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

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.

Protein dynamics underlying allosteric regulation

Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, and conformational changes. An increasing level of experimental resolution and control over allosteric mechanisms promises a deeper understanding of the molecular basis for life and powerful new therapeutics. In this review, we survey the literature for an up-to-date biological and theoretical understanding of protein allostery. By delineating five ways in which the energy landscape or the kinetics of a system may change to give rise to allostery, we aim to help the reader grasp its physical origins. To illustrate this framework, we examine three systems that display these forms of allostery: allosteric inhibitors of beta-lactamases, thermosensation of TRP channels, and the role of kinetic allostery in the function of kinases. Finally, we summarize the growing power of computational tools available to investigate the different forms of allostery presented in this review.

Oliveira AS, Mulholland AJ, Serapian SA, Colombo G. Decrypting Allostery in Membrane-Bound K-Ras4B Using Complementary In Silico Approaches Based on Unbiased Molecular Dynamics Simulations

Protein functions are dynamically regulated by allostery, which enables conformational communication even between faraway residues, and expresses itself in many forms, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are often unintuitively reshaped whenever biochemical perturbations arise (e.g., mutations). To accurately model allostery, unbiased molecular dynamics (MD) simulations require integration with a reliable method able to, e.g., detect incipient allosteric changes or likely perturbation pathways; this is because allostery can operate at longer time scales than those accessible by plain MD. Such methods are typically applied singularly, but we here argue their joint application─as a "multilingual" approach─could work significantly better. We successfully prove this through unbiased MD simulations (∼100 μs) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, from which we decrypt allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess perturbations upon, respectively, either superheating or hydrolyzing the GTP that oncogenically activates K-Ras4B. Chosen "languages" work synergistically, providing an articulate, mutually coherent, experimentally consistent picture of K-Ras4B allostery, whereby distinct traits emerge at equilibrium and upon GTP cleavage. At equilibrium, combined evidence confirms prominent allosteric communication from the membrane-embedded hypervariable region, through a hub comprising helix α5 and sheet β5, and up to the active site, encompassing allosteric "switches" I and II (marginally), and two proposed pockets. Upon GTP cleavage, allosteric perturbations mostly accumulate on the switches and documented interfaces.

Fluctuation Relations to Calculate Protein Redox Potentials from Molecular Dynamics Simulations

The tunable design of protein redox potentials promises to open a range of applications in biotechnology and catalysis. Here, we introduce a method to calculate redox potential changes by combining fluctuation relations with molecular dynamics simulations. It involves the simulation of reduced and oxidized states, followed by the instantaneous conversion between them. Energy differences introduced by the perturbations are obtained using the Kubo-Onsager approach. Using a detailed fluctuation relation coupled with Bayesian inference, these are postprocessed into estimates for the redox potentials in an efficient manner. This new method, denoted MD + CB, is tested on a de novo four-helix bundle heme protein (the m4D2 "maquette") and five designed mutants, including some mutants characterized experimentally in this work. The MD + CB approach is found to perform reliably, giving redox potential shifts with reasonably good correlation (0.85) to the experimental values for the mutants. The MD + CB approach also compares well with redox potential shift predictions using a continuum electrostatic method. The estimation method employed within the MD + CB approach is straightforwardly transferable to standard equilibrium MD simulations and holds promise for redox protein engineering and design applications.

Molecular mechanisms of chaperone-directed protein folding: Insights from atomistic simulations

Molecular chaperones, a family of proteins of which Hsp90 and Hsp70 are integral members, form an essential machinery to maintain healthy proteomes by controlling the folding and activation of a plethora of substrate client proteins. This is achieved through cycles in which Hsp90 and Hsp70, regulated by task-specific co-chaperones, process ATP and become part of a complex network that undergoes extensive compositional and conformational variations. Despite impressive advances in structural knowledge, the mechanisms that regulate the dynamics of functional assemblies, their response to nucleotides, and their relevance for client remodeling are still elusive. Here, we focus on the glucocorticoid receptor (GR):Hsp90:Hsp70:co-chaperone Hop client-loading and the GR:Hsp90:co-chaperone p23 client-maturation complexes, key assemblies in the folding cycle of glucocorticoid receptor (GR), a client strictly dependent upon Hsp90/Hsp70 for activity. Using a combination of molecular dynamics simulation approaches, we unveil with unprecedented detail the mechanisms that underpin function in these chaperone machineries. Specifically, we dissect the processes by which the nucleotide-encoded message is relayed to the client and how the distinct partners of the assemblies cooperate to (pre)organize partially folded GR during Loading and Maturation. We show how different ligand states determine distinct dynamic profiles for the functional interfaces defining the interactions in the complexes and modulate their overall flexibility to facilitate progress along the chaperone cycle. Finally, we also show that the GR regions engaged by the chaperone machinery display peculiar energetic signatures in the folded state, which enhance the probability of partial unfolding fluctuations. From these results, we propose a model where a dynamic cross-talk emerges between the chaperone dynamics states and remodeling of client-interacting regions. This factor, coupled to the highly dynamic nature of the assemblies and the conformational heterogeneity of their interactions, provides the basis for regulating the functions of distinct assemblies during the chaperoning cycle.

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.

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.

Structural insights in cell-type specific evolution of intra-host diversity by SARS-CoV-2

As the global burden of SARS-CoV-2 infections escalates, so does the evolution of viral variants with increased transmissibility and pathology. In addition to this entrenched diversity, RNA viruses can also display genetic diversity within single infected hosts with co-existing viral variants evolving differently in distinct cell types. The BriSΔ variant, originally identified as a viral subpopulation from SARS-CoV-2 isolate hCoV-19/England/02/2020, comprises in the spike an eight amino-acid deletion encompassing a furin recognition motif and S1/S2 cleavage site. We elucidate the structure, function and molecular dynamics of this spike providing mechanistic insight into how the deletion correlates to viral cell tropism, ACE2 receptor binding and infectivity of this SARS-CoV-2 variant. Our results reveal long-range allosteric communication between functional domains that differ in the wild-type and the deletion variant and support a view of SARS-CoV-2 probing multiple evolutionary trajectories in distinct cell types within the same infected host.

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.