Folding and binding cascades: dynamic landscapes and population shifts

Chemical Scaffolds for the Clinical Development of Mutant-Selective and Reversible Fourth-Generation EGFR-TKIs in NSCLC

In nonsmall cell lung cancer (NSCLC), as well as in other tumors, the targeted therapy is mainly represented by tyrosine kinase inhibitors (TKIs), small molecules able to target oncogenic driver alterations affecting the gene encoding the epidermal growth factor receptor (EGFR). Up to now, several different TKIs have been developed. However, cancer cells showed an incredible adaptive tumor response to the inhibition of the sequentially mutated EGFR (EGFRM+), triggering the need to explore novel pharmacochemical strategies. This Review summarizes the recent efforts in the development of new reversible next-generation EGFR TKIs to fight the resistance against T790M and C797S mutations. Specifically, after giving an overview of the role of the EGFR's signaling pathways in cancer progression, we are going to discuss the most relevant approved drugs and drug candidates in terms of chemical structure, binding modalities, and their potency and selectivity against the mutated EGFR over the wild-type form. This could provide important guidelines and rationale for the discovery and iterative development of new drugs.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

Food Antioxidants and Their Interaction with Human Proteins

Common to all biological systems and living organisms are molecular interactions, which may lead to specific physiological events. Most often, a cascade of events occurs, establishing an equilibrium between possibly competing and/or synergistic processes. Biochemical pathways that sustain life depend on multiple intrinsic and extrinsic factors contributing to aging and/or diseases. This article deals with food antioxidants and human proteins from the circulation, their interaction, their effect on the structure, properties, and function of antioxidant-bound proteins, and the possible impact of complex formation on antioxidants. An overview of studies examining interactions between individual antioxidant compounds and major blood proteins is presented with findings. Investigating antioxidant/protein interactions at the level of the human organism and determining antioxidant distribution between proteins and involvement in the particular physiological role is a very complex and challenging task. However, by knowing the role of a particular protein in certain pathology or aging, and the effect exerted by a particular antioxidant bound to it, it is possible to recommend specific food intake or resistance to it to improve the condition or slow down the process.

MCL-1 promiscuity and the structural resilience of its binding partners

The allosteric protein MCL-1 and its natural inhibitors, the BH3-only proteins PUMA, BIM, and NOXA regulate apoptosis by interacting promiscuously within an entangled binding network. Little is known about the transient processes and dynamic conformational fluctuations that are the basis for the formation and stability of the MCL-1/BH3-only complex. In this study, we designed photoswitchable versions of MCL-1/PUMA and MCL-1/NOXA, and investigated the protein response after an ultrafast photo-perturbation with transient infrared spectroscopy. We observed partial α-helical unfolding in all cases, albeit on strongly varying timescales (1.6 ns for PUMA, 9.7 ns for the previously studied BIM, and 85 ns for NOXA). These differences are interpreted as a BH3-only-specific "structural resilience" to defy the perturbation while remaining in MCL-1's binding pocket. Thus, the presented insights could help to better understand the differences between PUMA, BIM, and NOXA, the promiscuity of MCL-1, in general, and the role of the proteins in the apoptotic network.

The origin of genetic and metabolic systems: Evolutionary structuralinsights

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Disorder in the Human Skp1 Structure is the Key to its Adaptability to Bind Many Different Proteins in the SCF Complex Assembly

Skp1(S-phase kinase-associated protein 1 - Homo sapiens) is an adapter protein of the SCF(Skp1-Cullin1-Fbox) complex, which links the constant components (Cul1-RBX) and the variable receptor (F-box proteins) in Ubiquitin E3 ligase. It is intriguing how Skp1 can recognise and bind to a variety of structurally different F-box proteins. For practical reasons, previous efforts have used truncated Skp1, and thus it has not been possible to track the crucial aspects of the substrate recognition process. In this background, we report the solution structure of the full-length Skp1 protein determined by NMR spectroscopy for the first time and investigate the sequence-dependent dynamics in the protein. The solution structure reveals that Skp1 has an architecture: β1-β2-H1-H2-L1-H3-L2-H4-H5-H6-H7(partially formed) and a long tail-like disordered C-terminus. Structural analysis using DALI (Distance Matrix Alignment) reveals conserved domain structure across species for Skp1. Backbone dynamics investigated using NMR relaxation suggest substantial variation in the motional timescales along the length of the protein. The loops and the C-terminal residues are highly flexible, and the (R2/R1) data suggests μs-ms timescale motions in the helices as well. Further, the dependence of amide proton chemical shift on temperature and curved profiles of their residuals indicate that the residues undergo transitions between native state and excited state. The curved profiles for several residues across the length of the protein suggest that there are native-like low-lying excited states, particularly for several C-terminal residues. Our results provide a rationale for how the protein can adapt itself, bind, and get functionally associated with other proteins in the SCF complex by utilising its flexibility and conformational sub-states.

Exploiting the Innate Plasticity of the Programmed Cell Death-1 (PD1) Receptor to Design Pembrolizumab H3 Loop Mimics

Checkpoint blockade of the immunoreceptor programmed cell death-1 (PD1) with its ligand-1 (PDL1) by monoclonal antibodies such as pembrolizumab provided compelling clinical results in various cancer types, yet the molecular mechanism by which this drug blocks the PD1/PDL1 interface remains unclear. To address this question, we examined the conformational motion of PD1 associated with the binding of pembrolizumab. Our results revealed that the innate plasticity of both C'D and FG loops is crucial to form a deep binding groove (371 Å3 ) across several distant epitopes of PD1. This analysis ultimately provided a rational-design to create pembrolizumab H3 loop mimics [RDYRFDMGFD] into β-hairpin scaffolds. As a result, a 20-residue long β-hairpin peptide 1 e was identified as a first-in-class potent PD1-inhibitor (EC50 of 0.29 μM; Ki of 41 nM).

Landscape-Based View on the Stepping Movement of Myosin VI

Myosin VI dimer walks toward the minus end of the actin filament with a large and variable step size of 25–36 nm. Two competing models have been put forward to explain this large step size. The Spudich model assumes that the myosin VI dimer associates at a distal tail near the cargo-binding domain, which makes two full-length single α-helix (SAH) domains serve as long legs. In contrast, the Houdusse–Sweeney model assumes that the association occurs in the middle (between residues 913 and 940) of the SAH domain and that the three-helix bundles unfold to ensure the large step size. Their consistency with the observation of stepping motion with a large and variable step size has not been examined in detail. To compare the two proposed models of myosin VI, we computationally characterized the free energy landscape experienced by the leading head during the stepping movement along the actin filament using the elastic network model of two heads and an implicit model of the SAH domains. Our results showed that the Spudich model is more consistent with the 25–36 nm step size than the Houdusse–Sweeney model. The unfolding of the three-helix bundles gives rise to the free energy bias toward a shorter distance between two heads. Besides, the stiffness of the SAH domain is a key factor for giving strong energetic bias toward the longer distance of stepping. Free energy analysis of the stepping motion complements the visual inspection of static structures and enables a deeper understanding of underlying mechanisms of molecular motors.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

From Distinct to Differential Conformational Dynamics to Map Allosteric Communication Pathways in Proteins

Initiation of biological processes involving protein-ligand binding, transient protein-protein interactions, or amino acid modifications alters the conformational dynamics of proteins. Accompanying these biological processes are ensuing coupled atomic level conformational changes within the proteins. These conformational changes collectively connect multiple amino acid residues at distal allosteric, binding, and/or active sites. Local changes due to, for example, binding of a regulatory ligand at an allosteric site initiate the allosteric regulation. The allosteric signal propagates throughout the protein structure, causing changes at distal sites, activating, deactivating, or modifying the function of the protein. Hence, dynamical responses within protein structures to stimuli contain critical information on protein function. In this Perspective, we examine the description of allosteric regulation from protein dynamical responses and associated alternative and emerging computational approaches to map allosteric communication pathways between distal sites in proteins at the atomic level.

Investigating the Conformational Dynamics of a Y-Family DNA Polymerase during Its Folding and Binding to DNA and a Nucleotide

During DNA polymerization, the Y-family DNA polymerases are capable of bypassing various DNA damage, which can stall the replication fork progression. It has been well acknowledged that the structures of the Y-family DNA polymerases have been naturally evolved to undertake this vital task. However, the mechanisms of how these proteins utilize their unique structural and conformational dynamical features to perform the translesion DNA synthesis are less understood. Here, we developed structure-based models to study the precatalytic DNA polymerization process, including DNA and nucleotide binding to DPO4, a paradigmatic Y-family polymerase from Sulfolobus solfataricus. We studied the interplay between the folding and the conformational dynamics of DPO4 and found that DPO4 undergoes first unraveling (unfolding) and then folding for accomplishing the functional "open-to-closed" conformational transition. DNA binding dynamically modulates the conformational equilibrium in DPO4 during the stepwise binding through different types of interactions, leading to different conformational distributions of DPO4 at different DNA binding stages. We observed that nucleotide binding induces modulation of a few contacts surrounding the active site of the DPO4-DNA complex associated with a high free energy barrier. Our simulation results resonate with the experimental evidence that the conformational change at the active site led by nucleotide is the rate-limiting step of nucleotide incorporation. In combination with localized frustration analyses, we underlined the importance of DPO4 conformational dynamics and fluctuations in facilitating DNA and nucleotide binding. Our findings offer mechanistic insights into the processes of DPO4 conformational dynamics associated with the substrate binding and contribute to the understanding of the "structure-dynamics-function" relationship in the Y-family DNA polymerases.

Structural-Energetic Basis for Coupling between Equilibrium Fluctuations and Phosphorylation in a Protein Native Ensemble

The functioning of proteins is intimately tied to their fluctuations in the native ensemble. The structural-energetic features that determine fluctuation amplitudes and hence the shape of the underlying landscape, which in turn determine the magnitude of the functional output, are often confounded by multiple variables. Here, we employ the FF1 domain from human p190A RhoGAP protein as a model system to uncover the molecular basis for phosphorylation of a buried tyrosine, which is crucial to the transcriptional activity associated with transcription factor TFII-I. Combining spectroscopy, calorimetry, statistical-mechanical modeling, molecular simulations, and in vitro phosphorylation assays, we show that the FF1 domain samples a diverse array of conformations in its native ensemble, some of which are phosphorylation-competent. Upon eliminating unfavorable charge-charge interactions through a single charge-reversal (K53E) or charge-neutralizing (K53Q) mutation, we observe proportionately lower phosphorylation extents due to the altered structural coupling, damped equilibrium fluctuations, and a more compact native ensemble. We thus establish a conformational selection mechanism for phosphorylation in the FF1 domain with K53 acting as a "gatekeeper", modulating the solvent exposure of the buried tyrosine. Our work demonstrates the role of unfavorable charge-charge interactions in governing functional events through the modulation of native ensemble characteristics, a feature that could be prevalent in ordered protein domains.

Mechanism of RNA recognition by a Musashi RNA-binding protein

The Musashi RNA-binding proteins (RBPs) regulate translation of target mRNAs and maintenance of cell stemness and tumorigenesis. Musashi-1 (MSI1), long considered as an intestinal and neural stem cell marker, has been more recently found to be over expressed in many cancers. It has served as an important drug target for treating acute myeloid leukemia and solid tumors such as ovarian, colorectal and bladder cancer. One of the reported binding targets of MSI1 is Numb, a negative regulator of the Notch signaling. However, the dynamic mechanism of Numb RNA binding to MSI1 remains unknown, largely hindering effective drug design targeting this critical interaction. Here, we have performed extensive all-atom microsecond-timescale simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method, which successfully captured multiple times of spontaneous and highly accurate binding of the Numb RNA from bulk solvent to the MSI1 protein target site. GaMD simulations revealed that Numb RNA binding to MSI1 involved largely induced fit in both the RNA and protein. The simulations also identified important low-energy intermediate conformational states during RNA binding, in which Numb interacted mainly with the β2-β3 loop and C terminus of MSI1. The mechanistic understanding of RNA binding obtained from our GaMD simulations is expected to facilitate rational structure-based drug design targeting MSI1 and other RBPs.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Monitoring the binding and insertion of a single transmembrane protein by an insertase

Cells employ highly conserved families of insertases and translocases to insert and fold proteins into membranes. How insertases insert and fold membrane proteins is not fully known. To investigate how the bacterial insertase YidC facilitates this process, we here combine single-molecule force spectroscopy and fluorescence spectroscopy approaches, and molecular dynamics simulations. We observe that within 2 ms, the cytoplasmic α-helical hairpin of YidC binds the polypeptide of the membrane protein Pf3 at high conformational variability and kinetic stability. Within 52 ms, YidC strengthens its binding to the substrate and uses the cytoplasmic α-helical hairpin domain and hydrophilic groove to transfer Pf3 to the membrane-inserted, folded state. In this inserted state, Pf3 exposes low conformational variability such as typical for transmembrane α-helical proteins. The presence of YidC homologues in all domains of life gives our mechanistic insight into insertase-mediated membrane protein binding and insertion general relevance for membrane protein biogenesis.

Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development

After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG₁κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

Tuning Protein Dynamics to Sense Rapid Endoplasmic-Reticulum Calcium Dynamics

Multi-scale calcium (Ca²⁺ ) dynamics, exhibiting wide-ranging temporal kinetics, constitutes a ubiquitous mode of signal transduction. We report a novel endoplasmic-reticulum (ER)-targeted Ca²⁺ indicator, R-CatchER, which showed superior kinetics in vitro (k_off ≥2×10³  s^-1 , k_on ≥7×10⁶  M^-1  s^-1 ) and in multiple cell types. R-CatchER captured spatiotemporal ER Ca²⁺ dynamics in neurons and hotspots at dendritic branchpoints, enabled the first report of ER Ca²⁺ oscillations mediated by calcium sensing receptors (CaSRs), and revealed ER Ca²⁺ -based functional cooperativity of CaSR. We elucidate the mechanism of R-CatchER and propose a principle to rationally design genetically encoded Ca²⁺ indicators with a single Ca²⁺ -binding site and fast kinetics by tuning rapid fluorescent-protein dynamics and the electrostatic potential around the chromophore. The design principle is supported by the development of G-CatchER2, an upgrade of our previous (G-)CatchER with improved dynamic range. Our work may facilitate protein design, visualizing Ca²⁺ dynamics, and drug discovery.

Contemporary biomedical engineering perspective on volitional evolution for human radiotolerance enhancement beyond low-earth orbit

A primary objective of the National Aeronautics and Space Administration (NASA) is expansion of humankind's presence outside low-Earth orbit, culminating in permanent interplanetary travel and habitation. Having no inherent means of physiological detection or protection against ionizing radiation, humans incur capricious risk when journeying beyond low-Earth orbit for long periods. NASA has made large investments to analyze pathologies from space radiation exposure, emphasizing the importance of characterizing radiation's physiological effects. Because natural evolution would require many generations to confer resistance against space radiation, immediately pragmatic approaches should be considered. Volitional evolution, defined as humans steering their own heredity, may inevitably retrofit the genome to mitigate resultant pathologies from space radiation exposure. Recently, uniquely radioprotective genes have been identified, conferring local or systemic radiotolerance when overexpressed in vitro and in vivo. Aiding in this process, the CRISPR/Cas9 technique is an inexpensive and reproducible instrument capable of making limited additions and deletions to the genome. Although cohorts can be identified and engineered to protect against radiation, alternative and supplemental strategies should be seriously considered. Advanced propulsion and mild synthetic torpor are perhaps the most likely to be integrated. Interfacing artificial intelligence with genetic engineering using predefined boundary conditions may enable the computational modeling of otherwise overly complex biological networks. The ethical context and boundaries of introducing genetically pioneered humans are considered.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Ras isoform-specific expression, chromatin accessibility, and signaling

The anchorage of Ras isoforms in the membrane and their nanocluster formations have been studied extensively, including their detailed interactions, sizes, preferred membrane environments, chemistry, and geometry. However, the staggering challenge of their epigenetics and chromatin accessibility in distinct cell states and types, which we propose is a major factor determining their specific expression, still awaits unraveling. Ras isoforms are distinguished by their C-terminal hypervariable region (HVR) which acts in intracellular transport, regulation, and membrane anchorage. Here, we review some isoform-specific activities at the plasma membrane from a structural dynamic standpoint. Inspired by physics and chemistry, we recognize that understanding functional specificity requires insight into how biomolecules can organize themselves in different cellular environments. Within this framework, we suggest that isoform-specific expression may largely be controlled by the chromatin density and physical compaction, which allow (or curb) access to "chromatinized DNA." Genes are preferentially expressed in tissues: proteins expressed in pancreatic cells may not be equally expressed in lung cells. It is the rule-not an exception, and it can be at least partly understood in terms of chromatin organization and accessibility state. Genes are expressed when they can be sufficiently exposed to the transcription machinery, and they are less so when they are persistently buried in dense chromatin. Notably, chromatin accessibility can similarly determine expression of drug resistance genes.

Dynamic posttranslational modifications of cytoskeletal proteins unveil hot spots under nitroxidative stress

The cytoskeleton is a supramolecular structure consisting of interacting protein networks that support cell dynamics in essential processes such as migration and division, as well as in responses to stress. Fast cytoskeletal remodeling is achieved with the participation of regulatory proteins and posttranslational modifications (PTMs). Redox-related PTMs are emerging as critical players in cytoskeletal regulation. Here we used a cellular model of mild nitroxidative stress in which a peroxynitrite donor induced transient changes in the organization of three key cytoskeletal proteins, i.e., vimentin, actin and tubulin. Nitroxidative stress-induced reconfiguration of intermediate filaments, microtubules and actin structures were further correlated with their PTM profiles and dynamics of the PTM landscape. Using high-resolution mass spectrometry, 62 different PTMs were identified and relatively quantified in vimentin, actin and tubulin, including 12 enzymatic, 13 oxidative and 2 nitric oxide-derived modifications as well as 35 modifications by carbonylated lipid peroxidation products, thus evidencing the occurrence of a chain reaction with formation of numerous reactive species and activation of multiple signaling pathways. Our results unveil the presence of certain modifications under basal conditions and their modulation in response to stress in a target-, residue- and reactive species-dependent manner. Thus, some modifications accumulated during the experiment whereas others varied transiently. Moreover, we identified protein PTM "hot spots", such as the single cysteine residue of vimentin, which was detected in seven modified forms, thus, supporting its role in PTM crosstalk and redox sensing. Finally, identification of novel PTMs in these proteins paves the way for unveiling new cytoskeleton regulatory mechanisms.

A Framework for Investigating Rules of Life by Establishing Zones of Influence

The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (Smocovitis, 1992) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (Leroi, 2014) island biogeography, and (Gropp, 2016) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model

To initiate transcription, the holoenzyme (RNA polymerase [RNAP] in complex with σ factor) loads the promoter DNA via the flexible loading gate created by the clamp and β-lobe, yet their roles in DNA loading have not been characterized. We used a quasi-Markov State Model (qMSM) built from extensive molecular dynamics simulations to elucidate the dynamics of Thermus aquaticus holoenzyme's gate opening. We showed that during gate opening, β-lobe oscillates four orders of magnitude faster than the clamp, whose opening depends on the Switch 2's structure. Myxopyronin, an antibiotic that binds to Switch 2, was shown to undergo a conformational selection mechanism to inhibit clamp opening. Importantly, we reveal a critical but undiscovered role of β-lobe, whose opening is sufficient for DNA loading even when the clamp is partially closed. These findings open the opportunity for the development of antibiotics targeting β-lobe of RNAP. Finally, we have shown that our qMSMs, which encode non-Markovian dynamics based on the generalized master equation formalism, hold great potential to be widely applied to study biomolecular dynamics.

A nexus of intrinsic dynamics underlies translocase priming

The cytoplasmic ATPase SecA and the membrane-embedded SecYEG channel assemble to form the Sec translocase. How this interaction primes and catalytically activates the translocase remains unclear. We show that priming exploits a nexus of intrinsic dynamics in SecA. Using atomistic simulations, smFRET, and HDX-MS, we reveal multiple dynamic islands that cross-talk with domain and quaternary motions. These dynamic elements are functionally important and conserved. Central to the nexus is a slender stem through which rotation of the preprotein clamp of SecA is biased by ATPase domain motions between open and closed clamping states. An H-bonded framework covering most of SecA enables multi-tier dynamics and conformational alterations with minimal energy input. As a result, cognate ligands select preexisting conformations and alter local dynamics to regulate catalytic activity and clamp motions. These events prime the translocase for high-affinity reception of non-folded preprotein clients. Dynamics nexuses are likely universal and essential in multi-liganded proteins.

Diverse Native Ensembles Dictate the Differential Functional Responses of Nuclear Receptor Ligand-Binding Domains

Native states of folded proteins are characterized by a large ensemble of conformations whose relative populations and interconversion dynamics determine the functional output. This is more apparent in transcription factors that have evolved to be inherently sensitive to small perturbations, thus fine-tuning gene expression. To explore the extent to which such functional features are imprinted on the folding landscape of transcription factor ligand-binding domains (LBDs), we characterize paralogous LBDs of the nuclear receptor (NR) family employing an energetically detailed and ensemble-based Ising-like statistical mechanical model. We find that the native ensembles of the LBDs from glucocorticoid receptor, PPAγ, and thyroid hormone receptor display a remarkable diversity in the width of the native wells, the number and nature of partially structured states, and hence the degree of conformational order. Monte Carlo simulations employing the full state representation of the ensemble highlight that many of the functional conformations coexist in equilibrium, whose relative populations are sensitive to both temperature and the strength of ligand binding. Allosteric modulation of the degree of structure at a coregulator binding site on ligand binding is shown to arise via a redistribution of populations in the native ensembles of glucocorticoid and PPAγ LBDs. Our results illustrate how functional requirements can drive the evolution of conformationally diverse native ensembles in paralogs.

Predicting the aptamer SYL3C-EpCAM complex's structure with the Martini-based simulation protocol

Aptamers (small single strand DNA/RNAs) such as SYL3C are considered as ideal alternatives to antibodies in cancer related research studies. However, 3D structure predictions for aptamers and aptamer-protein complexes are scarce due to the high cost of experimental measurements and unreliable computer-based methods. Thus aptamers' diagnostic and therapeutic applications are severely restricted. To meet the challenge, we proposed a Martini-based aptamer-protein complex prediction protocol. By combining the base-base contact map from simulation and secondary structure prediction from various tools, improved secondary structure predictions can be obtained. This method reduced the risk of providing incorrect or incomplete base pairs in secondary structure prediction. Thus 3D structure modeling based on the secondary structure can be more reliable. We introduced the soft elastic network to the hairpin folded regions of the Martini ssDNAs to preserve their canonical structure. Using our protocol, we predicted the first 3D structure of the aptamer SYL3C and the SYL3C-EpCAM complex. We believe that our work could contribute to the future aptamer-related research studies and medical implications.

Inhibition of Nonfunctional Ras

Intuitively, functional states should be targeted; not nonfunctional ones. So why could drugging the inactive K-Ras4B^G12Cwork-but drugging the inactive kinase will likely not? The reason is the distinct oncogenic mechanisms. Kinase driver mutations work by stabilizing the active state and/or destabilizing the inactive state. Either way, oncogenic kinases are mostly in the active state. Ras driver mutations work by quelling its deactivation mechanisms, GTP hydrolysis, and nucleotide exchange. Covalent inhibitors that bind to the inactive GDP-bound K-Ras4B^G12C conformation can thus work. By contrast, in kinases, allosteric inhibitors work by altering the active-site conformation to favor orthosteric drugs. From the translational standpoint this distinction is vital: it expedites effective pharmaceutical development and extends the drug classification based on the mechanism of action. Collectively, here we postulate that drug action relates to blocking the mechanism of activation, not to whether the protein is in the active or inactive state.

Transient association between proteins elicits alteration of dynamics at sites far away from interfaces

Proteins are known to undergo structural changes upon binding to partner proteins. However, the prevalence, extent, location, and function of change in protein dynamics due to transient protein-protein interactions is not well documented. Here, we have analyzed a dataset of 58 protein-protein complexes of known three-dimensional structure and structures of their corresponding unbound forms to evaluate dynamics changes induced by binding. Fifty-five percent of cases showed significant dynamics change away from the interfaces. This change is not always accompanied by an observed structural change. Binding of protein partner is found to alter inter-residue communication within the tertiary structure in about 90% of cases. Also, residue motions accessible to proteins in unbound form were not always maintained in the bound form. Further analyses revealed functional roles for the distant site where dynamics change was observed. Overall, the results presented here strongly suggest that alteration of protein dynamics due to binding of a partner protein commonly occurs.

Long-lasting Salt Bridges Provide the Anchoring Mechanism of Oncogenic Kirsten Rat Sarcoma Proteins at Cell Membranes

RAS proteins work as GDP-GTP binary switches and regulate cytoplasmic signaling networks that are able to control several cellular processes, playing an essential role in signal transduction pathways involved in cell growth, differentiation, and survival, so that overacting RAS signaling can lead to cancer. One of the hardest challenges to face is the design of mutation-selective therapeutic strategies. In this work, a G12D-mutated farnesylated GTP-bound Kirsten RAt sarcoma (KRAS) protein has been simulated at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. A specific long-lasting salt bridge connection between farnesyl and the hypervariable region of the protein has been identified as the main mechanism responsible for the binding of oncogenic farnesylated KRAS-4B to the cell membrane. Free-energy landscapes allowed us to characterize local and global minima of KRAS-4B binding to the cell membrane, revealing the main pathways between anchored and released states.

Conformational gating, dynamics and allostery in human monoacylglycerol lipase

Inhibition of human Monoacylglycerol Lipase (hMGL) offers a novel approach for treating neurological diseases. The design of inhibitors, targeting active-inactive conformational transitions of the enzyme, can be aided by understanding the interplay between structure and dynamics. Here, we report the effects of mutations within the catalytic triad on structure, conformational gating and dynamics of hMGL by combining kinetics, NMR, and HDX-MS data with metadynamics simulations. We found that point mutations alter delicate conformational equilibria between active and inactive states. HDX-MS reveals regions of the hMGL that become substantially more dynamic upon substitution of catalytic acid Asp-239 by alanine. These regions, located far from the catalytic triad, include not only loops but also rigid α-helixes and β-strands, suggesting their involvement in allosteric regulation as channels for long-range signal transmission. The results identify the existence of a preorganized global communication network comprising of tertiary (residue-residue contacts) and quaternary (rigid-body contacts) networks that mediate robust, rapid intraprotein signal transmission. Catalytic Asp-239 controls hMGL allosteric communications and may be considered as an essential residue for the integration and transmission of information to enzymes' remote regions, in addition to its well-known role to facilitate Ser-122 activation. Our findings may assist in the identification of new druggable sites in hMGL.

The Energetic Origin of Different Catalytic Activities in Temperature-Adapted Trypsins

Psychrophilic enzymes were always observed to have higher catalytic activity (k cat) than their mesophilic homologs at room temperature, while the origin of this phenomenon remains obscure. Here, we used two different temperature-adapted trypsins, the psychrophilic Atlantic cod trypsin (ACT) and the mesophilic bovine trypsin (BT), as a model system to explore the energetic origin of their different catalytic activities using computational methods. The results reproduce the characteristic changing trends in the activation free energy, activation enthalpy, and activation entropy between the psychrophilic and mesophilic enzymes, where, in particular, the slightly decreased activation free energy of ACT is determined by its considerably reduced activation enthalpy rather than by its more negative activation entropy compared to BT. The calculated electrostatic contributions to the solvation free energies in the reactant state/ground sate (RS/GS) and transition state (TS) show that, going from BT to ACT, the TS stabilization has a predominant effect over the RS stabilization on lowering the activation enthalpy of ACT. Comparison between the solvation energy components reveals a more optimized electrostatic preorganization to the TS in ACT, which provides a larger stabilization to the TS through reducing the reorganization energy, thus resulting in the lower activation enthalpy and hence lower activation free energy of ACT. Thus, it can be concluded that it is the difference in the protein electrostatic environment, and hence its different stabilizing effects on the TS, that brings about the different catalytic activities of different temperature-adapted trypsins.

Effect of surface chemistry on islet amyloid polypeptide conformation

The formation of dense, linear arrays (fibrils) by biomolecules is the hallmark of a number of degenerative diseases, such as Alzheimer's and type-2 diabetes. Protein fibrils have also attracted interest as building blocks for new materials. It has long been recognized that surfaces can affect the fibrillation process. Recent work on the model fibril forming protein human islet amyloid polypeptide (hIAPP) has shown that while the protein concentration is highest at hydrophobic surfaces, the rate of fibril formation is lower than on other surfaces. To understand this, replica exchange molecular dynamics simulations were used to investigate the conformations that hIAPP adopts on surfaces of different hydrophobicities. The hydrophobic surface stabilizes α-helical structures which are significantly different to those found on the hydrophilic surface and in bulk solution. There is also a greatly reduced conformational ensemble on the hydrophobic surface due to long-lived contacts between hydrophobic residues on the protein and the surface. This new microscopic information will help us determine the mechanism of the enhancement of fibril formation on surfaces and provides new insight into the effect of nanointerfaces and protein conformation.

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.

Protein-Protein Binding as a Two-Step Mechanism: Preselection of Encounter Poses during the Binding of BPTI and Trypsin

Biomolecular recognition between proteins follows complex mechanisms, the understanding of which can substantially advance drug discovery efforts. Here, we track each step of the binding process in atomistic detail with molecular dynamics simulations using trypsin and its inhibitor bovine pancreatic trypsin inhibitor (BPTI) as a model system. We use umbrella sampling to cover a range of unbinding pathways. Starting from these simulations, we subsequently seed classical simulations at different stages of the process and combine them to a Markov state model. We clearly identify three kinetically separated states (an unbound state, an encounter state, and the final complex) and describe the mechanisms that dominate the binding process. From our model, we propose the following sequence of events. The initial formation of the encounter complex is driven by long-range interactions because opposite charges in trypsin and BPTI draw them together. The encounter complex features the prealigned binding partners with binding sites still partially surrounded by solvation shells. Further approaching leads to desolvation and increases the importance of van der Waals interactions. The native binding pose is adopted by maximizing short-range interactions. Thereby side-chain rearrangements ensure optimal shape complementarity. In particular, BPTI’s P1 residue adapts to the S1 pocket and prime site residues reorient to optimize interactions. After the paradigm of conformation selection, binding-competent conformations of BPTI and trypsin are already present in the apo ensembles and their probabilities increase during this proposed two-step association process. This detailed characterization of the molecular forces driving the binding process includes numerous aspects that have been discussed as central to the binding of trypsin and BPTI and protein complex formation in general. In this study, we combine all these aspects into one comprehensive model of protein recognition. We thereby contribute to enhance our general understanding of this fundamental mechanism, which is particularly critical as the development of biopharmaceuticals continuously gains significance.

Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal-modes analysis (NMA) using elastic network models (ENMs) provides an efficient procedure to explore global dynamics of proteins commonly associated with conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitudes of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact on the efficiency either to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

RNA-Dependent Structures of the RNA-Binding Loop in the Flavivirus NS3 Helicase

The flavivirus NS3 protein is a helicase that has pivotal functions during the viral genome replication process, where it unwinds double-stranded RNA and translocates along the nucleic acid polymer in a nucleoside triphosphate hydrolysis-dependent mechanism. Crystallographic and computational studies of the flavivirus NS3 helicase have identified the RNA-binding loop as an interesting structural element that may function as a component of the RNA-enhanced NTPase activity observed for this family of helicases. Microsecond-long unbiased molecular dynamics and extensive replica exchange umbrella sampling simulations of the Zika NS3 helicase have been performed to investigate the RNA dependence of this loop's structural conformations. Specifically, the effect of the bound single-stranded RNA (ssRNA) oligomer on the putative "open" and "closed" conformations of this loop is studied. In the Apo substrate state, the two loop conformations are nearly isoergonic (ΔAO→C = -0.22 kcal mol-1), explaining the structural ambiguity observed in Apo NS3h crystal structures. The bound ssRNA is seen to stabilize the "open" conformation (ΔAO→C = 1.97 kcal mol-1) through direct protein-RNA interactions at the top of the loop. Interestingly, a small ssRNA oligomer bound over 13 Å away from the loop is seen to affect the free energy surface to favor the "open" structure, while minimizing barriers between the two states. Both the mechanism of the "open" to "closed" transition and important residues of the RNA-binding loop structures are characterized. From these results, point mutations that are hypothesized to stabilize the "closed" RNA-binding loop and negatively impact RNA-binding and the RNA-enhanced NTPase activity are posited.

The roles of structural dynamics in the cellular functions of RNAs

RNAs fold into 3D structures that range from simple helical elements to complex tertiary structures and quaternary ribonucleoprotein assemblies. The functions of many regulatory RNAs depend on how their 3D structure changes in response to a diverse array of cellular conditions. In this Review, we examine how the structural characterization of RNA as dynamic ensembles of conformations, which form with different probabilities and at different timescales, is improving our understanding of RNA function in cells. We discuss the mechanisms of gene regulation by microRNAs, riboswitches, ribozymes, post-transcriptional RNA modifications and RNA-binding proteins, and how the cellular environment and processes such as liquid-liquid phase separation may affect RNA folding and activity. The emerging RNA-ensemble-function paradigm is changing our perspective and understanding of RNA regulation, from in vitro to in vivo and from descriptive to predictive.