomplexes, e.g., A2AR-D2R, GABAA-D5R, and FGFR1-5-HT1AR heterocomplexes. Molecular integration takes place through allosteric receptor-receptor interactions in heteroreceptor complexes of synaptic and extra-synaptic regions. It involves the modulation of receptor protomer recognition, signaling and trafficking, as well as the modulation of behavioral responses. Allosteric receptor-receptor interactions in hetero-complexes give rise to concepts like meta-modulation and protein modulation. The introduction of receptor-receptor interactions was the origin of the concept of meta-modulation provided by Katz and Edwards in 1999, which stood for the fine-tuning or modulation of nerve cell transmission. In 2000-2010, Ribeiro and Sebastiao, based on a series of papers, provided strong support for their view that adenosine can meta-modulate (fine-tune) synaptic transmission through adenosine receptors. However, another term should also be considered: protein modulation, which is the key feature of allosteric receptor-receptor interactions leading to learning and consolidation by novel adapter proteins to memory. Finally, it must be underlined that allosteric receptor-receptor interactions and their involvement both in brain disease and its treatment are of high interest. Their pathophysiological relevance has been obtained, especially for major depressive disorder, cocaine use disorder, and Parkinson's disease.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

Palmitoylation of the Glucagon-like Peptide-1 Receptor Modulates Cholesterol Interactions at the Receptor-Lipid Microenvironment

The G protein-coupled receptor (GPCR) superfamily of cell surface receptors has been shown to be functionally modulated by post-translational modifications. The glucagon-like peptide receptor-1 (GLP-1R), which is a drug target in diabetes and obesity, undergoes agonist-dependent palmitoyl tail conjugation. The palmitoylation in the C-terminal domain of GLP-1R has been suggested to modulate the receptor-lipid microenvironment. In this work, we have performed coarse-grain molecular dynamics simulations of palmitoylated and nonpalmitoylated GLP-1R to analyze the differential receptor-lipid interactions. Interestingly, the placement and dynamics of the C-terminal domain of GLP-1R are found to be directly dependent on the palmitoyl tail. We observe that both cholesterol and phospholipids interact with the receptor but display differential interactions in the presence and absence of the palmitoyl tail. We characterize important cholesterol-binding sites and validate sites that have been previously reported in experimentally resolved structures of the receptor. We show that the receptor acts like a conduit for cholesterol flip-flop by stabilizing cholesterol in the membrane core. Taken together, our work represents an important step in understanding the molecular effects of lipid modifications in GPCRs.

Cofilin-Membrane Interactions: Electrostatic Effects in Phosphoinositide Lipid Binding

The actin cytoskeleton interacts with the cell membrane primarily through the indirect interactions of actin-binding proteins such as cofilin-1. The molecular mechanisms underlying the specific interactions of cofilin-1 with membrane lipids are still unclear. Here, we performed coarse-grain molecular dynamics simulations of cofilin-1 with complex lipid bilayers to analyze the specificity of protein-lipid interactions. We observed the maximal interactions with phosphoinositide (PIP) lipids, especially PIP₂ and PIP₃ lipids. A good match was observed between the residues predicted to interact and previous experimental studies. The clustering of PIP lipids around the membrane bound protein leads to an overall lipid demixing and gives rise to persistent membrane curvature. Further, through a series of control simulations, we observe that both electrostatics and geometry are critical for specificity of lipid binding. Our current study is a step towards understanding the physico-chemical basis of cofilin-PIP lipid interactions.

Synergistic and Competitive Lipid Interactions in the Serotonin1A Receptor Microenvironment

The interaction of lipids with G-protein-coupled receptors (GPCRs) has been shown to modulate and dictate several aspects of GPCR organization and function. Diverse lipid interaction sites have been identified from structural biology, bioinformatics, and molecular dynamics studies. For example, multiple cholesterol interaction sites have been identified in the serotonin_1A receptor, along with distinct and overlapping sphingolipid interaction sites. How these lipids interact with each other and what is the resultant effect on the receptor is still not clear. In this work, we have analyzed lipid-lipid crosstalk at the receptor of the serotonin_1A receptor embedded in a membrane bilayer that mimics the neuronal membrane composition by long coarse-grain simulations. Using a set of similarity coefficients, we classified lipids that bind at the receptor together as synergistic cobinding, and those that bind individually as competitive. Our results show that certain lipids interact with the serotonin_1A receptor in synergy with each other. Not surprisingly, the ganglioside GM1 and cholesterol show a synergistic cobinding, along with the relatively uncommon GM1-phosphatidylethanolamine (PE) and cholesterol-PE synergy. In contrast, certain lipid pairs such as cholesterol and sphingomyelin appear to be in competition at several sites, despite their coexistence in lipid nanodomains. In addition, we observed intralipid competition between two lipid tails, with the receptor exhibiting increased interactions with the unsaturated lipid tails. We believe our work represents an important step in understanding the diversity of GPCR-lipid interactions and exploring synergistic cobinding and competition in natural membranes.

Bench to bedside: The ambitious goal of transducing medicinal chemistry from the lab to the clinic

This paper deals with a critical examination on the possibility of quantitatively predicting the in vivo activity of new chemical entities (NCEs) by making use of in silico and in vitro data including three-dimensional structure of drug-target complex, thermodynamic and crowding parameters, ADME (absorption, distribution, metabolism, excretion) properties, and off-target (toxic) interactions. This formidable challenge is still a dream, given the presently occurring exceedingly high (>95%) attrition rates of NCEs. As a solution we envisage exploiting advanced AI (artificial intelligence) algorithms. In fact, very recent AI implemented programs proved remarkably effective and accurate in predicting the 3D architecture of (any) protein, starting from the amino-acid sequence only. The same accuracy could not be obtained using classical conformational studies. Apart from these breakthrough results, AI algorithms could be profitably used to extract valuable information from the huge amount of data so far accumulated from previous studies. In case of positive results, the drug discovery procedure would be sensibly accelerated, and the relative costs remarkably reduced.

Connexins and Pannexins—Similarities and Differences According to the FOD-M Model

Connexins and pannexins are the transmembrane proteins of highly distinguished biological activity in the form of transport of molecules and electrical signals. Their common role is to connect the external environment with the cytoplasm of the cell, while connexin is also able to link two cells together allowing the transport from one to another. The analysis presented here aims to identify the similarities and differences between connexin and pannexin. As a comparative criterion, the hydrophobicity distribution in the structure of the discussed proteins was used. The comparative analysis is carried out with the use of a mathematical model, the FOD-M model (fuzzy oil drop model in its Modified version) expressing the specificity of the membrane’s external field, which in the case of the discussed proteins is significantly different from the external field for globular proteins in the polar environment of water. The characteristics of the external force field influence the structure of protein allowing the activity in a different environment.

Insights into the dynamic interactions at chemokine-receptor interfaces and mechanistic models of chemokine binding

Chemokine receptors are the central signaling hubs of several processes such as cell migration, chemotaxis and cell positioning. In this graphical review, we provide an overview of the structural and mechanistic principles governing chemokine recognition that are currently emerging. Structural models of chemokine-receptor co-complexes with endogenous chemokines, viral chemokines and therapeutics have been resolved that highlight multiple interaction sites, termed as CRS1, CRS1.5 etc. The first site of interaction has been shown to be the N-terminal domain of the receptors (CRS1 site). A large structural flexibility of the N-terminal domain has been reported that was supported by both experimental and simulation studies. Upon chemokine binding, the N-terminal domain appears to show constricted dynamics and opens up to interact with the chemokine via a large interface. The subsequent sites such as CRS1.5 and CRS2 sites have been structurally well resolved although differences arise such as the localization of the N-terminus of the ligand to a major or minor pocket of the orthosteric binding site. Several computational studies have highlighted the dynamic protein-protein interface at the CRS1 site that seemingly appears to resolve the differences in NMR and mutagenesis studies. Interestingly, the differential dynamics at the CRS1 site suggests a mixed model of binding with complex signatures of both conformational selection and induced fit models. Integrative experimental and computational approaches could help unravel the structural basis of promiscuity and specificity in chemokine-receptor binding and open up new avenues of therapeutic design.

Mass Spectrometry Methods for Measuring Protein Stability

Mass spectrometry is a central technology in the life sciences, providing our most comprehensive account of the molecular inventory of the cell. In parallel with developments in mass spectrometry technologies targeting such assessments of cellular composition, mass spectrometry tools have emerged as versatile probes of biomolecular stability. In this review, we cover recent advancements in this branch of mass spectrometry that target proteins, a centrally important class of macromolecules that accounts for most biochemical functions and drug targets. Our efforts cover tools such as hydrogen-deuterium exchange, chemical cross-linking, ion mobility, collision induced unfolding, and other techniques capable of stability assessments on a proteomic scale. In addition, we focus on a range of application areas where mass spectrometry-driven protein stability measurements have made notable impacts, including studies of membrane proteins, heat shock proteins, amyloidogenic proteins, and biotherapeutics. We conclude by briefly discussing the future of this vibrant and fast-moving area of research.

Molecular Mechanisms Underlying Caveolin-1 Mediated Membrane Curvature

Caveolin-1 is one of the main protein components of caveolae that acts as a mechanosensor at the cell membrane. The interactions of caveolin-1 with membranes have been shown to lead to complex effects such as curvature and the clustering of specific lipids. Here, we review the emerging concepts on the molecular interactions of caveolin-1, with a focus on insights from coarse-grain molecular dynamics simulations. Consensus structural models of caveolin-1 report a helix-turn-helix core motif with flanking domains of higher disorder that could be membrane composition dependent. Caveolin-1 appears to be mainly surface-bound and does not embed very deep in the membrane to which it is bound. The most interesting aspect of caveolin-1 membrane binding is the interplay of cholesterol clustering and membrane curvature. Although cholesterol has been reported to cluster in the vicinity of caveolin-1 by several approaches, simulations show that the clustering is maximal in membrane leaflet opposing the surface-bound caveolin-1. The intrinsic negative curvature of cholesterol appears to stabilize the negative curvature in the opposing leaflet. In fact, the simulations show that blocking cholesterol clustering (through artificial position restraints) blocks membrane curvature, and vice versa. Concomitant with cholesterol clustering is sphingomyelin clustering, again in the opposing leaflet, but in a concentration-dependent manner. The differential stress due to caveolin-1 binding and the inherent asymmetry of the membrane leaflets could be the determinant for membrane curvature and needs to be further probed. The review is an important step to reconcile the molecular level details emerging from simulations with the mesoscopic details provided by state of the art experimental approaches.

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field

The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit treatment of electronic polarization, such an extension must be accompanied by optimization of van der Waals (vdW) interactions, in the context of the Lennard-Jones (LJ) formalism in the present study. This is particularly challenging due to the extensive nature of chemical space combined with the correlated nature of LJ parameters. To address this challenge, a deep learning (DL)-based parametrization framework is developed, allowing for sampling of wide ranges of LJ parameters targeting experimental condensed phase thermodynamic properties. The present work utilizes this framework to develop the LJ parameters for atoms associated with four distinct groups covering 10 different atom types. Final parameter selection was facilitated by quantum mechanical data on rare-gas interactions with the training set molecules. The chosen parameters were then validated through experimental hydration free energies and condensed phase thermodynamic properties of validation set molecules to confirm transferability. The ultimate outcome of utilizing this framework is a set of LJ parameters in the context of the polarizable Drude FF, which demonstrated improvement in the reproduction of both experimental pure solvent and crystal properties and hydration free energies of the molecules compared to the additive CHARMM General FF (CGenFF) including the ability of the Drude FF to accurately reproduce both experimental pure solvent properties and hydration free energies. The study also shows how correlations between difference in the reproduction of condensed phase data between model compounds may be used to direct the selection of new atom types and training set molecules during FF development.

Allosteric modulation of the chemokine receptor-chemokine CXCR4-CXCL12 complex by tyrosine sulfation

The chemokine receptor CXCR4 and its cognate ligand CXCL12 mediate pathways that lead to cell migration and chemotaxis. Although the structural details of related receptor-ligand complexes have been resolved, the roles of the N-terminal domain of the receptor and post-translational sulfation that are determinants of ligand selectivity and affinity remain unclear. Here, we analyze the structural dynamics induced by receptor sulfation by combining molecular dynamics, docking and network analysis. The sulfotyrosine residues, 7Ys^N-term, 12Ys^N-term and 21Ys^N-term allow the N-terminal domain of the apo-sulfated receptor to adopt an "open" conformation that appears to facilitate ligand binding. The overall topology of the CXCR4-CXCL12 complex is independent of the sulfation state, but an extensive network of protein-protein interactions characterizes the sulfated receptor, in line with its increased ligand affinity. The altered interactions of sulfotyrosine residues, such as 21Ys^N-term-47R^CXCL12 replacing the 21Y^N-term-13F^CXCL12 interaction, propagate via allosteric pathways towards the receptor lumen. In particular, our results suggest that the experimentally-reported receptor-ligand interactions 262D^6.58-8R^CXCL12 and 277E^7.28-12R^CXCL12 could be dependent on the sulfation state of the receptor and need to be carefully analyzed. Our work is an important step in understanding chemokine-receptor interactions and how post-translational modifications could modulate receptor-ligand complexes.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Molecular determinants of GPCR pharmacogenetics: Deconstructing the population variants in β2-adrenergic receptor

G protein-coupled receptors (GPCRs) are membrane proteins that play a central role in cell signaling and constitute one of the largest classes of drug targets. The molecular mechanisms underlying GPCR function have been characterized by several experimental and computational methods and provide an understanding of their role in physiology and disease. Population variants arising from nsSNPs affect the native function of GPCRs and have been implicated in differential drug response. In this chapter, we provide an overview on GPCR structure and activation, with a special focus on the β₂-adrenergic receptor (β₂-AR). First, we discuss the current understanding of the structural and dynamic features of the wildtype receptor. Subsequently, the population variants identified in this receptor from clinical and large-scale genomic studies are described. We show how computational approaches such as bioinformatics tools and molecular dynamics simulations can be used to characterize the variant receptors in comparison to the wildtype receptor. In particular, we discuss three examples of clinically important variants and discuss how the structure and function of these variants differ from the wildtype receptor at a molecular level. Overall, the chapter provides an overview of structure and function of GPCR variants and is a step towards the study of inter-individual differences and personalized medicine.

Computational modeling and experimental validation of the EPI-X4/CXCR4 complex allows rational design of small peptide antagonists

EPI-X4, a 16-mer fragment of albumin, is a specific endogenous antagonist and inverse agonist of the CXC-motif-chemokine receptor 4 (CXCR4) and thus a key regulator of CXCR4 function. Accordingly, activity-optimized synthetic derivatives of EPI-X4 are promising leads for the therapy of CXCR4-linked disorders such as cancer or inflammatory diseases. We investigated the binding of EPI-X4 to CXCR4, which so far remained unclear, by means of biomolecular simulations combined with experimental mutagenesis and activity studies. We found that EPI-X4 interacts through its N-terminal residues with CXCR4 and identified its key interaction motifs, explaining receptor antagonization. Using this model, we developed shortened EPI-X4 derivatives (7-mers) with optimized receptor antagonizing properties as new leads for the development of CXCR4 inhibitors. Our work reveals the molecular details and mechanism by which the first endogenous peptide antagonist of CXCR4 interacts with its receptor and provides a foundation for the rational design of improved EPI-X4 derivatives.

Caveolin induced membrane curvature and lipid clustering: two sides of the same coin?

Caveolin-1 (cav-1) is a multi-domain membrane protein that is a key player in cell signaling, endocytosis and mechanoprotection. It is the principle component of cholesterol-rich caveolar domains and has been reported to induce membrane curvature. The molecular mechanisms underlying the interactions of cav-1 with complex membranes, leading to modulation of membrane topology and the formation of cholesterol-rich domains, remain elusive. In this study, we aim to understand the effect of lipid composition by analyzing the interactions of cav-1 with complex membrane bilayers comprised of about sixty lipid types. We have performed a series of coarse-grain molecular dynamics simulations using the Martini force-field with a cav-1 protein construct (residue 82-136) that includes the membrane binding domains and a palmitoyl tail. We observe that cav-1 induces curvature in this complex membrane, though it is restricted to a nanometer length scale. Concurrently, we observe a clustering of cholesterol, sphingolipids and other lipid molecules leading to the formation of nanodomains. Direct microsecond timescale interactions are observed for specific lipids such as cholesterol, phosphatidylserine and phosphatidylethanolamine lipid types. The results indicate that there is an interplay between membrane topology and lipid species. Our work is a step toward understanding how lipid composition and organization regulate the formation of caveolae, in the context of endocytosis and cell signaling.

Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces

The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.

All-Atom Molecular Dynamics Elucidating Molecular Mechanisms of Single-Transmembrane Model Peptide Dimerization in a Lipid Bilayer

Protein-protein interactions between transmembrane helices are essential elements for membrane protein structures and functions. To understand the effects of peptide sequences and lipid compositions on these interactions, single-molecule experiments using model systems comprising artificial peptides and membranes have been extensively performed. However, their dynamic behavior at the atomic level remains largely unclear. In this study, we applied the all-atom molecular dynamics (MD) method to simulate the interactions of single-transmembrane helical peptide dimers in membrane environments, which has previously been analyzed by single-molecule experiments. The simulations were performed with two peptides (Ala- and Leu-based artificially designed peptides, termed "host peptide", and the host peptide added with the GXXXG motif, termed "GXXXG peptide"), two membranes (pure-POPC and POPC mixed with 30% cholesterols), and two dimer directions (parallel and antiparallel), consistent with those in the previous experiment. As a result, the MD simulations with parallel dimers reproduced the experimentally observed tendency that introducing cholesterols weakened the interactions in the GXXXG dimer and facilitated those in the host dimer. Our simulation suggested that the host dimer formed hydrogen bonds but the GXXXG dimer did not. However, some discrepancies were also observed between the experiments and simulations. Limitations in the space and time scales of simulations restrict the large-scale undulation and peristaltic motions of the membranes, resulting in differences in lateral pressure profiles. This effect could cause a discrepancy in the rotation angles of helices against the membrane normal.

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

The dynamic interactions between G protein-coupled receptors (GPCRs) and their cognate protein partners are central to several cell signaling pathways. For example, the association of CXC chemokine receptor 1 (CXCR1) with its cognate chemokine, interleukin-8 (IL8 or CXCL8) initiates pathways leading to neutrophil-mediated immune responses. The N-terminal domain of chemokine receptors confers ligand selectivity, but unfortunately the conformational dynamics of this intrinsically disordered region remains unresolved. In this work, we have explored the interaction of CXCR1 with IL8 by microsecond time scale coarse-grain simulations, complemented by atomistic models and NMR chemical shift predictions. We show that the conformational plasticity of the apo-receptor N-terminal domain is restricted upon ligand binding, driving it to an open C-shaped conformation. Importantly, we corroborated the dynamic complex sampled in our simulations against chemical shift perturbations reported by previous NMR studies and show that the trends are similar. Our results indicate that chemical shift perturbation is often not a reporter of residue contacts in such dynamic associations. We believe our results represent a step forward in devising a strategy to understand intrinsically disordered regions in GPCRs and how they acquire functionally important conformational ensembles in dynamic protein-protein interfaces.

Role of Cholesterol in Transmembrane Dimerization of the ErbB2 Growth Factor Receptor

The association of ErbB2 growth factor receptors is critical for cell growth and potentiates tumor proliferation in several cancer types. An important aspect in ErbB2 association is the role of lipids such as cholesterol, especially since their metabolism is often reprogrammed in cancer cells. Here, we have coupled metadynamics with coarse-grain simulations to identify cholesterol effects in the transmembrane dimerization of ErbB2 receptors. Overall, cholesterol interactions are observed with the receptor that directly tunes the association energetics. Several dimer conformations are identified both in the presence and absence of cholesterol, although the dimer regime appears to be more favorable in the presence of cholesterol. We observe an overall modulation of the underlying energy profile and the symmetric active and inactive conformational states are not distinguished in the presence of cholesterol. We show that cholesterol binds to the receptor transmembrane domain at a site (CRAC motif) that overlaps with the dimer interface (SmXXXSm motif). The competition between the transmembrane interactions and cholesterol interactions decides the final conformational landscape. Our work is an important step toward characterizing cholesterol effects in ErbB2 membrane receptor function.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Lipid-dependent conformational landscape of the ErbB2 growth factor receptor dimers

Altered lipid metabolism has been linked to cancer development and progression. Several roles have been attributed to the increased saturation and length of lipid acyl tails observed in tumors, but its effect on signaling receptors is still emerging. In this work, we have analyzed the lipid dependence of the ErbB2 growth factor receptor dimerization that plays an important role in the pathogenesis of breast cancer. We have performed coarse-grain ensemble molecular dynamics simulations to comprehensively sample the ErbB2 monomer-dimer association. Our results indicate a dynamic dimer state with a complex conformational landscape that is modulated with increasing lipid tail length. We resolve the native N-terminal "active" and C-terminal "inactive" conformations in all membrane compositions. However, the relative population of the N-terminal and C-terminal conformers is dependent on length of the saturated lipid tails. In short-tail membranes, additional non-specific dimers are observed which are reduced or absent in long-tailed bilayers. Our results indicate that the relative population as well as the structure of the dimer state is modulated by membrane composition. We have correlated these differences to local perturbations of the membrane around the receptor. Our work is an important step in characterizing ErbB dimers in healthy and diseased states and emphasize the importance of sampling lipid dynamics in understanding receptor association.