Allostery in membrane proteins

The complexities of ligand/receptor interactions: Exploring the role of molecular vibrations and quantum tunnelling

Molecular vibrations and quantum tunneling may link ligand binding to the function of pharmacological receptors. The well-established lock-and-key model explains a ligand's binding and recognition by a receptor; however, a general mechanism by which receptors translate binding into activation, inactivation, or modulation remains elusive. The Vibration Theory of Olfaction was proposed in the 1930s to explain this subset of receptor-mediated phenomena by correlating odorant molecular vibrations to smell, but a mechanism was lacking. In the 1990s, inelastic electron tunneling was proposed as a plausible mechanism for translating molecular vibration to odorant physiology. More recently, studies of ligands' vibrational spectra and the use of deuterated ligand analogs have provided helpful information to study this admittedly controversial hypothesis in metabotropic receptors other than olfactory receptors. In the present work, based in part on published experiments from our laboratory using planarians as an experimental organism, I will present a rationale and possible experimental approach for extending this idea to ligand-gated ion channels.

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.

Free Energy Surface and Molecular Mechanism of Slow Structural Transitions in Lipid Bilayers

Lipid membrane remodeling, crucial for many cellular processes, is governed by the coupling of membrane structure and shape fluctuations. Given the importance of the ∼ nm length scale, details of the transition intermediates for conformational change are not fully captured by a continuum-mechanical description. Slow dynamics and the lack of knowledge of reaction coordinates (RCs) for biasing methods pose a challenge for all-atom (AA) simulations. Here, we map system dynamics on Langevin dynamics in a normal mode space determined from an elastic network model representation for the lipid-water Hamiltonian. AA molecular dynamics (MD) simulations are used to determine model parameters, and Langevin dynamics predictions for bilayer structural, mechanical, and dynamic properties are validated against MD simulations and experiments. Transferability to describe the dynamics of a larger lipid bilayer and a heterogeneous membrane-protein system is assessed. A set of generic RCs for pore formation in two tensionless bilayers is obtained by coupling Langevin dynamics to the underlying energy landscape for membrane deformations. Structure evolution is carried out by AA MD, wherein the generic RCs are used in a path metadynamics or an umbrella sampling simulation to determine the thermodynamics of pore formation and its molecular determinants, such as the role of distinct bilayer motions, lipid solvation, and lipid packing.

Unmasking an Allosteric Binding Site of the Papain-like Protease in SARS-CoV-2: Molecular Dynamics Simulations of Corticosteroids

To date, mechanistic insights into many clinical drugs against COVID-19 remain unexplored. Dexamethasone, a corticosteroid, is one of them. While treating the entire corticosteroid database, including vitamins D2 and D3, with cutting-edge computational techniques, several intriguing results are unfolded. From the top-notch candidates, dexamethasone is likely to inhibit the viral main protease (Mpro), with vitamin D3 exhibiting multitarget [Mpro, papain-like protease (PLpro), and nucleocapsid protein (N-pro)] roles and ciclesonide's dynamic flipping disinterring a cryptic allosteric site in the PLpro enzyme. The results rationalize why these drugs improve the health of COVID-19 patients. Understanding an enzyme's secret binding site is essential to understanding how the enzyme works and how to inhibit its function. Ciclesonide's allosteric inhibition could not only jeopardize PLpro's catalytic role in polyprotein processing but also make it less vulnerable to the host body's defense machinery. Hotspot residues in the identified allosteric site could be considered for effective therapeutic designs against PLpro.

Novel Approaches to the Establishment of Local Microenvironment from Resorbable Biomaterials in the Brain In Vitro Models

The development of brain in vitro models requires the application of novel biocompatible materials and biopolymers as scaffolds for controllable and effective cell growth and functioning. The "ideal" brain in vitro model should demonstrate the principal features of brain plasticity like synaptic transmission and remodeling, neurogenesis and angiogenesis, and changes in the metabolism associated with the establishment of new intercellular connections. Therefore, the extracellular scaffolds that are helpful in the establishment and maintenance of local microenvironments supporting brain plasticity mechanisms are of critical importance. In this review, we will focus on some carbohydrate metabolites-lactate, pyruvate, oxaloacetate, malate-that greatly contribute to the regulation of cell-to-cell communications and metabolic plasticity of brain cells and on some resorbable biopolymers that may reproduce the local microenvironment enriched in particular cell metabolites.

Binding and Functional Folding (BFF): A Physiological Framework for Studying Biomolecular Interactions and Allostery

EF-hand Ca2+-binding proteins (CBPs), such as S100 proteins (S100s) and calmodulin (CaM), are signaling proteins that undergo conformational changes upon increasing intracellular Ca2+. Upon binding Ca2+, S100 proteins and CaM interact with protein targets and induce important biological responses. The Ca2+-binding affinity of CaM and most S100s in the absence of target is weak (CaKD > 1 μM). However, upon effector protein binding, the Ca2+ affinity of these proteins increases via heterotropic allostery (CaKD < 1 μM). Because of the high number and micromolar concentrations of EF-hand CBPs in a cell, at any given time, allostery is required physiologically, allowing for (i) proper Ca2+ homeostasis and (ii) strict maintenance of Ca2+-signaling within a narrow dynamic range of free Ca2+ ion concentrations, [Ca2+]free. In this review, mechanisms of allostery are coalesced into an empirical "binding and functional folding (BFF)" physiological framework. At the molecular level, folding (F), binding and folding (BF), and BFF events include all atoms in the biomolecular complex under study. The BFF framework is introduced with two straightforward BFF types for proteins (type 1, concerted; type 2, stepwise) and considers how homologous and nonhomologous amino acid residues of CBPs and their effector protein(s) evolved to provide allosteric tightening of Ca2+ and simultaneously determine how specific and relatively promiscuous CBP-target complexes form as both are needed for proper cellular function.

DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design

SUMMARY

The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM.

AVAILABILITY AND IMPLEMENTATION

DREAMM web server is accessible via https://dreamm.ni4os.eu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The orchestrated signaling by PI3Kα and PTEN at the membrane interface

The oncogene PI3Kα and the tumor suppressor PTEN represent two antagonistic enzymatic activities that regulate the interconversion of the phosphoinositide lipids PI(4,5)P2 and PI(3,4,5)P3 in membranes. As such, they are defining components of phosphoinositide-based cellular signaling and membrane trafficking pathways that regulate cell survival, growth, and proliferation, and are often deregulated in cancer. In this review, we highlight aspects of PI3Kα and PTEN interplay at the intersection of signaling and membrane trafficking. We also discuss the mechanisms of PI3Kα- and PTEN- membrane interaction and catalytic activation, which are fundamental for our understanding of the structural and allosteric implications on signaling at the membrane interface and may aid current efforts in pharmacological targeting of these proteins.

Molecular basis of hUHRF1 allosteric activation for synergistic histone modification binding by PI5P

Chromatin marks are recognized by distinct binding modules, many of which are embedded in multidomain proteins. How the different functionalities of such complex chromatin modulators are regulated is often unclear. Here, we delineated the interplay of the H3 amino terminus- and K9me-binding activities of the multidomain hUHRF1 protein. We show that the phosphoinositide PI5P interacts simultaneously with two distant flexible linker regions connecting distinct domains of hUHRF1. The binding is dependent on both, the polar head group, and the acyl part of the phospholipid and induces a conformational rearrangement juxtaposing the H3 amino terminus and K9me3 recognition modules of the protein. In consequence, the two features of the H3 tail are bound in a multivalent, synergistic manner. Our work highlights a previously unidentified molecular function for PI5P outside of the context of lipid mono- or bilayers and establishes a molecular paradigm for the allosteric regulation of complex, multidomain chromatin modulators by small cellular molecules.

A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics

Activation of G-protein-coupled receptors (GPCRs) is mediated by molecular switches throughout the transmembrane region of the receptor. In this work, we continued along the path of a previous computational study wherein energy transport in the β2 Adrenergic Receptor (β2-AR) was examined and allosteric switches were identified in the molecular structure through the reorganization of energy transport networks during activation. In this work, we further investigated the allosteric properties of β2-AR, using Protein Contact Networks (PCNs). In this paper, we report an extensive statistical analysis of the topological and structural properties of β2-AR along its molecular dynamics trajectory to identify the activation pattern of this molecular system. The results show a distinct character to the activation that both helps to understand the allosteric switching previously identified and confirms the relevance of the network formalism to uncover relevant functional features of protein molecules.

Design and engineering of allosteric communications in proteins

Allostery in proteins plays an important role in regulating protein activities and influencing many biological processes such as gene expression, enzyme catalysis, and cell signaling. The process of allostery takes place when a signal detected at a site on a protein is transmitted via a mechanical pathway to a functional site and, thus, influences its activity. The pathway of allosteric communication consists of amino acids that form a network with covalent and non-covalent bonds. By mutating residues in this allosteric network, protein engineers have successfully established novel allosteric pathways to achieve desired properties in the target protein. In this review, we highlight the most recent and state-of-the-art techniques for allosteric communication engineering. We also discuss the challenges that need to be overcome and future directions for engineering protein allostery.

Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning

Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

Elucidation of Complex Dynamic Intermolecular Interactions in Membranes

Biomembranes composed of various proteins and lipids play important roles in cellular functions, such as signal transduction and substance transport. In addition, some bioactive peptides and pathogenic proteins target membrane proteins and lipids to exert their effects. Therefore, an understanding of dynamic and complex intermolecular interactions among these membrane constituents is needed to elucidate their mechanisms. This review summarizes the major research carried out in the author's laboratory on how lipids and their inhomogeneous distributions regulate the structures and functions of antimicrobial peptides and Alzheimer's amyloid β-protein. Also, how to detect transmembrane helix-helix and membrane protein-protein interactions and how they are modulated by lipids are discussed.

Modulating brain integrative actions as a new perspective on pharmacological approaches to neuropsychiatric diseases

A critical aspect of drug development in the therapy of neuropsychiatric diseases is the "Target Problem", that is, the selection of a proper target after not simply the etiopathological classification but rather the detection of the supposed structural and/or functional alterations in the brain networks. There are novel ways of approaching the development of drugs capable of overcoming or at least reducing the deficits without triggering deleterious side effects. For this purpose, a model of brain network organization is needed, and the main aspects of its integrative actions must also be established. Thus, to this aim we here propose an updated model of the brain as a hyper-network in which i) the penta-partite synapses are suggested as key nodes of the brain hyper-network and ii) interacting cell surface receptors appear as both decoders of signals arriving to the network and targets of central nervous system diseases. The integrative actions of the brain networks follow the "Russian Doll organization" including the micro (i.e., synaptic) and nano (i.e., molecular) levels. In this scenario, integrative actions result primarily from protein-protein interactions. Importantly, the macromolecular complexes arising from these interactions often have novel structural binding sites of allosteric nature. Taking G protein-coupled receptors (GPCRs) as potential targets, GPCRs heteromers offer a way to increase the selectivity of pharmacological treatments if proper allosteric drugs are designed. This assumption is founded on the possible selectivity of allosteric interventions on G protein-coupled receptors especially when organized as "Receptor Mosaics" at penta-partite synapse level.

The multi-specific human 17 beta-hydroxysteroid dehydrogenase type 7: Non-competitive inhibitors can target different catalyses to facilitate breast cancer treatment

Human 17β-hydroxysteroid dehydrogenase type 7 (17β-HSD7), a special multifunctional enzyme, activates the estrogen estrone while inactivating the potent androgen dihydrotestosterone. Thus, this enzyme has become an ideal target for hormone-dependent breast cancer treatment, as its inhibition leads to estradiol reduction and dihydrotestosterone restoration. However, a particular concern has arisen related to an additional role in cholesterol biosynthesis, as inhibition of the enzyme may lead to undesirable side effects. Our findings demonstrate that the available enzyme inhibitors are non-competitive. Among these, many such as INH81, are specific toward sex-hormone conversion, whereas others represented by 4-bromo-ethynylestradiol, are more specific for zymosterone reduction occurring during cholesterol biosynthesis. The binding of non-competitive inhibitors does not affect the substrate binding on the enzyme. This is the first demonstration of non-competitive inhibitors acting selectively on different catalyses, thereby facilitating inhibitor uses for breast cancer treatment. We aim to quickly communicate the novel results.

Informing NMR experiments with molecular dynamics simulations to characterize the dominant activated state of the KcsA ion channel

As the first potassium channel with an x-ray structure determined, and given its homology to eukaryotic channels, the pH-gated prokaryotic channel KcsA has been extensively studied. Nevertheless, questions related, in particular, to the allosteric coupling between its gates remain open. The many currently available x-ray crystallography structures appear to correspond to various stages of activation and inactivation, offering insights into the molecular basis of these mechanisms. Since these studies have required mutations, complexation with antibodies, and substitution of detergents in place of lipids, examining the channel under more native conditions is desirable. Solid-state nuclear magnetic resonance (SSNMR) can be used to study the wild-type protein under activating conditions (low pH), at room temperature, and in bacteriomimetic liposomes. In this work, we sought to structurally assign the activated state present in SSNMR experiments. We used a combination of molecular dynamics (MD) simulations, chemical shift prediction algorithms, and Bayesian inference techniques to determine which of the most plausible x-ray structures resolved to date best represents the activated state captured in SSNMR. We first identified specific nuclei with simulated NMR chemical shifts that differed significantly when comparing partially open vs fully open ensembles from MD simulations. The simulated NMR chemical shifts for those specific nuclei were then compared to experimental ones, revealing that the simulation of the partially open state was in good agreement with the SSNMR data. Nuclei that discriminate effectively between partially and fully open states belong to residues spread over the sequence and provide a molecular level description of the conformational change.