DynOmics: dynamics of structural proteome and beyond

Probing the Structural Dynamics of the Plasmodium falciparum Tunneling-Fold Enzyme 6-Pyruvoyl Tetrahydropterin Synthase to Reveal Allosteric Drug Targeting Sites

The de novo folate synthesis pathway is a well-established drug target in the treatment of many infectious diseases. Antimalarial antifolate drugs have proven to be effective against malaria, however, rapid drug resistance has emerged on the two primary targeted enzymes: dihydrofolate reductase and dihydroptoreate synthase. The need to identify alternative antifolate drugs and novel metabolic targets is of imminent importance. The 6-pyruvol tetrahydropterin synthase (PTPS) enzyme belongs to the tunneling fold protein superfamily which is characterized by a distinct central tunnel/cavity. The enzyme catalyzes the second reaction step of the parasite’s de novo folate synthesis pathway and is responsible for the conversion of 7,8-dihydroneopterin to 6-pyruvoyl-tetrahydropterin. In this study, we examine the structural dynamics of Plasmodium falciparum PTPS using the anisotropic network model, to elucidate the collective motions that drive the function of the enzyme and identify potential sites for allosteric modulation of its binding properties. Based on our modal analysis, we identified key sites in the N-terminal domains and central helices which control the accessibility to the active site. Notably, the N-terminal domains were shown to regulate the open-to-closed transition of the tunnel, via a distinctive wringing motion that deformed the core of the protein. We, further, combined the dynamic analysis with motif discovery which revealed highly conserved motifs that are unique to the Plasmodium species and are located in the N-terminal domains and central helices. This provides essential structural information for the efficient design of drugs such as allosteric modulators that would have high specificity and low toxicity as they do not target the PTPS active site that is highly conserved in humans.

Structure of Furin Protease Binding to SARS-CoV-2 Spike Glycoprotein and Implications for Potential Targets and Virulence

The COVID-19 pandemic is an urgent global health emergency, and the presence of Furin site in the SARS-CoV-2 spike glycoprotein alters virulence and warrants further molecular, structural, and biophysical studies. Here we report the structure of Furin in complex with SARS-CoV-2 spike glycoprotein, demonstrating how Furin binds to the S1/S2 region of spike glycoprotein and eventually cleaves the viral protein using experimental functional studies, molecular dynamics, and docking. The structural studies underline the mechanism and mode of action of Furin, which is a key process in host cell entry and a hallmark of enhanced virulence. Our whole-exome sequencing analysis shows the genetic variants/alleles in Furin were found to alter the binding affinity for viral spike glycoprotein and could vary in infectivity in humans. Unravelling the mechanisms of Furin action, binding dynamics, and the genetic variants opens the growing arena of bona fide antibodies and development of potential therapeutics targeting the blockage of Furin cleavage.

Comparative Dynamics and Functional Mechanisms of the CYP17A1 Tunnels Regulated by Ligand Binding

As an important member of cytochrome P450 (CYP) enzymes, CYP17A1 is a dual-function monooxygenase with a critical role in the synthesis of many human steroid hormones, making it an attractive therapeutic target. The emerging structural information about CYP17A1 and the growing number of inhibitors for these enzymes call for a systematic strategy to delineate and classify mechanisms of ligand transport through tunnels that control catalytic activity. In this work, we applied an integrated computational strategy to different CYP17A1 systems with a panel of ligands to systematically study at the atomic level the mechanism of ligand-binding and tunneling dynamics. Atomistic simulations and binding free energy computations identify the dynamics of dominant tunnels and characterize energetic properties of critical residues responsible for ligand binding. The common transporting pathways including S, 3, and 2c tunnels were identified in CYP17A1 binding systems, while the 2c tunnel is a newly formed pathway upon ligand binding. We employed and integrated several computational approaches including the analysis of functional motions and sequence conservation, atomistic modeling of dynamic residue interaction networks, and perturbation response scanning analysis to dissect ligand tunneling mechanisms. The results revealed the hinge-binding and sliding motions as main functional modes of the tunnel dynamic, and a group of mediating residues as key regulators of tunnel conformational dynamics and allosteric communications. We have also examined and quantified the mutational effects on the tunnel composition, conformational dynamics, and long-range allosteric behavior. The results of this investigation are fully consistent with the experimental data, providing novel rationale to the experiments and offering valuable insights into the relationships between the structure and function of the channel networks and a robust atomistic model of activation mechanisms and allosteric interactions in CYP enzymes.

Structure-based functional fitness analyses of carbapenemase variants identified among pathogenic carbapenem-resistant Gram-negative bacteria

Carbapenemase-mediated carbapenem resistance is a major public health concerns worldwide. In the present study, prevalence of circulating carbapenemases was estimated among carbapenem-resistant clinical isolates using PCR and sequencing. Diameters of zone of inhibition (ZDs) were compared for imipenem, meropenem and ertapenem among single carbapenemase producing isolates. Structure-based functional fitness of those carbapenemases was predicted through several in silico analyses. Approximately, 63.76% isolates demonstrated carbapenem resistance, of which 39.13% harboured carbapenemases like bla_NDM-1 (33.23%), bla_NDM-1-like (0.31%), bla_VIM-2 (4.35%), bla_KPC-2 (4.04%), bla_OXA-181 (6.85%), bla_OXA-23 (16.50%), bla_OXA-69 (3.88%), bla_OXA-66 (2.91%) and bla_OXA-104 (1.94%). Omega values indicated selection pressure over bla_OXA-69, bla_OXA-66 and bla_OXA-104. Protein structural dynamics predicted NDM-1 and KPC-2 to have the highest and least flexibility, indicating differences in β-lactam binding and catalytic efficiency. Increased requirement of free folding energy, improved solvent accessibility and decreased melting temperatures among NDM-1-like, OXA-181, OXA-66, OXA-69 and OXA-104 predicted functional improvement over their ancestral variants. NDM-1-like carbapenemases demonstrated improvement in binding stability, affinity and catalysis of meropenem than that of NDM-1. Catalytic activity of imipenem was predicted to improve among OXA-181, which could be correlated with more than 1.5 folds smaller ZDs around imipenem disc, than that of meropenem/ertapenem, among OXA-181 producing isolates. However, OXA-66 indicated greater binding stability and affinity for imipenem and meropenem. This study indicated structural/functional convergence as well as divergence among several carbapenemase variants and provided useful insights into carbapenemase-mediated carbapenem resistance that might help in identifying appropriate treatment regimen for bacterial infections.

Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods

With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.

Exploring Mechanisms of Communication Switching in the Hsp90-Cdc37 Regulatory Complexes with Client Kinases through Allosteric Coupling of Phosphorylation Sites: Perturbation-Based Modeling and Hierarchical Community Analysis of Residue Interaction Networks

Understanding molecular principles underlying chaperone-based modulation of kinase client activity is critically important to dissect functions and activation mechanisms of many oncogenic proteins. The recent experimental studies have suggested that phosphorylation sites in the Hsp90 and Cdc37 proteins can serve as conformational communication switches of chaperone regulation and kinase interactions. However, a mechanism of allosteric coupling between phosphorylation sites in the Hsp90 and Cdc37 during client binding is poorly understood, and the molecular signatures underpinning specific roles of phosphorylation sites in the Hsp90 regulation remain unknown. In this work, we employed a combination of evolutionary analysis, coarse-grained molecular simulations together with perturbation-based network modeling and scanning of the unbound and bound Hsp90 and Cdc37 structures to quantify allosteric effects of phosphorylation sites and identify unique signatures that are characteristic for communication switches of kinase-specific client binding. By using network-based metrics of the dynamic intercommunity bridgeness and community centrality, we characterize specific signatures of phosphorylation switches involved in allosteric regulation. Through perturbation-based analysis of the dynamic residue interaction networks, we show that mutations of kinase-specific phosphorylation switches can induce long-range effects and lead to a global rewiring of the allosteric network and signal transmission in the Hsp90-Cdc37-kinase complex. We determine a specific group of phosphorylation sites in the Hsp90 where mutations may have a strong detrimental effect on allosteric interaction network, providing insight into the mechanism of phosphorylation-induced communication switching. The results demonstrate that kinase-specific phosphorylation switches of communications in the Hsp90 may be partly predisposed for their regulatory role based on preexisting allosteric propensities.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Complementary computational and experimental evaluation of missense variants in the ROMK potassium channel

The renal outer medullary potassium (ROMK) channel is essential for potassium transport in the kidney, and its dysfunction is associated with a salt-wasting disorder known as Bartter syndrome. Despite its physiological significance, we lack a mechanistic understanding of the molecular defects in ROMK underlying most Bartter syndrome-associated mutations. To this end, we employed a ROMK-dependent yeast growth assay and tested single amino acid variants selected by a series of computational tools representative of different approaches to predict each variants’ pathogenicity. In one approach, we used in silico saturation mutagenesis, i.e. the scanning of all possible single amino acid substitutions at all sequence positions to estimate their impact on function, and then employed a new machine learning classifier known as Rhapsody. We also used two additional tools, EVmutation and Polyphen-2, which permitted us to make consensus predictions on the pathogenicity of single amino acid variants in ROMK. Experimental tests performed for selected mutants in different classes validated the vast majority of our predictions and provided insights into variants implicated in ROMK dysfunction. On a broader scope, our analysis suggests that consolidation of data from complementary computational approaches provides an improved and facile method to predict the severity of an amino acid substitution and may help accelerate the identification of disease-causing mutations in any protein.

As the number of sequenced human genomes rises, a major challenge is to identify which single amino acid variations in a protein affect function and predispose individuals to disease. While predictive algorithms are available for this purpose, a comparative analysis of recently developed algorithms has not been adequately performed, nor is it clear whether combining algorithms would improve predictive power. To this end, we compared the efficacy of three publicly available algorithms and applied the results to Bartter syndrome, a human disease for which numerous poorly-characterized single amino acid variants have been identified and for which there is no cure. In silico saturation mutagenesis, i.e., the computational prediction of pathogenesis for every possible amino acid substitution, allowed us to experimentally test predictions by measuring the activity of an ion channel linked to Bartter syndrome. Based on data from blinded experiments, we discovered that Rhapsody and EVmutation successfully predicted deleterious mutations. Moreover, Rhapsody—which takes into account evolutionary as well as structural and dynamic considerations—predicted that >90% of known Bartter syndrome mutations are deleterious. Overall, our data will aid investigators who wish to test single amino acid variants in any protein and aid biomedical researchers who wish to develop hypotheses on the potential severity of genetic variants uncovered from genome databases.

Dynamic plasticity of the lipid antigen-binding site of CD1d is crucially favoured by acidic pH and helper proteins

CD1 molecules present lipid antigens for recognition by T-cell receptors (TCRs). Although a reasonably detailed picture of the CD1-lipid-TCR interaction exists, the initial steps regarding lipid loading onto and exchange between CD1 proteins remain elusive. The hydrophobic nature of lipids and the fact that CD1 molecules are unable to extract lipids from membranes raise the need for the assistance of helper proteins in lipid trafficking. However, the experimental study of this traffic in the endosomal compartments at which it occurs is so challenging that computational studies can help provide mechanistic insight into the associated processes. Here we present a multifaceted computational approach to obtain dynamic structural data on the human CD1d isotype. Conformational dynamics analysis shows an intrinsic flexibility associated with the protein architecture. Electrostatic properties together with molecular dynamics results for CD1d complexes with several lipids and helper proteins unravel the high dynamic plasticity of the antigen-binding site that is crucially favoured by acidic pH and the presence of helper proteins.

Computational Insight into the Allosteric Activation Mechanism of Farnesoid X Receptor

The farnesoid X receptor (FXR) is a bile acid-sensing transcription factor with indispensable roles in regulating metabolic processes. Nowadays, FXR has become a highly promising drug target for severe liver disorders, especially nonalcoholic steatohepatitis (NASH). A recent study showed that imatinib and its analogues were able to allosterically enhance agonist-induced FXR activation and its target gene expression. However, the allosteric modulation mechanism of FXR by these compounds remains unclear. In this work, the most effective imatinib analogue, P16, was used as a probe to explore this issue by computational approaches. Our results identified one potential allosteric site surrounded by residues Ile335, Phe336, Lys338, Glu339, Leu340, and Leu348, which could efficiently accommodate P16. In addition, the long-time molecular dynamics simulations indicated that the binding of P16 could significantly decrease the fluctuation of the co-activator and enhance the communications between the endogenous ligand chenodeoxycholic acid (CDCA) and FXR. By analyzing the residue interaction network, we observed two unique communication pathways connecting P16 and CDCA through three key residues, Arg331, Ser332, and Phe336. The communications of network organization in the P16-bound complex may allow the synergistic effect of the two compounds via robust signal transmission between the binding sites and global network bridges, which coordinate allosteric transitions and modulate the receptor activity. Our study offers insights into the allosteric modulation occurring in FXR and would be helpful for discovery of new allosteric modulators targeting FXR for further clinical research.

Theoretical study of the adiponectin receptors: binding site characterization and molecular dynamics of possible ligands for drug design

The two adiponectin receptors (AdipoR1 and AdipoR2) have been implicated in glucose and lipid regulation involved in several metabolic pathologies including type II diabetes. Their exact biochemical functions and mechanisms remain poorly understood. Moreover, these receptors do not yet have data on possible co-crystallized active ligands. In this study, we applied different computational methodologies to address three main unanswered questions: first, the localization and validation of possible binding sites; second, the generation of novel ligands with amenable characteristics to target the receptors; and third, the determination of important chemical interactions between the ligands and the receptors. Computational analysis of the binding site reveals that the residues triad R267, F271, and Y310 could be responsible for changes in the spatial arrangement and geometry of the binding pocket in AdipoR1. Molecular docking results in high docking scores of − 13.6 and − 16.5 kcal/mol for the top best ligands in AdipoR1 and AdipoR2 respectively. Finally, molecular dynamics suggests that hydrolytic activity may be possible with these compounds and that this reaction could be mediated by aspartic acid residues. The two adiponectin receptors have an endogenous protein ligand, adiponectin. However the synthesis is expensive and technically challenging. Although some debatable agonists have been proposed investigations of suitable synthetic ligands are indeed, very much needed for targeting these receptors and their associate pathologies and metabolic pathways. Furthermore, these findings provide a framework for further biochemical investigations of amenable compounds for drug discovery in order to target these receptors and their associated pathologies.

Modulation of Toroidal Proteins Dynamics in Favor of Functional Mechanisms upon Ligand Binding

Toroidal proteins serve as molecular machines and play crucial roles in biological processes such as DNA replication and RNA transcription. Despite progress in the structural characterization of several toroidal proteins, we still lack a mechanistic understanding of the significance of their architecture, oligomerization states, and intermolecular interactions in defining their biological function. In this work, we analyze the collective dynamics of toroidal proteins with different oligomerization states, namely, dimeric and trimeric DNA sliding clamps, nucleocapsid proteins (4-, 5-, and 6-mers) and Trp RNA-binding attenuation proteins (11- and 12-mers). We observe common global modes, among which cooperative rolling stands out as a mechanism enabling DNA processivity, and clamshell motions as those underlying the opening/closure of the sliding clamps. Alterations in global dynamics due to complexation with DNA or the clamp loader are shown to assist in enhancing motions to enable robust function. The analysis provides new insights into the differentiation and enhancement of functional motions upon intersubunit and intermolecular interactions.

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.

Molecular dynamics simulations and linear response theories jointly describe biphasic responses of myoglobin relaxation and reveal evolutionarily conserved frequent communicators

In this study, we provide a time-dependent mechanical model, taking advantage of molecular dynamics simulations, quasiharmonic analysis of molecular dynamics trajectories, and time-dependent linear response theories to describe vibrational energy redistribution within the protein matrix. The theoretical description explained the observed biphasic responses of specific residues in myoglobin to CO-photolysis and photoexcitation on heme. The fast responses were found to be triggered by impulsive forces and propagated mainly by principal modes <40 cm⁻¹. The predicted fast responses for individual atoms were then used to study signal propagation within the protein matrix and signals were found to propagate ~8 times faster across helices (4076 m/s) than within the helices, suggesting the importance of tertiary packing in the sensitivity of proteins to external perturbations. We further developed a method to integrate multiple intramolecular signal pathways and discover frequent “communicators”. These communicators were found to be evolutionarily conserved including those distant from the heme.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

Conformational flexibility of GRASPs and their constituent PDZ subdomains reveals structural basis of their promiscuous interactome

The Golgi complex is a central component of the secretory pathway, responsible for several critical cellular functions in eukaryotes. The complex is organized by the Golgi matrix that includes the Golgi reassembly and stacking protein (GRASP), which was shown to be involved in cisternae stacking and lateral linkage in metazoan. GRASPs also have critical roles in other processes, with an unusual ability to interact with several different binding partners. The conserved N terminus of the GRASP family includes two PSD-95, DLG, and ZO-1 (PDZ) domains. Previous crystallographic studies of orthologues suggest that PDZ1 and PDZ2 have similar conformations and secondary structure content. However, PDZ1 alone mediates nearly all interactions between GRASPs and their partners. In this work, NMR, synchrotron radiation CD, and molecular dynamics (MD) were used to examine the structure, flexibility, and stability of the two constituent PDZ domains. GRASP PDZs are structured in an unusual β₃ α₁ β₄ β₅ α₂ β₆ β₁ β₂ secondary structural arrangement and NMR data indicate that the PDZ1 binding pocket is formed by a stable β₂ -strand and a more flexible and unstable α₂ -helix, suggesting an explanation for the higher PDZ1 promiscuity. The conformational free energy profiles of the two PDZ domains were calculated using MD simulations. The data suggest that, after binding, the protein partner significantly reduces the conformational space that GRASPs can access by stabilizing one particular conformation, in a partner-dependent fashion. The structural flexibility of PDZ1, modulated by PDZ2, and the coupled, coordinated movement between the two PDZs enable GRASPs to interact with multiple partners, allowing them to function as promiscuous, multitasking proteins.

Resolution-exchanged structural modeling and simulations jointly unravel that subunit rolling underlies the mechanism of programmed ribosomal frameshifting

MOTIVATION

Programmed ribosomal frameshifting (PRF) is widely used by viruses and bacteria to produce different proteins from a single mRNA template. How steric hindrance of a PRF-stimulatory mRNA structure transiently modifies the conformational dynamics of the ribosome, and thereby allows tRNA slippage, remains elusive.

RESULTS

Here, we leverage linear response theories and resolution-exchanged simulations to construct a structural/dynamics model that connects and rationalizes existing structural, single-molecule and mutagenesis data by resolution-exchanged structural modelling and simulations. Our combined theoretical techniques provide a temporal and spatial description of PRF with unprecedented mechanistic details. We discover that ribosomal unfolding of the PRF-stimulating pseudoknot exerts resistant forces on the mRNA entrance of the ribosome, and thereby drives 30S subunit rolling. Such motion distorts tRNAs, leads to tRNA slippage, and in turn serves as a delicate control of cis-element's unwinding forces over PRF.

AVAILABILITY AND IMPLEMENTATION

All the simulation scripts and computational implementations of our methods/analyses (including linear response theory) are included in the bioStructureM suite, provided through GitHub at https://github.com/Yuan-Yu/bioStructureM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor

Protein dynamics is central to all biological processes, including signal transduction, cellular regulation and biological catalysis. Among them, in-depth exploration of ligand-driven protein dynamics contributes to an optimal understanding of protein function, which is particularly relevant to drug discovery. Hence, a wide range of computational tools have been designed to investigate the important dynamic information in proteins. However, performing and analyzing protein dynamics is still challenging due to the complicated operation steps, giving rise to great difficulty, especially for nonexperts. Moreover, there is a lack of web protocol to provide online facility to investigate and visualize ligand-driven protein dynamics. To this end, in this study, we integrated several bioinformatic tools to develop a protocol, named Ligand and Receptor Molecular Dynamics (LARMD, http://chemyang.ccnu.edu.cn/ccb/server/LARMD/ and http://agroda.gzu.edu.cn:9999/ccb/server/LARMD/), for profiling ligand-driven protein dynamics. To be specific, estrogen receptor (ER) was used as a case to reveal ERβ-selective mechanism, which plays a vital role in the treatment of inflammatory diseases and many types of cancers in clinical practice. Two different residues (Ile373/Met421 and Met336/Leu384) in the pocket of ERβ/ERα were the significant determinants for selectivity, especially Met336 of ERβ. The helix H8, helix H11 and H7-H8 loop influenced the migration of selective agonist (WAY-244). These computational results were consistent with the experimental results. Therefore, LARMD provides a user-friendly online protocol to study the dynamic property of protein and to design new ligand or site-directed mutagenesis.

Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior

Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.

An Efficient Timer and Sizer of Biomacromolecular Motions

Life ticks as fast as how proteins move. Computationally expensive molecular dynamics simulation has been the only theoretical tool to gauge the time and sizes of these motions, though barely to their slowest ends. Here, we convert a computationally cheap elastic network model (ENM) into a molecular timer and sizer to gauge the slowest functional motions of structured biomolecules. Quasi-harmonic analysis, fluctuation profile matching, and the Wiener-Khintchine theorem are used to define the "time periods," t, for anharmonic principal components (PCs), which are validated by nuclear magnetic resonance (NMR) order parameters. The PCs with their respective "time periods" are mapped to the eigenvalues (λ_ENM) of the corresponding ENM modes. Thus, the power laws t(ns) = 56.1λ_ENM^-1.6 and σ²(Ų) = 32.7λ_ENM^-3.0 can be established allowing the characterization of the timescales of NMR-resolved conformers, crystallographic anisotropic displacement parameters, and important ribosomal motions, as well as motional sizes of the latter.

TMB Library of Nucleosome Simulations

Nucleosomes are the fundamental building blocks of chromatin, the biomaterial that houses the genome in all higher organisms. A nucleosome consists of 145-147 base pairs of DNA wrapped 1.7 times around eight histones. Given a four-letter code (A, C, G, T), there are approximately 4147 or 1088 oligonucleotides that can form a nucleosome. Comparative, rather than comprehensive, studies are required. Here we introduce the TMB Library of nucleosome simulations and present a meta-analysis of over 20 μs of all atom molecular dynamics simulations representing 518 different realizations of the nucleosome. The TMB Library serves as a reference for future comparative, on-demand simulations of nucleosomes and a demonstration of iBIOMES Lite as a tool for managing a laboratory's simulation library. For every simulation, dewatered trajectories, RMSD, and DNA helical parameter data are provided through iBIOMES Lite in a Web browser and a file browser format. A novel view of nucleosomal DNA emerges from our meta-analysis of the TMB Library. DNA conformation is restricted to a specific left-handed superhelix, but the range of conformations observed for individual bases and base pairs is not more restricted nor more highly deformed than DNA free in solution. With the exception of Roll, mean DNA helical parameter values obtained from simulations of nucleosomes are largely within the range of thermal motion of DNA free in solution. The library provides evidence of DNA kinking in the nucleosome and clearly demonstrates the effects of DNA sequence on the gross structure and dynamics of nucleosomes. These effects and mispositioning of the 601 super strong nucleosome positioning sequence can be detected in short simulations (10 ns). Collectively, the results provide a basis for comparative simulation studies of nucleosomes and extend our understanding of the binding of proteins and drugs to nucleosomal DNA. The TMB Library can be found at http://dna.engr.latech.edu/~tmbshare/ .

Feedforward Control of Plant Nitrate Transporter NRT1.1 Biphasic Adaptive Activity

Defective nitrate signaling in plants causes disorder in nitrogen metabolism, and it negatively affects nitrate transport systems, which toggle between high- and low-affinity modes in variable soil nitrate conditions. Recent discovery of a plasma membrane nitrate transceptor protein NRT1.1-a transporter cum sensor-provides a clue on this toggling mechanism. However, the general mechanistic description still remains poorly understood. Here, we illustrate adaptive responses and regulation of NRT1.1-mediated nitrate signaling in a wide range of extracellular nitrate concentrations. The results show that the homodimeric structure of NRT1.1 and its dimeric switch play an important role in eliciting specific cytosolic calcium waves sensed by the calcineurin-B-like calcium sensor CBL9, which activates the kinase CIPK23, in low nitrate concentration that is, however, impeded in high nitrate concentration. Nitrate binding at the high-affinity unit initiates NRT1.1 dimer decoupling and priming of the Thr101 site for phosphorylation by CIPK23. This phosphorylation stabilizes the NRT1.1 monomeric state, acting as a high-affinity nitrate transceptor. However, nitrate binding in both monomers, retaining the unmodified NRT1.1 state through dimerization, attenuates CIPK23 activity and thereby maintains the low-affinity mode of nitrate signaling and transport. This phosphorylation-led modulation of NRT1.1 activity shows bistable behavior controlled by an incoherent feedforward loop, which integrates nitrate-induced positive and negative regulatory effects on CIPK23. These results, therefore, advance our molecular understanding of adaptation in fluctuating nutrient availability and are a way forward for improving plant nitrogen use efficiency.

Probing Protein Allostery as a Residue-Specific Concept via Residue Response Maps

Allosteric regulation is a well-established phenomenon defined as a distal conformational or dynamical change of the protein upon allosteric effector binding. Here, we developed a novel approach to delineate allosteric effects in proteins. In this approach, we applied robust machine learning methods, including deep neural network and random forest, on extensive molecular dynamics (MD) simulations to distinguish otherwise similar allosteric states of proteins. Using the PDZ3 domain of PDS-95 as a model protein, we demonstrated that the allosteric effects could be represented as residue-specific properties through two-dimensional property-residue maps, which we refer to as "residue response maps". These maps were constructed through two machine learning methods and could accurately describe how different properties of various residues are affected upon allosteric perturbation on protein. Based on the "residue response maps", we propose allostery as a residue-specific concept, suggesting that all residues could be considered as allosteric residues because each residue "senses" the allosteric events through changing its single or multiple attributes in a quantitatively unique way. The "residue response maps" could be used to fingerprint a protein based on the unique patterns of residue responses upon binding events, providing a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.

Hinge Region in DNA Packaging Terminase pUL15 of Herpes Simplex Virus: A Potential Allosteric Target for Antiviral Drugs

Approximately 80% of adults are infected with a member of the herpesviridae family. Herpesviruses establish life-long latent infections within neurons, which may reactivate into lytic infections due to stress or immune suppression. There are nine human herpesviruses (HHV) posing health concerns from benign conditions to life threatening encephalitis, including cancers associated with viral infections. The current treatment options for most HHV conditions mainly include several nucleoside and nucleotide analogs targeting viral DNA polymerase. Although these drugs help manage infections, their common mechanism of action may lead to the development of drug resistance, which is particularly devastating in immunocompromised patients. Therefore, new classes of drugs directed against novel targets in HHVs are necessary to alleviate this issue. We analyzed the conservation rates of all proteins in herpes simplex virus 1 (HHV-1), a representative of the HHV family and one of the most common viruses infecting the human population. Furthermore, we generated a full-length structure model of the most conserved HHV-1 protein, the DNA packaging terminase pUL15. A series of computational analyses were performed on the model to identify ATP and DNA binding sites and characterize the dynamics of the protein. Our study indicates that proteins involved in HHV-1 DNA packaging and cleavage are amongst the most conserved gene products of HHVs. Since the packaging protein pUL15 is the most conserved among all HHV-1 gene products, the virus will have a lower chance of developing resistance to small molecules targeting pUL15. A subsequent analysis of the structure of pUL15 revealed distinct ATP and DNA binding domains and the elastic network model identifies a functionally important hinge region between the two domains of pUL15. The atomic information on the active and allosteric sites in the ATP- and DNA-bound model of pUL15 presented in this study can inform the structure-based drug discovery of a new class of drugs to treat a wide range of HHVs.

Normal Mode Analysis as a Routine Part of a Structural Investigation

Normal mode analysis (NMA) is a technique that can be used to describe the flexible states accessible to a protein about an equilibrium position. These states have been shown repeatedly to have functional significance. NMA is probably the least computationally expensive method for studying the dynamics of macromolecules, and advances in computer technology and algorithms for calculating normal modes over the last 20 years have made it nearly trivial for all but the largest systems. Despite this, it is still uncommon for NMA to be used as a component of the analysis of a structural study. In this review, we will describe NMA, outline its advantages and limitations, explain what can and cannot be learned from it, and address some criticisms and concerns that have been voiced about it. We will then review the most commonly used techniques for reducing the computational cost of this method and identify the web services making use of these methods. We will illustrate several of their possible uses with recent examples from the literature. We conclude by recommending that NMA become one of the standard tools employed in any structural study.

AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses

Allostery tweaks innumerable biological processes and plays a fundamental role in human disease and drug discovery. Exploration of allostery has thus been regarded as a crucial requirement for research on biological mechanisms and the development of novel therapeutics. Here, based on our previously developed allosteric data and methods, we present an interactive platform called AlloFinder that identifies potential endogenous or exogenous allosteric modulators and their involvement in human allosterome. AlloFinder automatically amalgamates allosteric site identification, allosteric screening and allosteric scoring evaluation of modulator-protein complexes to identify allosteric modulators, followed by allosterome mapping analyses of predicted allosteric sites and modulators in human proteome. This web server exhibits prominent performance in the reemergence of allosteric metabolites and exogenous allosteric modulators in known allosteric proteins. Specifically, AlloFinder enables identification of allosteric metabolites for metabolic enzymes and screening of potential allosteric compounds for disease-related targets. Significantly, the feasibility of AlloFinder to discover allosteric modulators was tested in a real case of signal transduction and activation of transcription 3 (STAT3) and validated by mutagenesis and functional experiments. Collectively, AlloFinder is expected to contribute to exploration of the mechanisms of allosteric regulation between metabolites and metabolic enzymes, and to accelerate allosteric drug discovery. The AlloFinder web server is freely available to all users at http://mdl.shsmu.edu.cn/ALF/.

Identification of key sites controlling protein functional motions by using elastic network model combined with internal coordinates

The elastic network model (ENM) is an effective method to extract the intrinsic dynamical properties encoded in protein tertiary structures. We have proposed a new ENM-based analysis method to reveal the motion modes directly responsible for a specific protein function, in which an internal coordinate related to the specific function was introduced to construct the internal/Cartesian hybrid coordinate space. In the present work, the function-related internal coordinates combined with a linear perturbation method were applied to identify the key sites controlling specific protein functional motions. The change in the fluctuations of the internal coordinate in response to residue perturbation was calculated in the hybrid coordinate space by using the linear response theory. The residues with the large fluctuation changes were identified to be the key sites that allosterically control the specific protein function. Two proteins, i.e., human DNA polymerase β and the chaperonin from Methanococcus maripaludis, were investigated as case studies, in which several collective and local internal coordinates were applied to identify the functionally key residues of these two studied proteins. The calculation results are consistent with the experimental observations. It is found that different collective internal coordinates lead to similar results, where the predicted functionally key sites are located at similar positions in the protein structure. While for the local internal coordinates, the predicted key sites tend to be situated at the region near to the coordinate-involving residues. Our studies provide a starting point for further exploring other function-related internal coordinates for other interesting proteins.

Cryo-EM structure of OSCA1.2 from Oryza sativa elucidates the mechanical basis of potential membrane hyperosmolality gating

Sensing and responding to environmental water deficiency and osmotic stresses are essential for the growth, development, and survival of plants. Recently, an osmolality-sensing ion channel called OSCA1 was discovered that functions in sensing hyperosmolality in Arabidopsis Here, we report the cryo-electron microscopy (cryo-EM) structure and function of an OSCA1 homolog from rice (Oryza sativa; OsOSCA1.2), leading to a model of how it could mediate hyperosmolality sensing and transport pathway gating. The structure reveals a dimer; the molecular architecture of each subunit consists of 11 transmembrane (TM) helices and a cytosolic soluble domain that has homology to RNA recognition proteins. The TM domain is structurally related to the TMEM16 family of calcium-dependent ion channels and lipid scramblases. The cytosolic soluble domain possesses a distinct structural feature in the form of extended intracellular helical arms that are parallel to the plasma membrane. These helical arms are well positioned to potentially sense lateral tension on the inner leaflet of the lipid bilayer caused by changes in turgor pressure. Computational dynamic analysis suggests how this domain couples to the TM portion of the molecule to open a transport pathway. Hydrogen/deuterium exchange mass spectrometry (HDXMS) experimentally confirms the conformational dynamics of these coupled domains. These studies provide a framework to understand the structural basis of proposed hyperosmolality sensing in a staple crop plant, extend our knowledge of the anoctamin superfamily important for plants and fungi, and provide a structural mechanism for potentially translating membrane stress to transport regulation.

Shared dynamics of LeuT superfamily members and allosteric differentiation by structural irregularities and multimerization

The LeuT-fold superfamily includes secondary active transporters from different functional families, which share a common tertiary structure, despite having a remarkably low sequence similarity. By identifying the common structural and dynamical features upon principal component analysis of a comprehensive ensemble of 90 experimentally resolved structures and anisotropic network model evaluation of collective motions, we provide a unified point of view for understanding the reasons why this particular fold has been selected by evolution to accomplish such a broad spectrum of functions. The parallel identification of conserved sequence features, localized at specific sites of transmembrane helices, sheds light on the role of broken helices (TM1 and TM6 in LeuT) in promoting ion/substrate binding and allosteric interconversion between the outward- and inward-facing conformations of transporters. Finally, the determination of the dynamics landscape for the structural ensemble provides a promising framework for the classification of transporters based on their dynamics, and the characterization of the collective movements that favour multimerization.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Characterization of Differential Dynamics, Specificity, and Allostery of Lipoxygenase Family Members

Accurate modeling of structural dynamics of proteins and their differentiation across different species can help us understand generic mechanisms of function shared by family members and the molecular basis of the specificity of individual members. We focused here on the family of lipoxygenases, enzymes that catalyze lipid oxidation, the mammalian and bacterial structures of which have been elucidated. We present a systematic method of approach for characterizing the sequence, structure, dynamics, and allosteric signaling properties of these enzymes using a combination of structure-based models and methods and bioinformatics tools applied to a data set of 88 structures. The analysis elucidates the signature dynamics of the lipoxygenase family and its differentiation among members, as well as key sites that enable its adaptation to specific substrate binding and allosteric activity.

Computational investigation of mechanistic insights of Aβ42 interactions against extracellular domain of nAChRα7 in Alzheimer's disease

AIM

Amyloid beta (Aβ) 1-42, which is a basic constituent of amyloid plaques, binds with extracellular transmembrane receptor nicotine acetylcholine receptor α7 (nAChRα7) in Alzheimer's disease.

MATERIALS AND METHODS

In the current study, a computational approach was employed to explore the active binding sites of nAChRα7 through Aβ 1-42 interactions and their involvement in the activation of downstream signalling pathways. Sequential and structural analyses were performed on the extracellular part of nAChRα7 to identify its core active binding site.

RESULTS

Results showed that a conserved residual pattern and well superimposed structures were observed in all nAChRs proteins. Molecular docking servers were used to predict the common interactive residues in nAChRα7 and Aβ1-42 proteins. The docking profile results showed some common interactive residues such as Glu22, Ala42 and Trp171 may consider as the active key player in the activation of downstream signalling pathways. Moreover, the signal communication and receiving efficacy of best-docked complexes was checked through DynOmic online server. Furthermore, the results from molecular dynamic simulation experiment showed the stability of nAChRα7. The generated root mean square deviations and fluctuations (RMSD/F), solvent accessible surface area (SASA) and radius of gyration (Rg) graphs of nAChRα7 also showed its backbone stability and compactness, respectively.

CONCLUSION

Taken together, our predicted results intimated the structural insight on the molecular interactions of beta amyloid protein involved in the activation of nAChRα7 receptor. In future, a better understanding of nAChRα7 and their interconnected proteins signalling cascade may be consider as target to cure Alzheimer's disease.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

Key residues controlling bidirectional ion movements in Na+/Ca2+ exchanger

Prokaryotic and eukaryotic Na+/Ca2+ exchangers (NCX) control Ca2+ homeostasis. NCX orthologs exhibit up to 104-fold differences in their turnover rates (kcat), whereas the ratios between the cytosolic (cyt) and extracellular (ext) Km values (Kint = KmCyt/KmExt) are highly asymmetric and alike (Kint ≤ 0.1) among NCXs. The structural determinants controlling a huge divergence in kcat at comparable Kint remain unclear, although 11 (out of 12) ion-coordinating residues are highly conserved among NCXs. The crystal structure of the archaeal NCX (NCX_Mj) was explored for testing the mutational effects of pore-allied and loop residues on kcat and Kint. Among 55 tested residues, 26 mutations affect either kcat or Kint, where two major groups can be distinguished. The first group of mutations (14 residues) affect kcat rather than Kint. The majority of these residues (10 out of 14) are located within the extracellular vestibule near the pore center. The second group of mutations (12 residues) affect Kint rather than kcat, whereas the majority of residues (9 out 12) are randomly dispersed within the extracellular vestibule. In conjunction with computational modeling-simulations and hydrogen-deuterium exchange mass-spectrometry (HDX-MS), the present mutational analysis highlights structural elements that differentially govern the intrinsic asymmetry and transport rates. The key residues, located at specific segments, can affect the characteristic features of local backbone dynamics and thus, the conformational flexibility of ion-transporting helices contributing to critical conformational transitions. The underlying mechanisms might have a physiological relevance for matching the response modes of NCX variants to cell-specific Ca2+ and Na+ signaling.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Pseudomonas aeruginosa utilizes host polyunsaturated phosphatidylethanolamines to trigger theft-ferroptosis in bronchial epithelium

Ferroptosis is a death program executed via selective oxidation of arachidonic acid-phosphatidylethanolamines (AA-PE) by 15-lipoxygenases. In mammalian cells and tissues, ferroptosis has been pathogenically associated with brain, kidney, and liver injury/diseases. We discovered that a prokaryotic bacterium, Pseudomonas aeruginosa, that does not contain AA-PE can express lipoxygenase (pLoxA), oxidize host AA-PE to 15-hydroperoxy-AA-PE (15-HOO-AA-PE), and trigger ferroptosis in human bronchial epithelial cells. Induction of ferroptosis by clinical P. aeruginosa isolates from patients with persistent lower respiratory tract infections was dependent on the level and enzymatic activity of pLoxA. Redox phospholipidomics revealed elevated levels of oxidized AA-PE in airway tissues from patients with cystic fibrosis (CF) but not with emphysema or CF without P. aeruginosa. We believe that the evolutionarily conserved mechanism of pLoxA-driven ferroptosis may represent a potential therapeutic target against P. aeruginosa-associated diseases such as CF and persistent lower respiratory tract infections.

Dynamics-based community analysis and perturbation response scanning of allosteric interaction networks in the TRAP1 chaperone structures dissect molecular linkage between conformational asymmetry and sequential ATP hydrolysis

Allosteric interactions of the Hsp90 chaperones with cochaperones and diverse protein clients can often exhibit distinct asymmetric features that determine regulatory mechanisms and cellular functions in many signaling networks. The recent crystal structures of the mitochondrial Hsp90 isoform TRAP1 in complexes with ATP analogs have provided first evidence of significant asymmetry in the closed dimerized state that triggers independent activity of the chaperone protomers, whereby preferential hydrolysis of the buckled protomer is followed by conformational flipping between protomers and hydrolysis of the second protomer. Despite significant insights in structural characterizations of the TRAP1 chaperone, the atomistic details and mechanics of allosteric interactions that couple sequential ATP hydrolysis with asymmetric conformational switching in the TRAP1 protomers remain largely unknown. In this work, we explored atomistic and coarse-grained simulations of the TRAP1 dimer structures in combination with the ensemble-based network modeling and perturbation response scanning of residue interaction networks to probe salient features underlying allosteric signaling mechanism. This study has revealed that key effector sites that orchestrate allosteric interactions occupy the ATP binding region and N-terminal interface of the buckled protomer, whereas the main sensors of allosteric signals that drive functional conformational changes during ATPase cycle are consolidated near the client binding region of the straight protomer, channeling the energy of ATP hydrolysis for client remodeling. The community decomposition analysis of the interaction networks and reconstruction of allosteric communication pathways in the TRAP1 structures have quantified mechanism of allosteric regulation, revealing control points and interactions that coordinate asymmetric switching during ATP hydrolysis.

Structural dynamics is a determinant of the functional significance of missense variants

Discrimination of clinically relevant mutations from neutral mutations is of paramount importance in precision medicine and pharmacogenomics. Our study shows that current computational predictions of pathogenicity, mostly based on analysis of sequence conservation, may be improved by considering the changes in the structural dynamics of the protein due to point mutations. We introduce and demonstrate the utility of a classifier that takes advantage of efficient evaluation of structural dynamics by elastic network models.

Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein’s structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.

Activation and desensitization of ionotropic glutamate receptors by selectively triggering pre-existing motions

Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that are key players in synaptic transmission and plasticity. They are composed of four subunits, each containing four functional domains, the quaternary packing and collective structural dynamics of which are important determinants of their molecular mechanism of function. With the explosion of structural studies on different members of the family, including the structures of activated open channels, the mechanisms of action of these central signaling machines are now being elucidated. We review the current state of computational studies on two major members of the family, AMPA and NMDA receptors, with focus on molecular simulations and elastic network model analyses that have provided insights into the coupled movements of extracellular and transmembrane domains. We describe the newly emerging mechanisms of activation, allosteric signaling and desensitization, as mainly a selective triggering of pre-existing soft motions, as deduced from computational models and analyses that leverage structural data on intact AMPA and NMDA receptors in different states.

A speculation on the tandem fasciclin 1 repeat of FLA4 proteins in angiosperms

The Arabidopsis thaliana Fasciclin like arabinogalactan protein 4 (FLA4) locus is required for normal root growth in a linear genetic pathway with the FEI1 and FEI2 loci coding for receptor-like kinases. The two Fas1 domains of FLA4 are conserved among angiosperms but only the C-terminal Fas1 domain is required for genetic function. We show that at low salt deletion of the N-terminal Fas1 domain of transgenic FLA4 leads to enhanced root elongation compared to the tandem Fas1 wild type version. Modeling the hypothetical interaction between FLA4 and FEI1 we show that the predicted interaction is predominantly involving the C-terminal Fas1 domain. Relative conformational mobility between the two FLA4 Fas1 domains might regulate the interaction with the FEI receptor kinases. We therefore speculate that the FLA4 FEI complex might be a sensor for environmental conditions in the apoplast.

Network-based modelling and percolation analysis of conformational dynamics and activation in the CDK2 and CDK4 proteins: dynamic and energetic polarization of the kinase lobes may determine divergence of the regulatory mechanisms

Network modeling and percolation analysis of conformational dynamics and energetics of regulatory mechanisms in cyclin-dependent kinases.