A General Mechanism for Signal Propagation in the Nicotinic Acetylcholine Receptor Family

Allosteric modulation by the fatty acid site in the glycosylated SARS-CoV-2 spike

The spike protein is essential to the SARS-CoV-2 virus life cycle, facilitating virus entry and mediating viral-host membrane fusion. The spike contains a fatty acid (FA) binding site between every two neighbouring receptor-binding domains. This site is coupled to key regions in the protein, but the impact of glycans on these allosteric effects has not been investigated. Using dynamical nonequilibrium molecular dynamics (D-NEMD) simulations, we explore the allosteric effects of the FA site in the fully glycosylated spike of the SARS-CoV-2 ancestral variant. Our results identify the allosteric networks connecting the FA site to functionally important regions in the protein, including the receptor-binding motif, an antigenic supersite in the N-terminal domain, the fusion peptide region, and another allosteric site known to bind heme and biliverdin. The networks identified here highlight the complexity of the allosteric modulation in this protein and reveal a striking and unexpected link between different allosteric sites. Comparison of the FA site connections from D-NEMD in the glycosylated and non-glycosylated spike revealed that glycans do not qualitatively change the internal allosteric pathways but can facilitate the transmission of the structural changes within and between subunits.

Exploring the Allosteric Response of Fascin to Its Inhibitor

Fascin is a major actin-binding protein (ABP) for stabilizing filopodia to support efficient adhesion and migration of cancer cells. Fascin is also highly expressed in metastatic tumors. Disrupting the actin-binding site (ABS) on fascin constitutes a critical approach to hindering tumor metastasis. The G2 series of small molecules was formulated with the specific purpose of obstructing the binding pocket of fascin. The determination of inhibitor-induced structural dynamics in fascin is crucial for a comprehensive of its biological functions and the strategic development of pharmacological interventions. In this study, we utilized both equilibrium and dynamical-nonequilibrium molecular dynamics (D-NEMD) to elucidate the molecular mechanisms responsible for transmitting structural changes when removing the G2 inhibitor, in both the wild type (WT) and its variants. Our findings indicate that when G2 is removed, structural dynamics in fascin originate from the G2 binding pocket of fascin and propagate signals through the conformational transformation that spans all four β-trefoil domains. Although different mutant variants demonstrated comparable conformational networks, they showed varying response times. However, the signaling pathways in mutants remained consistent in comparison to the WT fascin. This study provides valuable insights into the structural features and communication pathways of fascin and provides avenues for the development of targeted inhibitors with promising prospects in cancer therapy.

Pharmacology and molecular modeling studies of sulfoxaflor, flupyradifurone and neonicotinoids on the human neuronal α7 nicotinic acetylcholine receptor

We conducted electrophysiological and molecular docking studies using a heterologous expression system (Xenopus oocytes) to compare the effects of four neonicotinoids (acetamiprid, imidacloprid, clothianidin and thiamethoxam), one sulfoximine, (sulfoxaflor), and one butenolide (flupyradifurone), on human α7 neuronal nicotinic acetylcholine receptors (nAChRs). All neonicotinoids (except thiamethoxam), as well as the recently introduced nAChR competitive modulators, flupyradifurone and sulfoxaflor, appear to be weaker agonists than acetylcholine. Two mutations in loop C (E211N and E211P) and one mutation in loop D (Q79K), known to be involved in the binding properties of neonicotinoids were introduced to the α7 wild type. Interestingly, the acetylcholine and nicotine-evoked activation was not modified in human α7 mutated receptors, but the net charge was enhanced for clothianidin and imidacloprid, respectively. Flupyradifurone responses strongly increased under the Q79K mutation. The molecular docking investigations demonstrated that the orientations and interactions of the ligands considered were in accordance with those observed experimentally. Specifically, the charged fragments of acetylcholine and nicotine, used as reference ligands, and their neonicotinoid homologs were found to be surrounded by aromatic residues, with key interactions with Trp171 and Y210. Furthermore, the molecular docking investigations predicted the water-mediated interaction between the carbonyl oxygen of acetylcholine and the Nsp2 nitrogen of the pyridine ring for nicotine (as well as for the majority of the corresponding neonicotinoid fragments) and main chain NH of L141. The docking scores, extending over a significant range of 6 kcal/mol, showed that most neonicotinoids were poorly stabilized in the α7 nAChR compared to acetylcholine, except sulfoxaflor.

Integrating Molecular Dynamics and Machine Learning Algorithms to Predict the Functional Profile of Kinase Ligands

The modulation of protein function via designed small molecules is providing new opportunities in chemical biology and medicinal chemistry. While drugs have traditionally been developed to block enzymatic activities through active site occupation, a growing number of strategies now aim to control protein functions in an allosteric fashion, allowing for the tuning of a target's activation or deactivation via the modulation of the populations of conformational ensembles that underlie its function. In the context of the discovery of new active leads, it would be very useful to generate hypotheses for the functional impact of new ligands. Since the discovery and design of allosteric modulators (inhibitors/activators) is still a challenging and often serendipitous target, the development of a rapid and robust approach to predict the functional profile of a new ligand would significantly speed up candidate selection. Herein, we present different machine learning (ML) classifiers to distinguish between potential orthosteric and allosteric binders. Our approach integrates information on the chemical fingerprints of the ligands with descriptors that recapitulate ligand effects on protein functional motions. The latter are derived from molecular dynamics (MD) simulations of the target protein in complex with orthosteric or allosteric ligands. In this framework, we train and test different ML architectures, which are initially probed on the classification of orthosteric versus allosteric ligands for cyclin-dependent kinases (CDKs). The results demonstrate that different ML methods can successfully partition allosteric versus orthosteric effectors (although to different degrees). Next, we further test the models with FDA-approved CDK drugs, not included in the original dataset, as well as ligands that target other kinases, to test the range of applicability of these models outside of the domain on which they were developed. Overall, the results show that enriching the training dataset with chemical physics-based information on the protein-ligand dynamic cross-talk can significantly expand the reach and applicability of approaches for the prediction and classification of the mode of action of small molecules.

The Nicotinic Acetylcholine Receptor and Its Pentameric Homologs: Toward an Allosteric Mechanism of Signal Transduction at the Atomic Level

The nicotinic acetylcholine receptor has served, since its biochemical identification in the 1970s, as a model of an allosteric ligand-gated ion channel mediating signal transition at the synapse. In recent years, the application of X-ray crystallography and high-resolution cryo-electron microscopy, together with molecular dynamic simulations of nicotinic receptors and homologs, have opened a new era in the understanding of channel gating by the neurotransmitter. They reveal, at atomic resolution, the diversity and flexibility of the multiple ligand-binding sites, including recently discovered allosteric modulatory sites distinct from the neurotransmitter orthosteric site, and the conformational dynamics of the activation process as a molecular switch linking these multiple sites. The model emerging from these studies paves the way for a new pharmacology based, first, upon the occurrence of an original mode of indirect allosteric modulation, distinct from a steric competition for a single and rigid binding site, and second, the design of drugs that specifically interact with privileged conformations of the receptor such as agonists, antagonists, and desensitizers. Research on nicotinic receptors is still at the forefront of understanding the mode of action of drugs on the nervous system.

Advancing biomolecular simulation through exascale HPC, AI and quantum computing

Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations.

Signal Propagation in the ATPase Domain of Mycobacterium tuberculosis DNA Gyrase from Dynamical-Nonequilibrium Molecular Dynamics Simulations

DNA gyrases catalyze negative supercoiling of DNA, are essential for bacterial DNA replication, transcription, and recombination, and are important antibacterial targets in multiple pathogens, including Mycobacterium tuberculosis, which in 2021 caused >1.5 million deaths worldwide. DNA gyrase is a tetrameric (A2B2) protein formed from two subunit types: gyrase A (GyrA) carries the breakage-reunion active site, whereas gyrase B (GyrB) catalyzes ATP hydrolysis required for energy transduction and DNA translocation. The GyrB ATPase domains dimerize in the presence of ATP to trap the translocated DNA (T-DNA) segment as a first step in strand passage, for which hydrolysis of one of the two ATPs and release of the resulting inorganic phosphate is rate-limiting. Here, dynamical-nonequilibrium molecular dynamics (D-NEMD) simulations of the dimeric 43 kDa N-terminal fragment of M. tuberculosis GyrB show how events at the ATPase site (dissociation/hydrolysis of bound nucleotides) are propagated through communication pathways to other functionally important regions of the GyrB ATPase domain. Specifically, our simulations identify two distinct pathways that respectively connect the GyrB ATPase site to the corynebacteria-specific C-loop, thought to interact with GyrA prior to DNA capture, and to the C-terminus of the GyrB transduction domain, which in turn contacts the C-terminal GyrB topoisomerase-primase (TOPRIM) domain responsible for interactions with GyrA and the centrally bound G-segment DNA. The connection between the ATPase site and the C-loop of dimeric GyrB is consistent with the unusual properties of M. tuberculosis DNA gyrase relative to those from other bacterial species.

Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design

The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

SARS-CoV-2 spike variants differ in their allosteric responses to linoleic acid

The SARS-CoV-2 spike protein contains a functionally important fatty acid (FA) binding site, which is also found in some other coronaviruses, e.g. SARS-CoV and MERS-CoV. The occupancy of the FA site by linoleic acid (LA) reduces infectivity by 'locking' the spike in a less infectious conformation. Here, we use dynamical-nonequilibrium molecular dynamics (D-NEMD) simulations to compare the allosteric responses of spike variants to LA removal. D-NEMD simulations show that the FA site is coupled to other functional regions of the protein, e.g. the receptor-binding motif (RBM), N-terminal domain (NTD), furin cleavage site, and regions surrounding the fusion peptide. D-NEMD simulations also identify the allosteric networks connecting the FA site to these functional regions. The comparison between the wild-type spike and four variants (Alpha, Delta, Delta plus, and Omicron BA.1) shows that the variants differ significantly in their responses to LA removal. The allosteric connections to the FA site on Alpha are generally similar to those on the wild-type protein, with the exception of the RBM and the S71-R78 region, which show a weaker link to the FA site. In contrast, Omicron is the most different variant, exhibiting significant differences in the RBM, NTD, V622-L629, and furin cleavage site. These differences in the allosteric modulation may be of functional relevance, potentially affecting transmissibility and virulence. Experimental comparison of the effects of LA on SARS-CoV-2 variants, including emerging variants, is warranted.

Dynamical Nonequilibrium Molecular Dynamics Simulations Identify Allosteric Sites and Positions Associated with Drug Resistance in the SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease (Mpro) plays an essential role in the coronavirus lifecycle by catalyzing hydrolysis of the viral polyproteins at specific sites. Mpro is the target of drugs, such as nirmatrelvir, though resistant mutants have emerged that threaten drug efficacy. Despite its importance, questions remain on the mechanism of how Mpro binds its substrates. Here, we apply dynamical nonequilibrium molecular dynamics (D-NEMD) simulations to evaluate structural and dynamical responses of Mpro to the presence and absence of a substrate. The results highlight communication between the Mpro dimer subunits and identify networks, including some far from the active site, that link the active site with a known allosteric inhibition site, or which are associated with nirmatrelvir resistance. They imply that some mutations enable resistance by altering the allosteric behavior of Mpro. More generally, the results show the utility of the D-NEMD technique for identifying functionally relevant allosteric sites and networks including those relevant to resistance.

SARS-CoV-2 spike ectodomain targets α7 nicotinic acetylcholine receptors

Virus entry into animal cells is initiated by attachment to target macromolecules located on host cells. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) trimeric spike glycoprotein targets host angiotensin converting enzyme 2 to gain cellular access. The SARS-CoV-2 glycoprotein contains a neurotoxin-like region that has sequence similarities to the rabies virus and the HIV glycoproteins, as well as to snake neurotoxins, which interact with nicotinic acetylcholine receptor (nAChR) subtypes via this region. Using a peptide of the neurotoxin-like region of SARS-CoV-2 (SARS-CoV-2 glycoprotein peptide [SCoV2P]), we identified that this area moderately inhibits α3β2, α3β4, and α4β2 subtypes, while potentiating and inhibiting α7 nAChRs. These nAChR subtypes are found in target tissues including the nose, lung, central nervous system, and immune cells. Importantly, SCoV2P potentiates and inhibits ACh-induced α7 nAChR responses by an allosteric mechanism, with nicotine enhancing these effects. Live-cell confocal microscopy was used to confirm that SCoV2P interacts with α7 nAChRs in transfected neuronal-like N2a and human embryonic kidney 293 cells. The SARS-CoV-2 ectodomain functionally potentiates and inhibits the α7 subtype with nanomolar potency. Our functional findings identify that the α7 nAChR is a target for the SARS-CoV-2 glycoprotein, providing a new aspect to our understanding of SARS-CoV-2 and host cell interactions, in addition to disease pathogenesis.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Antagonistic Mechanism of α-Conotoxin BuIA toward the Human α3β2 Nicotinic Acetylcholine Receptor

Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand-gated ion channels that are abundantly expressed in the central and peripheral nervous systems, playing an important role in mediating neurotransmitter release and inter-synaptic signaling. Dysfunctional nAChRs are associated with neurological disorders, and studying the structure and function of nAChRs is essential for development of drugs or strategies for treatment of related diseases. α-Conotoxins are selective antagonists of the nAChR and are an important class of drug leads. So far, the antagonistic mechanism of α-conotoxins toward the nAChRs is still unclear. In this study, we built an α3β2 nAChR homology model and investigated its conformational transition mechanism upon binding with a highly potent inhibitor, α-conotoxin BuIA, through μs molecular dynamic simulations and site-directed mutagenesis studies. The results suggested that the α3β2 nAChR underwent global conformational transitions and was stabilized into a closed state with three hydrophobic gates present in the transmembrane domain by BuIA. Finally, the probable antagonistic mechanism of BuIA was proposed. Overall, the closed-state model of the α3β2 nAChR bound with BuIA is not only essential for understanding the antagonistic mechanism of α-conotoxins but also particularly valuable for development of therapeutic inhibitors in future.

The fatty acid site is coupled to functional motifs in the SARS-CoV-2 spike protein and modulates spike allosteric behaviour

The SARS-CoV-2 spike protein is the first contact point between the SARS-CoV-2 virus and host cells and mediates membrane fusion. Recently, a fatty acid binding site was identified in the spike (Toelzer et al. Science 2020). The presence of linoleic acid at this site modulates binding of the spike to the human ACE2 receptor, stabilizing a locked conformation of the protein. Here, dynamical-nonequilibrium molecular dynamics simulations reveal that this fatty acid site is coupled to functionally relevant regions of the spike, some of them far from the fatty acid binding pocket. Removal of a ligand from the fatty acid binding site significantly affects the dynamics of distant, functionally important regions of the spike, including the receptor-binding motif, furin cleavage site and fusion-peptide-adjacent regions. Simulations of the D614G mutant show differences in behaviour between these clinical variants of the spike: the D614G mutant shows a significantly different conformational response for some structural motifs relevant for binding and fusion. The simulations identify structural networks through which changes at the fatty acid binding site are transmitted within the protein. These communication networks significantly involve positions that are prone to mutation, indicating that observed genetic variation in the spike may alter its response to linoleate binding and associated allosteric communication.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

ACM REFERENCE FORMAT

Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics

Understanding allostery in enzymes and tools to identify it offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways in two prototypical class A β-lactamases, TEM-1 and KPC-2, which are important determinants of antibiotic resistance. The nonequilibrium simulations reveal pathways of communication operating over distances of 30 Å or more. Propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more of clinically relevant amino acid substitutions map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover, and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences.

A potential interaction between the SARS-CoV-2 spike protein and nicotinic acetylcholine receptors

Changeux et al. (Changeux et al. C. R. Biol. 343:33-39.) recently suggested that the SARS-CoV-2 spike protein may interact with nicotinic acetylcholine receptors (nAChRs) and that such interactions may be involved in pathology and infectivity. This hypothesis is based on the fact that the SARS-CoV-2 spike protein contains a sequence motif similar to known nAChR antagonists. Here, we use molecular simulations of validated atomically detailed structures of nAChRs and of the spike to investigate the possible binding of the Y674-R685 region of the spike to nAChRs. We examine the binding of the Y674-R685 loop to three nAChRs, namely the human α4β2 and α7 subtypes and the muscle-like αβγδ receptor from Tetronarce californica. Our results predict that Y674-R685 has affinity for nAChRs. The region of the spike responsible for binding contains a PRRA motif, a four-residue insertion not found in other SARS-like coronaviruses. The conformational behavior of the bound Y674-R685 is highly dependent on the receptor subtype; it adopts extended conformations in the α4β2 and α7 complexes but is more compact when bound to the muscle-like receptor. In the α4β2 and αβγδ complexes, the interaction of Y674-R685 with the receptors forces the loop C region to adopt an open conformation, similar to other known nAChR antagonists. In contrast, in the α7 complex, Y674-R685 penetrates deeply into the binding pocket in which it forms interactions with the residues lining the aromatic box, namely with TrpB, TyrC1, and TyrC2. Estimates of binding energy suggest that Y674-R685 forms stable complexes with all three nAChR subtypes. Analyses of simulations of the glycosylated spike show that the Y674-R685 region is accessible for binding. We suggest a potential binding orientation of the spike protein with nAChRs, in which they are in a nonparallel arrangement to one another.

Molecular Simulations of Hydrophobic Gating of Pentameric Ligand Gated Ion Channels: Insights into Water and Ions

Ion channels are proteins which form gated nanopores in biological membranes. Many channels exhibit hydrophobic gating, whereby functional closure of a pore occurs by local dewetting. The pentameric ligand gated ion channels (pLGICs) provide a biologically important example of hydrophobic gating. Molecular simulation studies comparing additive vs polarizable models indicate predictions of hydrophobic gating are robust to the model employed. However, polarizable models suggest favorable interactions of hydrophobic pore-lining regions with chloride ions, of relevance to both synthetic carriers and channel proteins. Electrowetting of a closed pLGIC hydrophobic gate requires too high a voltage to occur physiologically but may inform designs for switchable nanopores. Global analysis of ∼200 channels yields a simple heuristic for structure-based prediction of (closed) hydrophobic gates. Simulation-based analysis is shown to provide an aid to interpretation of functional states of new channel structures. These studies indicate the importance of understanding the behavior of water and ions within the nanoconfined environment presented by ion channels.

Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems

ABSTRACT

A dynamical approach to nonequilibrium molecular dynamics (D-NEMD), proposed in the 1970s by Ciccotti et al., is undergoing a renaissance and is having increasing impact in the study of biological macromolecules. This D-NEMD approach, combining MD simulations in stationary (in particular, equilibrium) and nonequilibrium conditions, allows for the determination of the time-dependent structural response of a system using the Kubo-Onsager relation. Besides providing a detailed picture of the system's dynamic structural response to an external perturbation, this approach also has the advantage that the statistical significance of the response can be assessed. The D-NEMD approach has been used recently to identify a general mechanism of inter-domain signal propagation in nicotinic acetylcholine receptors, and allosteric effects in β -lactamase enzymes, for example. It complements equilibrium MD and is a very promising approach to identifying and analysing allosteric effects. Here, we review the D-NEMD approach and its application to biomolecular systems, including transporters, receptors, and enzymes.

Simulations support the interaction of the SARS-CoV-2 spike protein with nicotinic acetylcholine receptors

Changeux et al. recently suggested that the SARS-CoV-2 spike (S) protein may interact with nicotinic acetylcholine receptors (nAChRs). Such interactions may be involved in pathology and infectivity. Here, we use molecular simulations of validated atomically detailed structures of nAChRs, and of the S protein, to investigate this 'nicotinic hypothesis'. We examine the binding of the Y674-R685 loop of the S protein to three nAChRs, namely the human α4β2 and α7 subtypes and the muscle-like αβγδ receptor from Tetronarce californica. Our results indicate that Y674-R685 has affinity for nAChRs and the region responsible for binding contains the PRRA motif, a four-residue insertion not found in other SARS-like coronaviruses. In particular, R682 has a key role in the stabilisation of the complexes as it forms interactions with loops A, B and C in the receptor's binding pocket. The conformational behaviour of the bound Y674-R685 region is highly dependent on the receptor subtype, adopting extended conformations in the α4β2 and α7 complexes and more compact ones when bound to the muscle-like receptor. In the α4β2 and αβγδ complexes, the interaction of Y674-R685 with the receptors forces the loop C region to adopt an open conformation similar to other known nAChR antagonists. In contrast, in the α7 complex, Y674-R685 penetrates deeply into the binding pocket where it forms interactions with the residues lining the aromatic box, namely with TrpB, TyrC1 and TyrC2. Estimates of binding energy suggest that Y674-R685 forms stable complexes with all three nAChR subtypes. Analyses of the simulations of the full-length S protein show that the Y674-R685 region is accessible for binding, and suggest a potential binding orientation of the S protein with nAChRs.

Induced Polarization in Molecular Dynamics Simulations of the 5-HT3 Receptor Channel

Ion channel proteins form water-filled nanoscale pores within lipid bilayers, and their properties are dependent on the complex behavior of water in a nanoconfined environment. Using a simplified model of the pore of the 5-HT3 receptor (5HT3R) which restrains the backbone structure to that of the parent channel protein from which it is derived, we compare additive with polarizable models in describing the behavior of water in nanopores. Molecular dynamics simulations were performed with four conformations of the channel: two closed state structures, an intermediate state, and an open state, each embedded in a phosphatidylcholine bilayer. Water density profiles revealed that for all water models, the closed and intermediate states exhibited strong dewetting within the central hydrophobic gate region of the pore. However, the open state conformation exhibited varying degrees of hydration, ranging from partial wetting for the TIP4P/2005 water model to complete wetting for the polarizable AMOEBA14 model. Water dipole moments calculated using polarizable force fields also revealed that water molecules remaining within dewetted sections of the pore resemble gas phase water. Free energy profiles for Na+ and for Cl- ions within the open state pore revealed more rugged energy landscapes using polarizable force fields, and the hydration number profiles of these ions were also sensitive to induced polarization resulting in a substantive reduction of the number of waters within the first hydration shell of Cl- while it permeates the pore. These results demonstrate that induced polarization can influence the complex behavior of water and ions within nanoscale pores and provides important new insights into their chemical properties.