Dynamically driven protein allostery

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

The high-fidelity (HF1), hyper-accurate (Hypa), and evolved (Evo) variants of the CRISPR-associated protein 9 (Cas9) endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in recognition subdomain 3 (Rec3) mediate specificity in these variants are poorly understood. Here, solution nuclear magnetic resonance and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic His-Asn-His (HNH) domain. The variants remodel the communication sourcing from the Rec3 α helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Allosteric communication and signal transduction in proteins

Allostery is one of the cornerstones of biological function, as it plays a fundamental role in regulating protein activity. The modelling of allostery has gradually moved from a conformation-based framework, linked to structural changes, to dynamics-based allostery, whereby the effects of ligand binding propagate via signal transduction from the allosteric site to other regions of the protein via inter-residue interactions. Characterising such allosteric signalling pathways, which do not necessarily lead to conformational changes, has been pursued experimentally and complemented by computational analysis of protein networks to detect subtle dynamic propagation paths. Considering allostery from the perspective of signal transduction broadens the understanding of allosteric mechanisms, underscores the importance of protein topology, and can provide insights into allosteric drug design.

Gauging Dynamics-driven Allostery Using a New Computational Tool: A CAP Case Study

In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP's entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.

Computational analysis of long-range allosteric communications in CFTR

Malfunction of the CFTR protein results in cystic fibrosis, one of the most common hereditary diseases. CFTR functions as an anion channel, the gating of which is controlled by long-range allosteric communications. Allostery also has direct bearings on CF treatment: the most effective CFTR drugs modulate its activity allosterically. Herein, we integrated Gaussian network model, transfer entropy, and anisotropic normal mode-Langevin dynamics and investigated the allosteric communications network of CFTR. The results are in remarkable agreement with experimental observations and mutational analysis and provide extensive novel insight. We identified residues that serve as pivotal allosteric sources and transducers, many of which correspond to disease-causing mutations. We find that in the ATP-free form, dynamic fluctuations of the residues that comprise the ATP-binding sites facilitate the initial binding of the nucleotide. Subsequent binding of ATP then brings to the fore and focuses on dynamic fluctuations that were present in a latent and diffuse form in the absence of ATP. We demonstrate that drugs that potentiate CFTR's conductance do so not by directly acting on the gating residues, but rather by mimicking the allosteric signal sent by the ATP-binding sites. We have also uncovered a previously undiscovered allosteric 'hotspot' located proximal to the docking site of the phosphorylated regulatory (R) domain, thereby establishing a molecular foundation for its phosphorylation-dependent excitatory role. This study unveils the molecular underpinnings of allosteric connectivity within CFTR and highlights a novel allosteric 'hotspot' that could serve as a promising target for the development of novel therapeutic interventions.

Genetic switching by the Lac repressor is based on two-state Monod-Wyman-Changeux allostery

High-resolution NMR spectroscopy enabled us to characterize allosteric transitions between various functional states of the dimeric Escherichia coli Lac repressor. In the absence of ligands, the dimer exists in a dynamic equilibrium between DNA-bound and inducer-bound conformations. Binding of either effector shifts this equilibrium toward either bound state. Analysis of the ternary complex between repressor, operator DNA, and inducer shows how adding the inducer results in allosteric changes that disrupt the interdomain contacts between the inducer binding and DNA binding domains and how this in turn leads to destabilization of the hinge helices and release of the Lac repressor from the operator. Based on our data, the allosteric mechanism of the induction process is in full agreement with the well-known Monod-Wyman-Changeux model.

The Onset of Molecule-Spanning Dynamics in Heat Shock Protein Hsp90

Protein dynamics have been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how fast (local) fluctuations can lead to slow global (allosteric) changes. Here, fast molecule-spanning dynamics on the 100 to 200 ns time scale in the heat shock protein 90 (Hsp90) are shown. Global real-space movements are assigned to dynamic modes on this time scale, which is possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom molecular dynamics (MD) simulations. The time scale of these dynamic modes depends on the conformational state of the Hsp90 dimer. In addition, the dynamic modes are affected to various degrees by Sba1, a co-chaperone of Hsp90, depending on the location within Hsp90, which is in very good agreement with MD simulations. Altogether, this data is best described by fast molecule-spanning dynamics, which precede larger conformational changes in Hsp90 and might be the molecular basis for allostery. This integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein.

Advances of Predicting Allosteric Mechanisms Through Protein Contact in New Technologies and Their Application

Allostery is an intriguing phenomenon wherein the binding activity of a biological macromolecule is modulated via non-canonical binding site, resulting in synchronized functional changes. The mechanics underlying allostery are relatively complex and this review is focused on common methodologies used to study allostery, such as X-ray crystallography, NMR spectroscopy, and HDXMS. Different methodological approaches are used to generate data in different scenarios. For example, X-ray crystallography provides high-resolution structural information, NMR spectroscopy offers dynamic insights into allosteric interactions in solution, and HDXMS provides information on protein dynamics. The residue transition state (RTS) approach has emerged as a critical tool in understanding the energetics and conformational changes associated with allosteric regulation. Allostery has significant implications in drug discovery, gene transcription, disease diagnosis, and enzyme catalysis. Enzymes' catalytic activity can be modulated by allosteric regulation, offering opportunities to develop novel therapeutic alternatives. Understanding allosteric mechanisms associated with infectious organisms like SARS-CoV and bacterial pathogens can aid in the development of new antiviral drugs and antibiotics. Allosteric mechanisms are crucial in the regulation of a variety of signal transduction and cell metabolism pathways, which in turn govern various cellular processes. Despite progress, challenges remain in identifying allosteric sites and characterizing their contribution to a variety of biological processes. Increased understanding of these mechanisms can help develop allosteric systems specifically designed to modulate key biological mechanisms, providing novel opportunities for the development of targeted therapeutics. Therefore, the current review aims to summarize common methodologies that are used to further our understanding of allosteric mechanisms. In conclusion, this review provides insights into the methodologies used for the study of allostery, its applications in in silico modeling, the mechanisms underlying antibody allostery, and the ongoing challenges and prospects in advancing our comprehension of this intriguing phenomenon.

Protein Kinase Structure and Dynamics: Role of the αC-β4 Loop

Although the αC-β4 loop is a stable feature of all protein kinases, the importance of this motif as a conserved element of secondary structure, as well as its links to the hydrophobic architecture of the kinase core, has been underappreciated. We first review the motif and then describe how it is linked to the hydrophobic spine architecture of the kinase core, which we first discovered using a computational tool, Local Spatial Pattern (LSP) alignment. Based on NMR predictions that a mutation in this motif abolishes the synergistic high-affinity binding of ATP and a pseudo substrate inhibitor, we used LSP to interrogate the F100A mutant. This comparison highlights the importance of the αC-β4 loop and key residues at the interface between the N- and C-lobes. In addition, we delved more deeply into the structure of the apo C-subunit, which lacks ATP. While apo C-subunit showed no significant changes in backbone dynamics of the αC-β4 loop, we found significant differences in the side chain dynamics of K105. The LSP analysis suggests disruption of communication between the N- and C-lobes in the F100A mutant, which would be consistent with the structural changes predicted by the NMR spectroscopy.

Temperature as a modulator of allosteric motions and crosstalk in mesophilic and thermophilic enzymes

Mesophilic and thermophilic enzyme counterparts are often studied to understand how proteins function under harsh conditions. To function well outside of standard temperature ranges, thermophiles often tightly regulate their structural ensemble through intra-protein communication (via allostery) and altered interactions with ligands. It has also become apparent in recent years that the enhancement or diminution of allosteric crosstalk can be temperature-dependent and distinguish thermophilic enzymes from their mesophilic paralogs. Since most studies of allostery utilize chemical modifications from pH, mutations, or ligands, the impact of temperature on allosteric function is comparatively understudied. Here, we discuss the biophysical methods, as well as critical case studies, that dissect temperature-dependent function of mesophilic-thermophilic enzyme pairs and their allosteric regulation across a range of temperatures.

Kinetic and thermodynamic allostery in the Ras protein family

Allostery, the transfer of information between distant parts of a macromolecule, is a fundamental feature of protein function and regulation. However, allosteric mechanisms are usually not explained by protein structure, requiring information on correlated fluctuations uniquely accessible to molecular simulation. Existing work to extract allosteric pathways from molecular dynamics simulations has focused on thermodynamic correlations. Here, we show how kinetic correlations encode complementary information essential to explain observed variations in allosteric regulation. We applied kinetic and thermodynamic correlation analysis on atomistic simulations of H, K, and NRas isoforms in the apo, GTP, and GDP-bound states of Ras protein, with and without complexing to its downstream effector, Raf. We show that switch I and switch II are the primary components of thermodynamic and kinetic allosteric networks, consistent with the key roles of these two motifs. These networks connect the switches to an allosteric loop recently discovered from a crystal structure of HRas. This allosteric loop is inactive in KRas, but is coupled to the hydrolysis arm switch II in NRas and HRas. We find that the mechanism in the latter two isoforms are thermodynamic and kinetic, respectively. Binding of Raf-RBD further activates thermodynamic allostery in HRas and KRas but has limited effect on NRas. These results indicate that kinetic and thermodynamic correlations are both needed to explain protein function and allostery. These two distinct channels of allosteric regulation, and their combinatorial variability, may explain how subtle mutational differences can lead to diverse regulatory profiles among enzymatic proteins.

High-Fidelity, Hyper-Accurate, and Evolved Mutants Rewire Atomic Level Communication in CRISPR-Cas9

The Cas9-HF1, HypaCas9, and evoCas9 variants of the Cas9 endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in the Rec3 domain mediate specificity in these variants are poorly understood. Here, solution NMR and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and peculiar dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic HNH domain. The variants remodel the communication sourcing from the Rec3 α-helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.

Allosteric Determinants in High Temperature Requirement A Enzymes Are Conserved and Regulate the Population of Active Conformations

High temperature requirement A (HtrA) are allosterically regulated enzymes wherein effector binding to the PDZ domain triggers proteolytic activity. Yet, it remains unclear if the inter-residue network governing allostery is conserved across HtrA enzymes. Here, we investigated and identified the inter-residue interaction networks by molecular dynamics simulations on representative HtrA proteases, Escherichia coli DegS and Mycobacterium tuberculosis PepD, in effector-bound and free forms. This information was used to engineer mutations that could potentially perturb allostery and conformational sampling in a different homologue, M. tuberculosis HtrA. Mutations in HtrA perturbed allosteric regulation─a finding consistent with the hypothesis that the inter-residue interaction network is conserved across HtrA enzymes. Electron density from data collected on cryo-protected HtrA crystals revealed that mutations altered the topology of the active site. Ensemble models fitted into electron density calculated from room-temperature diffraction data showed that only a fraction of these models had a catalytically competent active site conformation alongside a functional oxyanion hole thus providing experimental evidence that these mutations influenced conformational sampling. Mutations at analogous positions in the catalytic domain of DegS perturbed the coupling between effector binding and proteolytic activity, thus confirming the role of these residues in the allosteric response. The finding that a perturbation in the conserved inter-residue network alters conformational sampling and the allosteric response suggests that an ensemble allosteric model best describes regulated proteolysis in HtrA enzymes.

Carbon Monoxide-Sensing Transcription Factors: Regulators of Microbial Carbon Monoxide Oxidation Pathway Gene Expression

Carbon monoxide (CO) serves as a source of energy and carbon for a diverse set of microbes found in anaerobic and aerobic environments. The enzymes that bacteria and archaea use to oxidize CO depend upon complex metallocofactors that require accessory proteins for assembly and proper function. This complexity comes at a high energetic cost and necessitates strict regulation of CO metabolic pathways in facultative CO metabolizers to ensure that gene expression occurs only when CO concentrations and redox conditions are appropriate. In this review, we examine two known heme-dependent transcription factors, CooA and RcoM, that regulate inducible CO metabolism pathways in anaerobic and aerobic microorganisms. We provide an analysis of the known physiological and genomic contexts of these sensors and employ this analysis to contextualize known biochemical properties. In addition, we describe a growing list of putative transcription factors associated with CO metabolism that potentially use cofactors other than heme to sense CO.

Perspective: How Fast Dynamics Affect Slow Function in Protein Machines

Internal motions in proteins take place on a broad range of time- and space-scales. The potential roles of these dynamics in the biochemical functions of proteins have intrigued biophysicists for many years, and multiple mechanisms to couple motions to function have been proposed. Some of these mechanisms have relied on equilibrium concepts. For example, the modulation of dynamics was proposed to change the entropy of a protein, hence affecting processes such as binding. This so-called dynamic allostery scenario has been demonstrated in several recent experiments. Perhaps even more intriguing may be models that involve out-of-equilibrium operation, which by necessity require the input of energy. We discuss several recent experimental studies that expose such potential mechanisms for coupling dynamics and function. In Brownian ratchets, for example, directional motion is promoted by switching a protein between two free energy surfaces. An additional example involves the effect of microsecond domain-closure dynamics of an enzyme on its much slower chemical cycle. These observations lead us to propose a novel two-time-scale paradigm for the activity of protein machines: fast equilibrium fluctuations take place on the microsecond-millisecond time scale, while on a slower time scale, free energy is invested in order to push the system out of equilibrium and drive functional transitions. Motions on the two time scales affect each other and are essential for the overall function of these machines.

Theoretical investigation of functional responses of bio-molecular assembly networks

Cooperative protein-protein and protein-DNA interactions form programmable complex assemblies, often performing non-linear gene regulatory operations involved in signal transductions and cell fate determination. The apparent structure of those complex assemblies is very similar, but their functional response strongly depends on the topology of the protein-DNA interaction networks. Here, we demonstrate how the coordinated self-assembly creates gene regulatory network motifs that corroborate the existence of a precise functional response at the molecular level using thermodynamic and dynamic analyses. Our theoretical and Monte Carlo simulations show that a complex network of interactions can form a decision-making loop, such as feedback and feed-forward circuits, only by a few molecular mechanisms. We characterize each possible network of interactions by systematic variations of free energy parameters associated with the binding among biomolecules and DNA looping. We also find that the higher-order networks exhibit alternative steady states from the stochastic dynamics of each network. We capture this signature by calculating stochastic potentials and attributing their multi-stability features. We validate our findings against the Gal promoter system in yeast cells. Overall, we show that the network topology is vital in phenotype diversity in regulatory circuits.

Predicting allosteric pockets in protein biological assemblages

MOTIVATION

Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages.

RESULTS

Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein's global dynamics.

AVAILABILITY AND IMPLEMENTATION

APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Biochemical methods to map and quantify allosteric motions in human glucokinase

Allosteric regulation of protein function is ubiquitous in biology. Allostery originates from ligand-mediated alterations in polypeptide structure and/or dynamics, which produce a cooperative kinetic or thermodynamic response to changing ligand concentrations. Establishing a mechanistic description of individual allosteric events requires both mapping the relevant changes in protein structure and quantifying the rates of differential conformational dynamics in the absence and presence of effectors. In this chapter, we describe three biochemical approaches to understand the dynamic and structural signatures of protein allostery using the well-established cooperative enzyme glucokinase as a case study. The combined application of pulsed proteolysis, biomolecular nuclear magnetic resonance spectroscopy and hydrogen-deuterium exchange mass spectrometry offers complementary information that can used to establish molecular models for allosteric proteins, especially when differential protein dynamics are involved.

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

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

The "violin model": Looking at community networks for dynamic allostery

Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a "violin model" for allostery as a contemporary method for approaching the Cooper-Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.

Analysis of coordinated NMR chemical shifts to map allosteric regulatory networks in proteins

The exquisite sensitivity of the NMR chemical shift to local environment makes it an ideal probe to assess atomic level perturbations in proteins of all sizes and structural compositions. Recent advances in solution and solid-state NMR spectroscopy of biomolecules have leveraged the chemical shift to report on short- and long-range couplings between individual amino acids to establish "networks" of residues that form the basis of allosteric pathways that transmit chemical signals through the protein matrix to induce functional responses. The simple premise that thermodynamically and functionally coupled regions of a protein (i.e. active and allosteric sites) should be reciprocally sensitive to structural or dynamic perturbations has enabled NMR spectroscopy, the premier method for molecular resolution of protein structural fluctuations, to occupy a place at the forefront of investigations into protein allostery. Here, we detail several key methods of NMR chemical shift analysis to extract mechanistic information about long-range chemical signaling in a protein, focusing on practical methodological aspects and the circumstances under which a given approach would be relevant. We also detail some of the experimental considerations that should be made when applying these methods to specific protein systems.

Protein dynamics: The future is bright and complicated!

Biological life depends on motion, and this manifests itself in proteins that display motion over a formidable range of time scales spanning from femtoseconds vibrations of atoms at enzymatic transition states, all the way to slow domain motions occurring on micro to milliseconds. An outstanding challenge in contemporary biophysics and structural biology is a quantitative understanding of the linkages among protein structure, dynamics, and function. These linkages are becoming increasingly explorable due to conceptual and methodological advances. In this Perspective article, we will point toward future directions of the field of protein dynamics with an emphasis on enzymes. Research questions in the field are becoming increasingly complex such as the mechanistic understanding of high-order interaction networks in allosteric signal propagation through a protein matrix, or the connection between local and collective motions. In analogy to the solution to the "protein folding problem," we argue that the way forward to understanding these and other important questions lies in the successful integration of experiment and computation, while utilizing the present rapid expansion of sequence and structure space. Looking forward, the future is bright, and we are in a period where we are on the doorstep to, at least in part, comprehend the importance of dynamics for biological function.

Binding and Functional Folding (BFF): A Physiological Framework for Studying Biomolecular Interactions and Allostery

EF-hand Ca2+-binding proteins (CBPs), such as S100 proteins (S100s) and calmodulin (CaM), are signaling proteins that undergo conformational changes upon increasing intracellular Ca2+. Upon binding Ca2+, S100 proteins and CaM interact with protein targets and induce important biological responses. The Ca2+-binding affinity of CaM and most S100s in the absence of target is weak (CaKD > 1 μM). However, upon effector protein binding, the Ca2+ affinity of these proteins increases via heterotropic allostery (CaKD < 1 μM). Because of the high number and micromolar concentrations of EF-hand CBPs in a cell, at any given time, allostery is required physiologically, allowing for (i) proper Ca2+ homeostasis and (ii) strict maintenance of Ca2+-signaling within a narrow dynamic range of free Ca2+ ion concentrations, [Ca2+]free. In this review, mechanisms of allostery are coalesced into an empirical "binding and functional folding (BFF)" physiological framework. At the molecular level, folding (F), binding and folding (BF), and BFF events include all atoms in the biomolecular complex under study. The BFF framework is introduced with two straightforward BFF types for proteins (type 1, concerted; type 2, stepwise) and considers how homologous and nonhomologous amino acid residues of CBPs and their effector protein(s) evolved to provide allosteric tightening of Ca2+ and simultaneously determine how specific and relatively promiscuous CBP-target complexes form as both are needed for proper cellular function.

Conformational interdomain flexibility in a bacterial α-isopropylmalate synthase is necessary for leucine biosynthesis

α-Isopropylmalate synthase (IPMS) catalyzes the first step in leucine (Leu) biosynthesis and is allosterically regulated by the pathway end product, Leu. IPMS is a dimeric enzyme with each chain consisting of catalytic, accessory, and regulatory domains, with the accessory and regulatory domains of each chain sitting adjacent to the catalytic domain of the other chain. The IPMS crystal structure shows significant asymmetry because of different relative domain conformations in each chain. Owing to the challenges posed by the dynamic and asymmetric structures of IPMS enzymes, the molecular details of their catalytic and allosteric mechanisms are not fully understood. In this study, we have investigated the allosteric feedback mechanism of the IPMS enzyme from the bacterium that causes meningitis, Neisseria meningitidis (NmeIPMS). By combining molecular dynamics simulations with small-angle X-ray scattering, mutagenesis, and heterodimer generation, we demonstrate that Leu-bound NmeIPMS is in a rigid conformational state stabilized by asymmetric interdomain polar interactions. Furthermore, we found removing these polar interactions by mutagenesis impaired the allosteric response without compromising Leu binding. Our results suggest that the allosteric inhibition of NmeIPMS is achieved by restricting the flexibility of the accessory and regulatory domains, demonstrating that significant conformational flexibility is required for catalysis.

Allosteric rescue of catalytically impaired ATP phosphoribosyltransferase variants links protein dynamics to active-site electrostatic preorganisation

ATP phosphoribosyltransferase catalyses the first step of histidine biosynthesis and is controlled via a complex allosteric mechanism where the regulatory protein HisZ enhances catalysis by the catalytic protein HisG_S while mediating allosteric inhibition by histidine. Activation by HisZ was proposed to position HisG_S Arg56 to stabilise departure of the pyrophosphate leaving group. Here we report active-site mutants of HisG_S with impaired reaction chemistry which can be allosterically restored by HisZ despite the HisZ:HisG_S interface lying ~20 Å away from the active site. MD simulations indicate HisZ binding constrains the dynamics of HisG_S to favour a preorganised active site where both Arg56 and Arg32 are poised to stabilise leaving-group departure in WT-HisG_S. In the Arg56Ala-HisG_S mutant, HisZ modulates Arg32 dynamics so that it can partially compensate for the absence of Arg56. These results illustrate how remote protein-protein interactions translate into catalytic resilience by restoring damaged electrostatic preorganisation at the active site.

Energy Transport in Class B GPCRs: Role of Protein-Water Dynamics and Activation

We compute energy exchange networks (EENs) through glucagon-like peptide-1 receptor (GLP-1R), a class B G-protein-coupled receptor (GPCR), in inactive and two active states, one activated by a peptide ligand and the other by a small molecule agonist, from results of molecular dynamics simulations. The reorganized network upon activation contains contributions from structural as well as from dynamic changes and corresponding entropic contributions to the free energy of activation, which are estimated in terms of the change in rates of energy transfer across non-covalent contacts. The role of water in the EENs and in activation of GLP-1R is also investigated. The dynamics of water in contact with the central polar network of the transmembrane region is found to be significantly slower for both activated states compared to the inactive state. This result is consistent with the contribution of water molecules to activation of GLP-1R previously suggested and resembles water dynamics in parts of the transmembrane region found in earlier studies of rhodopsin-like GPCRs.

Solvent paramagnetic relaxation enhancement as a versatile method for studying structure and dynamics of biomolecular systems

Solvent paramagnetic relaxation enhancement (sPRE) is a versatile nuclear magnetic resonance (NMR)-based method that allows characterization of the structure and dynamics of biomolecular systems through providing quantitative experimental information on solvent accessibility of NMR-active nuclei. Addition of soluble paramagnetic probes to the solution of a biomolecule leads to paramagnetic relaxation enhancement in a concentration-dependent manner. Here we review recent progress in the sPRE-based characterization of structural and dynamic properties of biomolecules and their complexes, and aim to deliver a comprehensive illustration of a growing number of applications of the method to various biological systems. We discuss the physical principles of sPRE measurements and provide an overview of available co-solute paramagnetic probes. We then explore how sPRE, in combination with complementary biophysical techniques, can further advance biomolecular structure determination, identification of interaction surfaces within protein complexes, and probing of conformational changes and low-population transient states, as well as deliver insights into weak, nonspecific, and transient interactions between proteins and co-solutes. In addition, we present examples of how the incorporation of solvent paramagnetic probes can improve the sensitivity of NMR experiments and discuss the prospects of applying sPRE to NMR metabolomics, drug discovery, and the study of intrinsically disordered proteins.

Dynamic allostery in substrate binding by human thymidylate synthase

Human thymidylate synthase (hTS) is essential for DNA replication and therefore a therapeutic target for cancer. Effective targeting requires knowledge of the mechanism(s) of regulation of this 72 kDa homodimeric enzyme. Here, we investigate the mechanism of binding cooperativity of the nucleotide substrate. We have employed exquisitely sensitive methyl-based CPMG and CEST NMR experiments enabling us to identify residues undergoing bifurcated linear 3-state exchange, including concerted switching between active and inactive conformations in the apo enzyme. The inactive state is populated to only ~1.3%, indicating that conformational selection contributes negligibly to the cooperativity. Instead, methyl rotation axis order parameters, determined by ²H transverse relaxation rates, suggest that rigidification of the enzyme upon substrate binding is responsible for the entropically-driven cooperativity. Lack of the rigidification in product binding and substrate binding to an N-terminally truncated enzyme, both non-cooperative, support this idea. In addition, the lack of this rigidification in the N-terminal truncation indicates that interactions between the flexible N-terminus and the rest of the protein, which are perturbed by substrate binding, play a significant role in the cooperativity—a novel mechanism of dynamic allostery. Together, these findings yield a rare depth of insight into the substrate binding cooperativity of an essential enzyme.

IGF-dependent dynamic modulation of a protease cleavage site in the intrinsically disordered linker domain of human IGFBP2

Functional regulation via conformational dynamics is well known in structured proteins but less well characterized in intrinsically disordered proteins and their complexes. Using NMR spectroscopy, we have identified a dynamic regulatory mechanism in the human insulin-like growth factor (IGF) system involving the central, intrinsically disordered linker domain of human IGF-binding protein-2 (hIGFBP2). The bioavailability of IGFs is regulated by the proteolysis of IGF-binding proteins. In the case of hIGFBP2, the linker domain (L-hIGFBP2) retains its intrinsic disorder upon binding IGF-1, but its dynamics are significantly altered, both in the IGF binding region and distantly located protease cleavage sites. The increase in flexibility of the linker domain upon IGF-1 binding may explain the IGF-dependent modulation of proteolysis of IGFBP2 in this domain. As IGF homeostasis is important for cell growth and function, and its dysregulation is a key contributor to several cancers, our findings open up new avenues for the design of IGFBP analogs inhibiting IGF-dependent tumors.

Observation of conformational changes that underlie the catalytic cycle of Xrn2

Nuclear magnetic resonance (NMR) methods that quantitatively probe motions on molecular and atomic levels have propelled the understanding of biomolecular processes for which static structures cannot provide a satisfactory description. In this work, we studied the structure and dynamics of the essential 100-kDa eukaryotic 5′→3′ exoribonuclease Xrn2. A combination of complementary fluorine and methyl-TROSY NMR spectroscopy reveals that the apo enzyme is highly dynamic around the catalytic center. These observed dynamics are in agreement with a transition of the enzyme from the ground state into a catalytically competent state. We show that the conformational equilibrium in Xrn2 shifts substantially toward the active state in the presence of substrate and magnesium. Finally, our data reveal that the dynamics in Xrn2 correlate with the RNA degradation rate, as a mutation that attenuates motions also affects catalytic activity. In that light, our results stress the importance of studies that go beyond static structural information.

Using methyl group and fluorine NMR spectroscopic methods, Overbeck et al revealed that the dynamics of the eukaryotic 5′→3′ exoribonuclease Xrn2 in the region around the active site are correlated with its catalytic activity.

Phenol sensing in nature is modulated via a conformational switch governed by dynamic allostery

The NtrC family of proteins senses external stimuli and accordingly stimulates stress and virulence pathways via activation of associated σ⁵⁴-dependent RNA polymerases. However, the structural determinants that mediate this activation are not well understood. Here, we establish using computational, structural, biochemical, and biophysical studies that MopR, an NtrC protein, harbors a dynamic bidirectional electrostatic network that connects the phenol pocket to two distal regions, namely the “G-hinge” and the “allosteric linker.” While the G-hinge influences the entry of phenol into the pocket, the allosteric linker passes the signal to the downstream ATPase domain. We show that phenol binding induces a rewiring of the electrostatic connections by eliciting dynamic allostery and demonstrates that perturbation of the core relay residues results in a complete loss of ATPase stimulation. Furthermore, we found a mutation of the G-hinge, ∼20 Å from the phenol pocket, promotes altered flexibility by shifting the pattern of conformational states accessed, leading to a protein with 7-fold enhanced phenol binding ability and enhanced transcriptional activation. Finally, we conducted a global analysis that illustrates that dynamic allostery-driven conserved community networks are universal and evolutionarily conserved across species. Taken together, these results provide insights into the mechanisms of dynamic allostery-mediated conformational changes in NtrC sensor proteins.

Optimising Elastic Network Models for Protein Dynamics and Allostery: Spatial and Modal Cut-offs and Backbone Stiffness

The family of coarse-grained models for protein dynamics known as Elastic Network Models (ENMs) require careful choice of parameters to represent well experimental measurements or fully-atomistic simulations. The most basic ENM that represents each protein residue by a node at the position of its C-alpha atom, all connected by springs of equal stiffness, up to a cut-off in distance. Even at this level a choice is required of the optimum cut-off distance and the upper limit of elastic normal modes taken in any sum for physical properties, such as dynamic correlation or allosteric effects on binding. Additionally, backbone-enhanced ENM (BENM) may improve the model by allocating a higher stiffness to springs that connect along the protein backbone. This work reports on the effect of varying these three parameters (distance and mode cutoffs, backbone stiffness) on the dynamical structure of three proteins, Catabolite Activator Protein (CAP), Glutathione S-transferase (GST), and the SARS-CoV-2 Main Protease (M pro ). Our main results are: (1) balancing B-factor and dispersion-relation predictions, a near-universal optimal value of 8.5 Å is advisable for ENMs; (2) inhomogeneity in elasticity brings the first mode containing spatial structure not well-resolved by the ENM typically within the first 20; (3) the BENM only affects modes in the upper third of the distribution, and, additionally to the ENM, is only able to model the dispersion curve better in this vicinity; (4) BENM does not typically affect fluctuation-allostery, which also requires careful treatment of the effector binding to the host protein to capture.

Prediction of Allosteric Communication Pathways in Proteins

MOTIVATION

Allostery in proteins is an essential phenomenon in biological processes. In this paper, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form.

RESULTS

Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large Bcl-xL, Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase DHFR, HRas GTPase, and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or preexistence of some other functional states. Our model is computationally fast and simple, and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Size-Dependent Interplay of Volume Exclusion Versus Soft Interactions: Cytochrome c in Macromolecular Crowded Environment

Even though there are a great number of possible conformational states, how a protein generated as a linear unfolded polypeptide efficiently folds into its physiologically active form remained a fascinating and unanswered enigma inside crowded conditions of cells. In this study, various spectroscopic techniques have been exploited to know and understand the effect and mechanism of action of two different sizes of polyethylene glycols, or PEGs (molecular mass ∼10 and ∼20 kilo Daltons, kDa), on cytochrome c (cyt c). The outcomes showed that small size of the PEG leads to perturbation of the protein structure, and conversely, large size of the PEG has stabilizing effect on cyt c. Moreover, binding measurements showed that small size of PEG interacts strongly via soft interactions compared to the larger size of PEG, the latter being governed more by excluded volume effect or preferential exclusion from the protein. Overall, this finding suggests that conformations of protein may be influenced in cellular crowded conditions via interactions which depend upon the size of molecule in the environment. This study proposes that both volume exclusion and soft (chemical) interactions governs the protein’s conformation and functional activities. The cellular environment’s internal architecture as evident from crowder size and shape in this study has a significant role.

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.

Quantitative Analysis of Dynamic Allostery

Dynamic allostery refers to one important class of allosteric regulation that does not involve noticeable conformational changes upon effector binding. In recent years, many "quasi"-dynamic allosteric proteins have been found to only experience subtle conformational changes during allosteric regulation. However, as enthalpic and entropic contributions are coupled to each other and even tiny conformational changes could bring in noticeable free energy changes, a quantitative description is essential to understand the contribution of pure dynamic allostery. Here, by developing a unified anisotropic elastic network model (uANM) considering both side-chain information and ligand heavy atoms, we quantitatively estimated the contribution of pure dynamic allostery in a dataset of known allosteric proteins by excluding the conformational changes upon ligand binding. We found that the contribution of pure dynamic allostery is generally small (much weaker than previously expected) and robustly exhibits an allosteric activation effect, which exponentially decays with the distance between the substrate and the allosteric ligand. We further constructed toy models to study the determinant factors of dynamic allostery in monomeric and oligomeric proteins using the uANM. Analysis of the toy models revealed that a short distance, a small angle between the two ligands, strong protein-ligand interactions, and weak protein internal interactions lead to strong dynamic allostery. Our study provides a quantitative estimation of pure dynamic allostery and facilitates the understanding of dynamic-allostery-controlled biological processes and the design of allosteric drugs and proteins.

Structural Bioinformatic Survey of Protein-Small Molecule Interfaces Delineates the Role of Glycine in Surface Pocket Formation

With a structural bioinformatic approach, we have explored amino acid compositions at PISA defined interfaces between small molecules and proteins that are contained in an optimized subset of 11,351 PDB files. The use of a series of restrictions, to prevent redundancy and biases from interactions between amino acids with charged side chains and ions, yielded a final data set of 45,230 protein-small molecule interfaces. We have compared occurrences of natural amino acids in surface exposed regions and binding sites for all the proteins of our data set. From our structural bioinformatic survey, the most relevant signal arose from the unexpected Gly abundance at enzyme catalytic sites. This finding suggested that Gly must have a fundamental role in stabilizing concave protein surface moieties. Subsequently, we have tried to predict the effect of in silico Gly mutations in hen egg white lysozyme to optimize those conditions that can reshape the protein surface with the appearance of new pockets. Replacing amino acids having bulky side chains with Gly in specific protein regions seems a feasible way for designing proteins with additional surface pockets, which can alter protein surface dynamics, therefore, representing controllable switches for protein activity.

Allosteric pluripotency: challenges and opportunities

Allosteric pluripotency arises when the functional response of an allosteric receptor to an allosteric stimulus depends on additional allosteric modulators. Here, we discuss allosteric pluripotency as observed in the prototypical Protein Kinase A (PKA) as well as in other signaling systems, from typical multidomain signaling proteins to bacterial enzymes. We identify key drivers of pluripotent allostery and illustrate how hypothesizing allosteric pluripotency may solve apparent discrepancies currently present in the literature regarding the dual nature of known allosteric modulators. We also outline the implications of allosteric pluripotency for cellular signaling and allosteric drug design, and analyze the challenges and opportunities opened by the pluripotent nature of allostery.

SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet (“Structure ENSEmble NETworks”) framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology.

Inherent conformational plasticity in dsRBDs enables interaction with topologically distinct RNAs

Many double-stranded RNA-binding domains (dsRBDs) interact with topologically distinct dsRNAs in biological pathways pivotal to viral replication, cancer causation, neurodegeneration, and so on. We hypothesized that the adaptability of dsRBDs is essential to target different dsRNA substrates. A model dsRBD and a few dsRNAs, slightly different in shape from each other, were used to test the systematic shape dependence of RNA on the dsRBD-binding using nuclear magnetic resonance (NMR) spectroscopy and molecular modeling. NMR-based titrations showed a distinct binding pattern for the dsRBD with the topologically distinct dsRNAs. The line broadening upon RNA binding was observed to cluster in the residues lying in close proximity, thereby suggesting an RNA-induced conformational exchange in the dsRBD. Further, while the intrinsic microsecond dynamics observed in the apo-dsRBD were found to quench upon binding with the dsRNA, the microsecond dynamics got induced at residues spatially proximal to quench sites upon binding with the dsRNA. This apparent relay of conformational exchange suggests the significance of intrinsic dynamics to help adapt the dsRBD to target various dsRNA-shapes. The conformational pool visualized in MD simulations for the apo-dsRBD reported here has also been observed to sample the conformations seen previously for various dsRBDs in apo- and in dsRNA-bound state structures, further suggesting the conformational adaptability of the dsRBDs. These investigations provide a dynamic basis for the substrate promiscuity for dsRBD proteins.

Dissecting the Protein Dynamics Coupled Ligand Binding with Kinetic Models and Single-Molecule FRET

Protein-ligand interactions are crucial to many biological processes. Ligand binding and dissociation are the basic steps that allow proteins to function. Protein conformational dynamics have been shown to play important roles in ligand binding and dissociation. However, it is challenging to determine the ligand binding kinetics of dynamic proteins. Here, we undertook comprehensive single-molecule FRET (smFRET) measurements and kinetic model analysis to characterize the conformational dynamics coupled ligand binding of glutamine-binding protein (GlnBP). We showed that hinge and T118A mutations of GlnBP affect its conformational dynamics as well as the ligand binding affinity. Based on smFRET measurements, the kinetic model of ligand-GlnBP interactions was constructed. Using experimentally measured parameters, we solved the rate equations of the model and obtained the undetectable parameters of the model which allowed us to understand the ligand binding kinetics fully. Our results demonstrate that modulation of the conformational dynamics of GlnBP affects the ligand binding and dissociation rates. This study provides insights into the binding kinetics of ligands, which are related to the protein function itself.

Altered cyclic nucleotide-binding and pore opening in a diseased human HCN4 channel

A growing number of nonsynonymous mutations in the human HCN4 channel gene, the major component of the funny channel of the sinoatrial node, are associated with disease but how they impact channel structure and function, and, thus, how they result in disease, is not clear for any of them. Here, we study the S672R mutation, in the cyclic nucleotide-binding domain of the channel, which has been associated with an inherited bradycardia in an Italian family. This may be the best studied of all known mutations, yet the underlying molecular and atomistic mechanisms remain unclear and controversial. We combine measurements of binding by isothermal titration calorimetry to a naturally occurring tetramer of the HCN4 C-terminal region with a mathematical model to show that weaker binding of cAMP to the mutant channel contributes to a lower level of facilitation of channel opening at submicromolar ligand concentrations but that, in general, facilitation occurs over a range that is similar between the mutant and wild-type because of enhanced opening of the mutant channel when liganded. We also show that the binding affinity for cGMP, which produces the same maximum facilitation of HCN4 opening as cAMP, is weaker in the mutant HCN4 channel but that, for both wild-type and mutant, high-affinity binding of cGMP occurs in a range of concentrations below 1 μM. Thus, binding of cGMP to the HCN4 channel may be relevant normally in vivo and reduced binding of cGMP, as well as cAMP, to the mutant channel may contribute to the reduced resting heart rate observed in the affected family.

Thermodynamic and biophysical study of fatty acid effector binding to soybean lipoxygenase: implications for allostery driven by helix α2 dynamics

Previous comparative kinetic isotope effects have inferred an allosteric site for fatty acids and their derivatives that modulates substrate selectivity in 15-lipoxygenases. Hydrogen-deuterium exchange also previously revealed regionally defined enhanced protein flexibility, centred at helix α2 - a gate to the substrate entrance. Direct evidence for allosteric binding and a complete understanding of its mechanism remains elusive. In this study, we examine the binding thermodynamics of the fatty acid mimic, oleyl sulfate (OS), with the monomeric model plant 15-LOX, soybean lipoxygenase (SLO), using isothermal titration calorimetry. Dynamic light scattering and differential scanning calorimetry rule out OS-induced oligomerization or structural changes. These data provide evidence that the fatty acid allosteric regulation of SLO is controlled by the dynamics of helix α2.