Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms

The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.

The alteration of structural network upon transient association between proteins studied using graph theory

Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.

Effective drug design screening in bacterial glycolytic enzymes via targeting alternative allosteric sites

Three glycolytic enzymes phosphofructokinase (PFK), glyceraldehyde-3-phosphate dehydrogenase (GADPH) and pyruvate kinase (PK) that belong to Staphylococcus aureus were used as targets for screening a dataset composed of 7229 compounds of which 1416 were FDA-approved. Instead of catalytic sites, evolutionarily less conserved allosteric sites were targeted to identify compounds that would selectively bind the bacteria's glycolytic enzymes instead of the human host. Seven different allosteric sites provided by three enzymes were used in independent screening experiments via docking. For each of the seven sites, a total of 723 compounds were selected as the top 10 % which displayed the highest binding affinities. All compounds were then united to yield the top 54 drug candidates shared by all seven sites. Next, 17 out of 54 were selected and subjected to in vitro experiments for testing their inhibition capability for antibacterial growth and enzymatic activity. Accordingly, four compounds displaying antibacterial growth inhibition above 40 % were determined as Candesartan cilexetil, Montelukast (sodium), Dronedarone (hydrochloride) and Thonzonium (bromide). In a second round of experiment, Candesartan cilexetil and Thonzonium displayed exceptionally high killing efficiencies on two bacterial strains of S.aureus (methicillin-sensitive and methicillin-resistant) with concentrations as low as 4 μg/mL and 0.5 μg/mL. Yet, their enzymatic assays were not in accordance with their killing effectiveness. Different inhibitory effects was observed for each compound in each enzymatic assay. A more effective target strategy would be to screen for drug compounds that woud inhibit a combination of glycolytic enzymes observed in the glycolytic pathway.

Theory vs. experiment: The rise of the dynamic view of proteins

Over the past century, the scientific conception of the protein has evolved significantly. This paper focuses on the most recent stage of this evolution, namely, the origin of the dynamic view of proteins and the challenge it posed to the static view of classical molecular biology. Philosophers and scientists have offered two hypotheses to explain the origin of the dynamic view and its slow reception by structural biologists. Some have argued that the shift from the static to the dynamic view was a Kuhnian revolution, driven by the accumulation of dynamic anomalies, while others have argued that the shift was caused by new empirical findings made possible by technological advances. I analyze this scientific episode and ultimately reject both of these empiricist accounts. I argue that focusing primarily on technological advances and empirical discoveries overlooks the important role of theory in driving this scientific change. I show how the application of general thermodynamic principles to proteins gave rise to the dynamic view, and a commitment to these principles then led early adopters to seek out the empirical examples of protein dynamics, which would eventually convince their peers. My analysis of this historical case shows that empiricist accounts of modern scientific progress-at least those that aim to explain developments in the molecular life sciences-need to be tempered in order to capture the interplay between theory and experiment.

A neutral invertase controls cell division besides hydrolysis of sucrose for nutrition during germination and seed setting in rice

Sucrose is the transport form of carbohydrate in plants serving as signal molecule besides nutrition, but the signaling is elusive. Here, neutral invertase 8 (OsNIN8) mutated at G461R into OsNIN8m, which increased its charge and hydrophobicity, decreased hydrolysis of sucrose to 13% and firmer binding to sucrose than the wildtype. This caused downstream metabolites and energy accumulation forming overnutrition. Paradoxically, division of subinitials in longitudinal cell lineages was only about 15 times but more than 100 times in wildtype, resulting in short radicle. Further, mutation of OsNIN8 into deficiency of hydrolysis but maintenance of sucrose binding allowed cell division until ran out of energy showing the association but not hydrolysis gave the signal. Chemically, sucrose binding to OsNIN8 was exothermic but to OsNIN8m was endothermic. Therefore, OsNIN8m lost the signal function owing to change of thermodynamic state. So, OsNIN8 sensed sucrose for cell division besides hydrolyzed sucrose.

Normal Mode Analysis Elicits Conformational Shifts in Proteins at Both Proximal and Distal Regions to the Phosphosite Stemming from Single-Site Phosphorylation

Phosphorylation, a fundamental biochemical switch, intricately regulates protein function and signaling pathways. Our study employs extensive computational structural analyses on a curated data set of phosphorylated and unphosphorylated protein pairs to explore the multifaceted impact of phosphorylation on protein conformation. Using normal mode analysis (NMA), we investigated changes in protein flexibility post-phosphorylation, highlighting an enhanced level of structural dynamism. Our findings reveal that phosphorylation induces not only local changes at the phosphorylation site but also extensive alterations in distant regions, showcasing its far-reaching influence on protein structure-dynamics. Through in-depth case studies on polyubiquitin B and glycogen synthase kinase-3 beta, we elucidate how phosphorylation at distinct sites leads to variable structural and dynamic modifications, potentially dictating functional outcomes. While phosphorylation largely preserves the residue motion correlation, it significantly disrupts low-frequency global modes, presenting a dualistic impact on protein dynamics. We also explored alterations in the total accessible surface area (ASA), emphasizing region-specific changes around phosphorylation sites. This study sheds light on phosphorylation-induced conformational changes, dynamic modulation, and surface accessibility alterations, leveraging an integrated computational approach with RMSD, NMA, and ASA, thereby contributing to a comprehensive understanding of cellular regulation and suggesting promising avenues for therapeutic interventions.

Allosteric crosstalk in modular proteins: Function fine-tuning and drug design

Modular proteins are regulatory proteins that carry out more than one function. These proteins upregulate or downregulate a biochemical cascade to establish homeostasis in cells. To switch the function or alter the efficiency (based on cellular needs), these proteins require different facilitators that bind to a site different from the catalytic (active/orthosteric) site, aka 'allosteric site', and fine-tune their function. These facilitators (or effectors) are allosteric modulators. In this Review, we have discussed the allostery, characterized them based on their mechanisms, and discussed how allostery plays an important role in the activity modulation and function fine-tuning of proteins. Recently there is an emergence in the discovery of allosteric drugs. We have also emphasized the role, significance, and future of allostery in therapeutic applications.

DNA Tile and Invading Stacking Primer-Assisted CRISPR-Cas12a Multiple Amplification System for Entropy-Driven Electrochemical Detection of MicroRNA with Tunable Sensitivity

Conventional electrochemical detection of microRNA (miRNA) encounters issues of poor sensitivity and fixed dynamic range. Here, we report a DNA tile and invading stacking primer-assisted CRISPR-Cas12a multiple amplification strategy to construct an entropy-controlled electrochemical biosensor for the detection of miRNA with tunable sensitivity and dynamic range. To amplify the signal, a cascade amplification of the CRISPR-Cas12a system along with invading stacking primer signal amplification (ISPSA) was designed to detect trace amounts of miRNA-31 (miR-31). The target miR-31 could activate ISPSA and produce numerous DNAs, triggering the cleavage of the single-stranded linker probe (LP) that connects a methylene blue-labeled DNA tile with a DNA tetrahedron to form a Y-shaped DNA scaffold on the electrode. Based on the decrease of current, miR-31 can be accurately and efficiently detected. Impressively, by changing the loop length of the LP, it is possible to finely tune the entropic contribution while keeping the enthalpic contribution constant. This strategy has shown a tunable limit of detection for miRNA from 0.31 fM to 0.56 pM, as well as a dynamic range from ∼2200-fold to ∼270,000-fold. Moreover, it demonstrated satisfactory results in identifying cancer cells with a high expression of miR-31. Our strategy broadens the application of conventional electrochemical biosensing and provides a tunable strategy for detecting miRNAs at varying concentrations.

Insight into the Nucleotide Based Modulation of the Grp94 Molecular Chaperone Using Multiscale Dynamics

Grp94, an ER-localized molecular chaperone, is required for the folding and activation of many membrane and secretory proteins. Client activation by Grp94 is mediated by nucleotide and conformational changes. In this work, we aim to understand how microscopic changes from nucleotide hydrolysis can potentiate large-scale conformational changes of Grp94. We performed all-atom molecular dynamics simulations on the ATP-hydrolysis competent state of the Grp94 dimer in four different nucleotide bound states. We found that Grp94 was the most rigid when ATP was bound. ATP hydrolysis or nucleotide removal enhanced mobility of the N-terminal domain and ATP lid, resulting in suppression of interdomain communication. In an asymmetric conformation with one hydrolyzed nucleotide, we identified a more compact state, similar to experimental observations. We also identified a potential regulatory role of the flexible linker, as it formed electrostatic interactions with the Grp94 M-domain helix near the region where BiP is known to bind. These studies were complemented with normal-mode analysis of an elastic network model to investigate Grp94's large-scale conformational changes. SPM analysis identified residues that are important in signaling conformational change, many of which have known functional relevance in ATP coordination and catalysis, client binding, and BiP binding. Our findings suggest that ATP hydrolysis in Grp94 alters allosteric wiring and facilitates conformational changes.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

Ionic Cooperativity between Lysine and Potassium in the Lysine Riboswitch: Single-Molecule Kinetic and Thermodynamic Studies

Functionality in many biological systems, including proteins and nucleic acid structures, including protein and nucleic acid riboswitch structures, can depend on cooperative kinetic behavior between multiple small molecule ligands. In this work, single-molecule FRET data on the Bacillus subtilis lysine riboswitch reveals that affinity for the cognate lysine ligand increases significantly with K⁺, providing evidence for synergism between lysine/K⁺ binding to the aptamer and successful folding of the riboswitch. To describe/interpret this more complex kinetic scenario, we explore the conventional 4-state ("square") model for aptamer binding as a function of K⁺. Extension into this additional dimension generates a novel "cube" model for riboswitch folding dynamics with respect to lysine/K⁺ binding, revealing that riboswitch folding (k_fold) and unfolding (k_unfold) rate constants increase and decrease dramatically with K⁺, respectively. Furthermore, temperature-dependent single-molecule kinetic studies indicate that the presence of K⁺ entropically enhances the transition state barrier to folding but partially compensates for this by increasing the overall exothermicity for lysine binding. We rationalize this behavior as evidence that K⁺ facilitates hydrogen bonding between the negatively charged carboxyl group of lysine and the RNA, increasing structural rigidity and lowering entropy in the binding pocket. Finally, we explore the effects of cation size with Na⁺ and Cs⁺ studies to demonstrate that K⁺ is optimally suited for bridging interactions between lysine and the riboswitch aptamer domain. Regulation of lysine production and transport, dictated by the riboswitch's ability to recognize and bind lysine, is therefore intimately tied to the presence of K⁺ in the binding pocket and is strongly modulated by local cation conditions. The results suggest an increase in lysine riboswitch functionality by sensitivity to additional species in the cellular riboswitch environment.

Coevolution-based prediction of key allosteric residues for protein function regulation

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar, and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.

Allosteric Modulators of Metabotropic Glutamate Receptors as Novel Therapeutics for Neuropsychiatric Disease

Metabotropic glutamate (mGlu) receptors, a family of G-protein-coupled receptors, have been identified as novel therapeutic targets based on extensive research supporting their diverse contributions to cell signaling and physiology throughout the nervous system and important roles in regulating complex behaviors, such as cognition, reward, and movement. Thus, targeting mGlu receptors may be a promising strategy for the treatment of several brain disorders. Ongoing advances in the discovery of subtype-selective allosteric modulators for mGlu receptors has provided an unprecedented opportunity for highly specific modulation of signaling by individual mGlu receptor subtypes in the brain by targeting sites distinct from orthosteric or endogenous ligand binding sites on mGlu receptors. These pharmacological agents provide the unparalleled opportunity to selectively regulate neuronal excitability, synaptic transmission, and subsequent behavioral output pertinent to many brain disorders. Here, we review preclinical and clinical evidence supporting the utility of mGlu receptor allosteric modulators as novel therapeutic approaches to treat neuropsychiatric diseases, such as schizophrenia, substance use disorders, and stress-related disorders. SIGNIFICANCE STATEMENT: Allosteric modulation of metabotropic glutamate (mGlu) receptors represents a promising therapeutic strategy to normalize dysregulated cellular physiology associated with neuropsychiatric disease. This review summarizes preclinical and clinical studies using mGlu receptor allosteric modulators as experimental tools and potential therapeutic approaches for the treatment of neuropsychiatric diseases, including schizophrenia, stress, and substance use disorders.

Altered Dynamics of S. aureus Phosphofructokinase via Bond Restraints at Two Distinct Allosteric Binding Sites

The effect of perturbation at the allosteric site was investigated through several replicas of molecular dynamics (MD) simulations conducted on bacterial phosphofructokinase (SaPFK). In our previous work, an alternative binding site was estimated to be allosteric in addition to the experimentally reported one. To highlight the effect of both allosteric sites on receptor's dynamics, MD runs were carried out on apo forms with and without perturbation. Perturbation was achieved via incorporating multiple bond restraints for residue pairs located at the allosteric site. Restraints applied to the predicted site caused one dimer to stiffen, whereas an increase in mobility was detected in the same dimer when the experimentally resolved site was restrained. Fluctuations in C_α-C_α distances which is used to disclose residues with high potential of communication indicated a marked increase in signal transmission within each dimer as the receptor switched to a restrained state. Cross-correlation of positional fluctuations indicated an overall decrease in the magnitude of both positive and negative correlations when restraints were employed on the predicted allosteric site whereas an exact opposite effect was observed for the reported site. Finally, mutual correspondence between positional fluctuations noticeably increased with restraints on predicted allosteric site, whereas an opposite effect was observed for restraints applied on experimentally reported one. In view of these findings, it is clear that the perturbation of either one of two allosteric sites effected the dynamics of the receptor with a distinct and contrasting character.

Allostery in the dynamic coactivator domain KIX occurs through minor conformational micro-states

The coactivator KIX of CBP uses two binding surfaces to recognize multiple activators and exhibits allostery in ternary complex formation. Activator•coactivator interactions are central to transcriptional regulation, yet the microscopic origins of allostery in dynamic proteins like KIX are largely unknown. Here, we investigate the molecular recognition and allosteric manifestations involved in two KIX ternary systems c-Myb•KIX•MLL and pKID•KIX•MLL. Exploring the hypothesis that binary complex formation prepays an entropic cost for positive cooperativity, we utilize molecular dynamics simulations, side chain methyl order parameters, and differential scanning fluorimetry (DSF) to explore conformational entropy changes in KIX. The protein's configurational micro-states from structural clustering highlight the utility of protein plasticity in molecular recognition and allostery. We find that apo KIX occupies a wide distribution of lowly-populated configurational states. Each binding partner has its own suite of KIX states that it selects, building a model of molecular recognition fingerprints. Allostery is maximized with MLL pre-binding, which corresponds to the observation of a significant reduction in KIX micro-states observed when MLL binds. With all binding partners, the changes in KIX conformational entropy arise predominantly from changes in the most flexible loop. Likewise, we find that a small molecule and mutations allosterically inhibit/enhance activator binding by tuning loop dynamics, suggesting that loop-targeting chemical probes could be developed to alter KIX•activator interactions. Experimentally capturing KIX stabilization is challenging, particularly because of the disordered nature of particular activators. However, DSF melting curves allow for inference of relative entropic changes that occur across complexes, which we compare to our computed entropy changes using simulation methyl order parameters.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

Folding of VemP into translation-arresting secondary structure is driven by the ribosome exit tunnel

The ribosome is a fundamental biomolecular complex that synthesizes proteins in cells. Nascent proteins emerge from the ribosome through a tunnel, where they may interact with the tunnel walls or small molecules such as antibiotics. These interactions can cause translational arrest with notable physiological consequences. Here, we studied the arrest caused by the regulatory peptide VemP, which is known to form α-helices inside the ribosome tunnel near the peptidyl transferase center under specific conditions. We used all-atom molecular dynamics simulations of the entire ribosome and circular dichroism spectroscopy to study the driving forces of helix formation and how VemP causes the translational arrest. To that aim, we compared VemP dynamics in the ribosome tunnel with its dynamics in solution. We show that the VemP peptide has a low helical propensity in water and that the propensity is higher in mixtures of water and trifluorethanol. We propose that helix formation within the ribosome is driven by the interactions of VemP with the tunnel and that a part of VemP acts as an anchor. This anchor might slow down VemP progression through the tunnel enabling α-helix formation, which causes the elongation arrest.

The Inherent Coupling of Intrinsically Disordered Regions in the Multidomain Receptor Tyrosine Kinase KIT

RTK KIT regulates a variety of crucial cellular processes via its cytoplasmic domain (CD), which is composed of the tyrosine kinase domain, crowned by the highly flexible domains—the juxtamembrane region, kinase insertion domain, and C-tail, which are key recruitment regions for downstream signalling proteins. To prepare a structural basis for the characterization of the interactions of KIT with its signalling proteins (KIT INTERACTOME), we generated the 3D model of the full-length CD attached to the transmembrane helix. This generic model of KIT in inactive state was studied by molecular dynamics simulation under conditions mimicking the natural environment of KIT. With the accurate atomistic description of the multidomain KIT dynamics, we explained its intrinsic (intra-domain) and extrinsic (inter-domain) disorder and represented the conformational assemble of KIT through free energy landscapes. Strongly coupled movements within each domain and between distant domains of KIT prove the functional interdependence of these regions, described as allosteric regulation, a phenomenon widely observed in many proteins. We suggested that KIT, in its inactive state, encodes all properties of the active protein and its post-transduction events.

Allosterism in the PDZ Family

Dynamic allosterism allows the propagation of signal throughout a protein. The PDZ (PSD-95/Dlg1/ZO-1) family has been named as a classic example of dynamic allostery in small modular domains. While the PDZ family consists of more than 200 domains, previous efforts have primarily focused on a few well-studied PDZ domains, including PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ. Taken together, experimental and computational studies have identified regions of these domains that are dynamically coupled to ligand binding. These regions include the αA helix, the αB lower-loop, and the αC helix. In this review, we summarize the specific residues on the αA helix, the αB lower-loop, and the αC helix of PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ that have been identified as participants in dynamic allostery by either experimental or computational approaches. This review can serve as an index for researchers to look back on the previously identified allostery in the PDZ family. Interestingly, our summary of previous work reveals clear consistencies between the domains. While the PDZ family has a low sequence identity, we show that some of the most consistently identified allosteric residues within PTP-BL PDZ2 and PSD-95 PDZ3 domains are evolutionarily conserved. These residues include A46/A347, V61/V362, and L66/L367 on PTP-BL PDZ2 and PSD-95 PDZ3, respectively. Finally, we expose a need for future work to explore dynamic allostery within (1) PDZ domains with multiple binding partners and (2) multidomain constructs containing a PDZ domain.

Uncovering the Dominant Motion Modes of Allosteric Regulation Improves Allosteric Site Prediction

Allostery is an important mechanism that biological systems use to regulate function at a distant site. Allosteric drugs have attracted much attention in recent years due to their high specificity and the possibility of overcoming existing drug-resistant mutations. However, the discovery of allosteric drugs remains challenging as allosteric regulation mechanisms are not clearly understood and allosteric sites cannot be accurately predicted. In this study, we analyzed the dominant modes that determine motion correlations between allosteric and orthosteric sites using the Gaussian network model and found that motion correlations between allosteric and orthosteric sites are dominated by either fast or slow vibrational modes. This dependence of modes results from the relative locations of the two sites and local secondary structures. Based on these analyses, we developed CorrSite2.0 to predict allosteric sites by taking the maximum of the Z-scores calculated from using either slow or fast modes. The prediction accuracy of CorrSite2.0 outperformed other commonly used allosteric site prediction methods with prediction accuracy over 90.0%. Our study uncovers the relationship of protein structure, dynamics, and allosteric regulation and demonstrates that using the dominant motion modes greatly improves allosteric site prediction accuracy. CorrSite2.0 has been integrated into the CavityPlus web server, which can be accessed at http://www.pkumdl.cn/cavityplus. CorrSite2.0 provides a powerful and user-friendly tool for allosteric drug and protein design.

Real-Time Tunable Dynamic Range for Calibration-Free Biomolecular Measurements with a Temperature-Modulated Electrochemical Aptamer-Based Sensor in an Unprocessed Actual Sample

The sensing technologies for monitoring molecular analytes in biological fluids with high frequency and in real time could enable a broad range of applications in personalized healthcare and clinical diagnosis. However, due to the limited dynamic range (less than 81-fold), real-time analysis of biomolecular concentration varying over multiple orders of magnitude is a severe challenge faced by this class of analytical platforms. For the first time, we describe here that temperature-modulated electrochemical aptamer-based sensors with a dynamically adjustable calibration-free detection window could enable continuous, real-time, and accurate response for the several-hundredfold target concentration changes in unprocessed actual samples. Specifically, we could regulate the electrode surface temperature of sensors to obtain the corresponding dynamic range because of the temperature-dependent affinity variations. This temperature modulation method relies on an alternate hot and cold electrode reported by our group, whose surface could actively be heated and cooled without the need for altering ambient temperature, thus likewise applying for the flowing system. We then performed dual-frequency calibration-free measurements at different interface temperatures, thus achieving an extended detection window from 25 to 2500 μM for procaine in undiluted urine, 1-500 μM for adenosine triphosphate, and 5-2000 μM for adenosine in undiluted serum. The resulting sensor architecture could drastically expand the real-time response range accessible to these continuous, reagent-less biosensors.

L-valine production in Corynebacterium glutamicum based on systematic metabolic engineering: progress and prospects

L-valine is an essential branched-chain amino acid that cannot be synthesized by the human body and has a wide range of applications in food, medicine and feed. Market demand has stimulated people's interest in the industrial production of L-valine. At present, the mutagenized or engineered Corynebacterium glutamicum is an effective microbial cell factory for producing L-valine. Because the biosynthetic pathway and metabolic network of L-valine are intricate and strictly regulated by a variety of key enzymes and genes, highly targeted metabolic engineering can no longer meet the demand for efficient biosynthesis of L-valine. In recent years, the development of omics technology has promoted the upgrading of traditional metabolic engineering to systematic metabolic engineering. This whole-cell-scale transformation strategy has become a productive method for developing L-valine producing strains. This review provides an overview of the biosynthesis and regulation mechanism of L-valine, and summarizes the current metabolic engineering techniques and strategies for constructing L-valine high-producing strains. Finally, the opinion of constructing a cell factory for efficiently biosynthesizing L-valine was proposed.

Ready, SET, Go: Post-translational regulation of the histone lysine methylation network in budding yeast

Histone lysine methylation is a key epigenetic modification that regulates eukaryotic transcription. Here, we comprehensively review the function and regulation of the histone methylation network in the budding yeast and model eukaryote, Saccharomyces cerevisiae. First, we outline the lysine methylation sites that are found on histone proteins in yeast (H3K4me1/2/3, H3K36me1/2/3, H3K79me1/2/3, and H4K5/8/12me1) and discuss their biological and cellular roles. Next, we detail the reduced but evolutionarily conserved suite of methyltransferase (Set1p, Set2p, Dot1p, and Set5p) and demethylase (Jhd1p, Jhd2p, Rph1p, and Gis1p) enzymes that are known to control histone lysine methylation in budding yeast cells. Specifically, we illustrate the domain architecture of the methylation enzymes and highlight the structural features that are required for their respective functions and molecular interactions. Finally, we discuss the prevalence of post-translational modifications on yeast histone methylation enzymes and how phosphorylation, acetylation, and ubiquitination in particular are emerging as key regulators of enzyme function. We note that it will be possible to completely connect the histone methylation network to the cell's signaling system, given that all methylation sites and cognate enzymes are known, most phosphosites on the enzymes are known, and the mapping of kinases to phosphosites is tractable owing to the modest set of protein kinases in yeast. Moving forward, we expect that the rich variety of post-translational modifications that decorates the histone methylation machinery will explain many of the unresolved questions surrounding the function and dynamics of this intricate epigenetic network.

Folding and Intrinsic Disorder of the Receptor Tyrosine Kinase KIT Insert Domain Seen by Conventional Molecular Dynamics Simulations

The kinase insert domain (KID) of RTK KIT is the key recruitment region for downstream signalling proteins. KID, studied by molecular dynamics simulations as a cleaved polypeptide and as a native domain fused to KIT, showed intrinsic disorder represented by a set of heterogeneous conformations. The accurate atomistic models showed that the helical fold of KID is mainly sequence dependent. However, the reduced fold of the native KID suggests that its folding is allosterically controlled by the kinase domain. The tertiary structure of KID represents a compact array of highly variable α- and 3₁₀-helices linked by flexible loops playing a principal role in the conformational diversity. The helically folded KID retains a collapsed globule-like shape due to non-covalent interactions associated in a ternary hydrophobic core. The free energy landscapes constructed from first principles-the size, the measure of the average distance between the conformations, the amount of helices and the solvent-accessible surface area-describe the KID disorder through a collection of minima (wells), providing a direct evaluation of conformational ensembles. We found that the cleaved KID simulated with restricted N- and C-ends better reproduces the native KID than the isolated polypeptide. We suggest that a cyclic, generic KID would be best suited for future studies of KID f post-transduction effects.

Is Disrupted Nucleotide-Substrate Cooperativity a Common Trait for Cushing's Syndrome Driving Mutations of Protein Kinase A?

Somatic mutations in the PRKACA gene encoding the catalytic α subunit of protein kinase A (PKA-C) are responsible for cortisol-producing adrenocortical adenomas. These benign neoplasms contribute to the development of Cushing's syndrome. The majority of these mutations occur at the interface between the two lobes of PKA-C and interfere with the enzyme's ability to recognize substrates and regulatory (R) subunits, leading to aberrant phosphorylation patterns and activation. Rarely, patients with similar phenotypes carry an allosteric mutation, E31V, located at the C-terminal end of the αA-helix and adjacent to the αC-helix, but structurally distinct from the PKA-C/R subunit interface mutations. Using a combination of solution NMR, thermodynamics, kinetic assays, and molecular dynamics simulations, we show that the E31V allosteric mutation disrupts central communication nodes between the N- and C- lobes of the enzyme as well as nucleotide-substrate binding cooperativity, a hallmark for kinases' substrate fidelity and regulation. For both orthosteric (L205R and W196R) and allosteric (E31V) Cushing's syndrome mutants, the loss of binding cooperativity is proportional to the density of the intramolecular allosteric network. This structure-activity relationship suggests a possible common mechanism for Cushing's syndrome driving mutations in which decreased nucleotide/substrate binding cooperativity is linked to loss in substrate fidelity and dysfunctional regulation.

Activation-Induced Reorganization of Energy Transport Networks in the β2 Adrenergic Receptor

We compute energy exchange networks (EENs) through the β₂ adrenergic receptor (β₂AR), a G-protein coupled receptor (GPCR), in inactive and active states, based on the results of molecular dynamics simulations of this membrane bound protein. We introduce a new definition for the reorganization of EENs upon activation that depends on the relative change in rates of energy transfer across noncovalent contacts throughout the protein. On the basis of the reorganized network that we obtain for β₂AR upon activation, we identify a branched pathway between the agonist binding site and the cytoplasmic region, where a G-protein binds to the receptor when activated. The pathway includes all of the motifs containing molecular switches previously identified as contributing to the allosteric transition of β₂AR upon agonist binding. EENs and their reorganization upon activation are compared with structure-based contact networks computed for the inactive and active states of β₂AR.

The loops of the N-SH2 binding cleft do not serve as allosteric switch in SHP2 activation

The Src-homology-2 domain-containing phosphatase SHP2 is a critical regulator of signal transduction, being implicated in cell growth and differentiation. Activating mutations cause developmental disorders and act as oncogenic drivers in hematologic cancers. SHP2 is activated by phosphopeptide binding to the N-SH2 domain, triggering the release of N-SH2 from the catalytic PTP domain. Based on early crystallographic data, it has been widely accepted that opening of the binding cleft of N-SH2 serves as the key "allosteric switch" driving SHP2 activation. To test the putative coupling between binding cleft opening and SHP2 activation as assumed by the allosteric switch model, we critically reviewed structural data of SHP2, and we used extensive molecular dynamics (MD) simulation and free energy calculations of isolated N-SH2 in solution, SHP2 in solution, and SHP2 in a crystal environment. Our results demonstrate that the binding cleft in N-SH2 is constitutively flexible and open in solution and that a closed cleft found in certain structures is a consequence of crystal contacts. The degree of opening of the binding cleft has only a negligible effect on the free energy of SHP2 activation. Instead, SHP2 activation is greatly favored by the opening of the central β-sheet of N-SH2. We conclude that opening of the N-SH2 binding cleft is not the key allosteric switch triggering SHP2 activation.

Unraveling the Coupling between Conformational Changes and Ligand Binding in Ribose Binding Protein Using Multiscale Molecular Dynamics and Free-Energy Calculations

Conformational changes of proteins upon ligand binding are usually explained in terms of several mechanisms including the induced fit, conformational selection, or their mixtures. Due to the slow time scales, conventional molecular dynamics (cMD) simulations based on the atomistic models cannot easily simulate the open-to-closed conformational transition in proteins. In our previous study, we have developed an enhanced sampling scheme (generalized replica exchange with solute tempering selected surface charged residues: gREST_SSCR) for multidomain proteins and applied it to ligand-mediated conformational changes in the G134R mutant of ribose-binding protein (RBPG134R) in solution. The free-energy landscape (FEL) of RBPG134R in the presence of a ribose at the binding site included the open and closed states and two intermediates, open-like and closed-like forms. Only the open and open-like forms existed in the FEL without a ribose. In the current study, the coupling between the conformational changes and ligand binding is further investigated using coarse-grained MD, multiple atomistic cMD, and free-energy calculations. The ribose is easily dissociated from the binding site of wild-type RBP and RBPG134R in the cMD simulations starting from the open and open-like forms. In contrast, it is stable at the binding site in the simulations from the closed and closed-like forms. The free-energy calculations provide the binding affinities of different structures, supporting the results of cMD simulations. Importantly, cMD simulations from the closed-like structures reveal transitions toward the closed one in the presence of a bound ribose. On the basis of the computational results, we propose a molecular mechanism in which conformational selection and induced fit happen in the first and second halves of the open-to-closed transition in RBP, respectively.

Conformational Strain Indicated by Ramachandran Angles for the Protein Backbone Is Only Weakly Related to the Flexibility

Studies on energy associated with free dipeptides have shown that conformers with unfavorable (ϕ,ψ) torsion angles have higher energy compared to conformers with favorable (ϕ,ψ) angles. It is expected that higher energy confers higher dynamics and flexibility to that part of the protein. Here, we explore a potential relationship between conformational strain in a residue due to unfavorable (ϕ,ψ) angles and its flexibility and dynamics in the context of protein structures. We compared flexibility of strained and relaxed residues, which are recognized based on outlier/allowed and favorable (ϕ,ψ) angles respectively, using normal-mode analysis (NMA). We also performed in-depth analysis on flexibility and dynamics at catalytic residues in protein kinases, which exhibit different strain status in different kinase structures using NMA and molecular dynamics simulations. We underline that strain of a residue, as defined by backbone torsion angles, is almost unrelated to the flexibility and dynamics associated with it. Even the overall trend observed among all high-resolution structures in which relaxed residues tend to have slightly higher flexibility than strained residues is counterintuitive. Consequently, we propose that identifying strained residues based on (ϕ,ψ) values is not an effective way to recognize energetic strain in protein structures.

Synchronous and Asynchronous Response in Dynamically Perturbed Proteins

We present a dynamic perturbation-response model of proteins based on the Gaussian Network Model, where a residue is perturbed periodically, and the dynamic response of other residues is determined. The model shows that periodic perturbation causes a synchronous response in phase with the perturbation and an asynchronous response that is out of phase. The asynchronous component results from the viscous effects of the solvent and other dispersive factors in the system. The model is based on the solution of the Langevin equation in the presence of solvent, noise, and perturbation. We introduce several novel ideas: The concept of storage and loss compliance of the protein and their dependence on structure and frequency; the amount of work lost and the residues that contribute significantly to the lost work; new dynamic correlations that result from perturbation; causality, that is, the response of j when i is perturbed is not equal to the response of i when j is perturbed. As examples, we study two systems, namely, bovine rhodopsin and the class of nanobodies. The general results obtained are (i) synchronous and asynchronous correlations depend strongly on the frequency of perturbation, their magnitude decreases with increasing frequency, (ii) time-delayed mean-squared fluctuations of residues have only synchronous components. Asynchronicity is present only in cross correlations, that is, correlations between different residues, (iii) perturbation of loop residues leads to a large dissipation of work, (iv) correlations satisfy the hypothesis of pre-existing pathways according to which information transfer by perturbation rides on already existing equilibrium correlations in the system, (v) dynamic perturbation can introduce a selective response in the system, where the perturbation of each residue excites different sets of responding residues, and (vi) it is possible to identify nondissipative residues whose perturbation does not lead to dissipation in the protein. Despite its simplicity, the model explains several features of allosteric manipulation.

Disclosing Allostery Through Protein Contact Networks

Proteins are located in the twilight zone between chemistry and biology, where a peculiar kind of complexity starts. Proteins are the smallest 'devices' showing a sensible adaptation to their environment by the production of appropriate behavior when facing a specific stimulus. This fact qualifies (from the 'effector' side) proteins as nanomachines working as catalysts, motors, or switches. However (from the sensor side), the need to single out the 'specific stimulus' out of thermal noise qualifies proteins as information processing devices. Allostery corresponds to the modification of the configuration (in a broad sense) of the protein molecule in response to a specific stimulus in a non-strictly local way, thereby connecting the sensor and effector sides of the nanomachine. This is why the 'disclosing' of allostery phenomenon is at the very heart of protein function; in this chapter, we will demonstrate how a network-based representation of protein structure in terms of nodes (aminoacid residues) and edges (effective contacts between residues) is the natural language for getting rid of allosteric phenomena and, more in general, of protein structure/function relationships.

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.

Optimizing enzymatic catalysts for rapid turnover of substrates with low enzyme sequestration

Enzymes are central to both metabolism and information processing in cells. In both cases, an enzyme's ability to accelerate a reaction without being consumed in the reaction is crucial. Nevertheless, enzymes are transiently sequestered when they bind to their substrates; this sequestration limits activity and potentially compromises information processing and signal transduction. In this article, we analyse the mechanism of enzyme-substrate catalysis from the perspective of minimizing the load on the enzymes through sequestration, while maintaining at least a minimum reaction flux. In particular, we ask: which binding free energies of the enzyme-substrate and enzyme-product reaction intermediates minimize the fraction of enzymes sequestered in complexes, while sustaining a certain minimal flux? Under reasonable biophysical assumptions, we find that the optimal design will saturate the bound on the minimal flux and reflects a basic trade-off in catalytic operation. If both binding free energies are too high, there is low sequestration, but the effective progress of the reaction is hampered. If both binding free energies are too low, there is high sequestration, and the reaction flux may also be suppressed in extreme cases. The optimal binding free energies are therefore neither too high nor too low, but in fact moderate. Moreover, the optimal difference in substrate and product binding free energies, which contributes to the thermodynamic driving force of the reaction, is in general strongly constrained by the intrinsic free-energy difference between products and reactants. Both the strategies of using a negative binding free-energy difference to drive the catalyst-bound reaction forward and of using a positive binding free-energy difference to enhance detachment of the product are limited in their efficacy.

Re-engineering Electrochemical Aptamer-Based Biosensors to Tune Their Useful Dynamic Range via Distal-Site Mutation and Allosteric Inhibition

Electrochemical aptamer-based (E-AB) sensors, exploiting binding-induced changes in biomolecular conformation, are rapid, specific, and selective and perform well even in a complex matrix, such as directly in whole blood and even in vivo. However, like all sensors employing biomolecular recognitions, E-AB sensors suffer from an inherent limitation of single-site binding, i.e., its fixed dose-response curve. To circumvent this, we employ here distal-site mutation and allosteric inhibition to rationally tune the dynamic range of E-AB sensors, achieving sets of sensors with a significantly varied target affinity (∼3 orders of magnitude). Using their combination, we recreate several approaches to narrow (down to 5-fold) or extend (up to 2000-fold) the dynamic range of biological receptors. The thermodynamic consequences of aptamer-surface interactions are estimated via the free-energy difference in solution-phase and surface-bound biosensors employing the same aptamer as a recognition element, revealing that an allostery strategy provides a more predictable and efficient means to finely control the target affinity and dynamic range. Such an ability to rationally modulate the affinity of biomolecule receptors would open the door to applications including cancer therapy, bioelectronics, and many other fields employing biomolecule recognition.

Selective Modulation of Dynamic Protein Complexes

Dynamic proteins perform critical roles in cellular machines, including those that control proteostasis, transcription, translation, and signaling. Thus, dynamic proteins are prime candidates for chemical probe and drug discovery but difficult targets because they do not conform to classical rules of design and screening. Selectivity is pivotal for candidate probe molecules due to the extensive interaction network of these dynamic hubs. Recognition that the traditional rules of probe discovery are not necessarily applicable to dynamic proteins and their complexes, as well as technological advances in screening, have produced remarkable results in the last 2-4 years. Particularly notable are the improvements in target selectivity for small-molecule modulators of dynamic proteins, especially with techniques that increase the discovery likelihood of allosteric regulatory mechanisms. We focus on approaches to small-molecule screening that appear to be more suitable for highly dynamic targets and have the potential to streamline identification of selective modulators.

Optimizing the Specificity Window of Biomolecular Receptors Using Structure-Switching and Allostery

To ensure maximum specificity (i.e., minimize cross-reactivity with structurally similar analogues of the desired target), most bioassays invoke "stringency", the careful tuning of the conditions employed (e.g., pH, ionic strength, or temperature). Willingness to control assay conditions will fall, however, as quantitative, single-step biosensors begin to replace multistep analytical processes. This is especially true for sensors deployed in vivo, where the tuning of such parameters is not just inconvenient but impossible. In response, we describe here the rational adaptation of two strategies employed by nature to tune the affinity of biomolecular receptors so as to optimize the placement of their specificity "windows" without the need to alter measurement conditions: structure-switching and allosteric control. We quantitatively validate these approaches using two distinct, DNA-based receptors: a simple, linear-chain DNA suitable for detecting a complementary DNA strand and a structurally complex DNA aptamer used for the detection of a small-molecule drug. Using these models, we show that, without altering assay conditions, structure-switching and allostery can tune the concentration range over which a receptor achieves optimal specificity over orders of magnitude, thus optimally matching the specificity window with the range of target concentrations expected to be seen in a given application.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

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.

Tuning Down the Pain - An Overview of Allosteric Modulation of Opioid Receptors: Mechanisms of Modulation, Allosteric Sites, Modulator Syntheses

Opioid signaling plays a central role in pain perception. As such, it remains the main target in the development of antinociceptive agents, despite serious side effects involved. In recent years, hopes for improved opioid painkillers are rising, together with our understanding of allosterism and biased signaling mechanisms. In this review, we focus on recently discovered allosteric modulators of opioid receptors, insights into phenomena underlying their action, as well as on how they extend our understanding of mechanisms of previously known compounds. A brief overlook of their synthesis is also presented.

Allosteric Regulation of DNAzyme Activities through Intrastrand Transformation Induced by Cu(II)-Mediated Artificial Base Pairing

Allosteric regulation is gaining increasing attention as a basis for the production of stimuli-responsive materials in many research areas including DNA nanotechnology. We expected that metal-mediated artificial base pairs, consisting of ligand-type nucleotides and a bridging metal ion, could serve as allosteric units that regulate the function of DNA molecules. In this study, we established a rational design strategy for developing Cu^II-responsive allosteric DNAzymes by incorporating artificial hydroxypyridone ligand-type nucleotides (H) that form a Cu^II-mediated base pair (H-Cu^II-H). We devised a new enzymatic method using a standard DNA polymerase and a ligase to prepare DNA strands containing H nucleotides. Previously reported DNAzymes were modified by introducing a H-H pair into the stem region, and the stem-loop sequences were altered so that the structure becomes catalytically inactive in the absence of Cu^II ions. The formation of a H-Cu^II-H base pair triggers intrastrand transformation from the inactive to the active structure, enabling allosteric regulation of the DNAzyme activity in response to Cu^II ions. The activity of the H-modified DNAzyme was reversibly switched by the addition and removal of Cu^II ions under isothermal conditions. Similarly, by incorporating a H-Cu^II-H pair into an in vitro-selected Ag^I-dependent DNAzyme, we have developed a DNAzyme that exhibits an AND logic-gate response to Cu^II and Ag^I ions. The rational design strategy and the easy enzymatic synthetic method presented here provide a versatile way to develop a variety of metal-responsive allosteric DNA materials, including molecular machines and logic circuits, based on metal-mediated artificial base pairing.

Revisiting allostery in CREB-binding protein (CBP) using residue-based interaction energy

CREB-binding protein (CBP) is a multi-subunit scaffold protein complex in transcription regulation process, binding and interacting with ligands such as mixed-lineage leukemia (MLL) and c-Myb allosterically. Here in this study, we have revisited the concept of allostery in CBP via residue-based interaction energy calculation based on molecular dynamics (MD) simulations. To this end, we conducted MD simulations of KIX:MLL:c-Myb ternary complex, its binary components and kinase-inducible domain (KID) interacting domain (KIX) backbone. Interaction energy profiles and cross correlation analysis were performed and the results indicated that KIX:MLL and KIX:c-Myb:MLL complexes demonstrate significant similarities according to both analysis methods. Two regions in the KIX backbone were apparent from the interaction energy and cross correlation maps that hold a key to allostery phenomena observed in CBP. While one of these regions are related to the ligand binding residues, the other comprises of L₁₂-G₂ loop and α₃ helix regions that have been found to have a significant role in allosteric signal propagation. All in all, residue-based interaction energy calculation method is demonstrated to be a valuable calculation technique for the detection of allosteric signal propagation and ligand interaction regions.

Purifying selection shaping the evolution of the Toll-like receptor 2 TIR domain in brown hares (Lepus europaeus) from Europe and the Middle East

Toll-like receptors (TLRs) are transmembrane proteins of the innate immune system, composed of the ectodomain involved in pathogen recognition and the intracellular Toll/interleukin-1 receptor (TIR) domain important for downstream signal transduction. Here, we analyze the genetic variability of TIR nucleotide and amino-acid sequences of the TLR2 gene in 243 brown hares from Europe and the Middle East and tested for the presence of selection signals and spatial structuring. TLR2 TIR domain sequences were PCR amplified and sequenced, while genotyping was performed by phasing. Genetic diversity indices were calculated in DnaSP and Arlequin, while presence of selection signals was tested using MEGA and the Datamonkey web server. The presence of spatial patterns in TIR sequence distribution was tested by spatial Principal Component Analysis (sPCA) in adegenet. A total of 13 haplotypes were revealed with haplotype diversity of 0.424, and nucleotide diversity (π) of 0.00138. Two spatial clusters were revealed: "Anatolia/Middle East" and "Europe". In Anatolia the two most prevalent amino-acid variants, A and B (the latter being the most ancestral) were maintained at similar frequencies; but in Europe a shift in genotype frequencies was observed as well as a higher number of nonsynonymous substitutions giving rise to novel amino-acid protein variants originating from the evolutionarily younger protein variant. Molecular diversity (haplotype and nucleotide diversity) indices were significantly higher in the "Anatolia/Middle East" cluster. A signal of purifying selection was detected acting on the TIR sequences.

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.

NMR and computational methods for molecular resolution of allosteric pathways in enzyme complexes

Allostery is a ubiquitous biological mechanism in which a distant binding site is coupled to and drastically alters the function of a catalytic site in a protein. Allostery provides a high level of spatial and temporal control of the integrity and activity of biomolecular assembles composed of proteins, nucleic acids, or small molecules. Understanding the physical forces that drive allosteric coupling is critical to harnessing this process for use in bioengineering, de novo protein design, and drug discovery. Current microscopic models of allostery highlight the importance of energetics, structural rearrangements, and conformational fluctuations, and in this review, we discuss the synergistic use of solution NMR spectroscopy and computational methods to probe these phenomena in allosteric systems, particularly protein-nucleic acid complexes. This combination of experimental and theoretical techniques facilitates an unparalleled detection of subtle changes to structural and dynamic equilibria in biomolecules with atomic resolution, and we provide a detailed discussion of specialized NMR experiments as well as the complementary methods that provide valuable insight into allosteric pathways in silico. Lastly, we highlight two case studies to demonstrate the adaptability of this approach to enzymes of varying size and mechanistic complexity.

Does Ras Activate Raf and PI3K Allosterically?

The mechanism through which oncogenic Ras activates its effectors is vastly important to resolve. If allostery is at play, then targeting allosteric pathways could help in quelling activation of MAPK (Raf/MEK/ERK) and PI3K (PI3K/Akt/mTOR) cell proliferation pathways. On the face of it, allosteric activation is reasonable: Ras binding perturbs the conformational ensembles of its effectors. Here, however, we suggest that at least for Raf, PI3K, and NORE1A (RASSF5), that is unlikely. Raf's long disordered linker dampens effective allosteric activation. Instead, we suggest that the high-affinity Ras–Raf binding relieves Raf's autoinhibition, shifting Raf's ensemble from the inactive to the nanocluster-mediated dimerized active state, as Ras also does for NORE1A. PI3K is recruited and allosterically activated by RTK (e.g., EGFR) at the membrane. Ras restrains PI3K's distribution and active site orientation. It stabilizes and facilitates PIP₂ binding at the active site and increases the PI3K residence time at the membrane. Thus, RTKs allosterically activate PI3Kα; however, merging their action with Ras accomplishes full activation. Here we review their activation mechanisms in this light and draw attention to implications for their pharmacology.

Oligomeric Receptor Complexes and Their Allosteric Receptor-Receptor Interactions in the Plasma Membrane Represent a New Biological Principle for Integration of Signals in the CNS

G protein-coupled receptors (GPCRs) not only exist as monomers but also as homomers and heteromers in which allosteric receptor-receptor interactions take place, modulating the functions of the participating GPCR protomers. GPCRs can also form heteroreceptor complexes with ionotropic receptors and receptor tyrosine kinases modulating their function. Furthermore, adaptor proteins interact with receptor protomers and modulate their interactions. The state of the art is that the allosteric receptor-receptor interactions are reciprocal, highly dynamic and substantially alter the signaling, trafficking, recognition and pharmacology of the participating protomers. The pattern of changes appears to be unique for each heteromer and can favor antagonistic or facilitatory interactions or switch the G protein coupling from e.g., Gi/o to Gq or to beta-arrestin signaling. It lends a new dimension to molecular integration in the nervous system. Future direction should be aimed at determining the receptor interface involving building models of selected heterodimers. This will make design of interface-interfering peptides that specifically disrupt the heterodimer possible. This will help to determine the functional role of the allosteric receptor-receptor interactions as well as the integration of signals at the plasma membrane by the heteroreceptor complexes, vs. integration of the intracellular signaling pathways. Integration of signals also at the plasma membrane seems crucial in view of the hypothesis that learning and memory at a molecular level takes place by reorganization of homo and heteroreceptor complexes in the postsynaptic membrane. Homo and heteroreceptor complexes are in balance with each other, and their disbalance is linked to disease. Targeting heteroreceptor complexes represents a novel strategy for the treatment of brain disorders.