Controlling Allosteric Networks in Proteins

Disulfide Reduction Allosterically Destabilizes the β-Ladder Subdomain Assembly within the NS1 Dimer of ZIKV

The Zika virus (ZIKV) was responsible for a recent debilitating epidemic that till date has no cure. A potential way to reduce ZIKV virulence is to limit the action of the nonstructural proteins involved in its viral replication. One such protein, NS1, encoded as a monomer by the viral genome, plays a major role via symmetric oligomerization. We examine the homodimeric structure of the dominant β-ladder segment of NS1 with extensive all atom molecular dynamics. We find it stably bounded by two spatially separated interaction clusters (C1 and C2) with significant differences in the nature of their interactions. Four pairs of distal, intramonomeric disulfide bonds are found to be coupled to the stability, local structure, and wettability of the interfacial region. Symmetric reduction of the intramonomeric disulfides triggers marked dynamical heterogeneity, interfacial wettability, and asymmetric salt-bridging propensity. Harnessing the model-free Lipari-Szabo based formalism for estimation of conformational entropy (S_conf), we find clear signatures of heterogeneity in the monomeric conformational entropies. The observed asymmetry, very small in the unperturbed state, expands significantly in the reduced states. This allosteric effect is most noticeable in the electrostatically bound C2 cluster that underlies the greatest stability in the unperturbed state. Allosteric induction of conformational and thermodynamic asymmetry is expected to affect the pathways leading to symmetric higher-ordered oligomerization, and thereby affect crucial replication pathways.

Mapping allosteric communications within individual proteins

Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Regulation of Thrombin Activity with Ultrasmall Nanoparticles: Effects of Surface Chemistry

Nanomaterials displaying well-tailored sizes and surface chemistries can provide novel ways with which to modulate the structure and function of enzymes. Recently, we showed that gold nanoparticles (AuNPs) in the ultrasmall size regime could perform as allosteric effectors inducing partial inhibition of thrombin activity. We now find that the nature of the AuNP surface chemistry controls the interactions to the anion-binding exosites 1 and 2 on the surface of thrombin, the allosterically induced changes to the active-site conformation, and, by extension, the enzymatic activity. Ultrasmall AuNPs passivated with p-mercaptobenzoic acid ligands (AuMBA) and a peptide-based (Ac-ECYN) biomimetic coat (AuECYN) were utilized in our investigations. Remarkably, we found that while AuMBA binds to exosites 1 and 2, AuECYN interacts primarily with exosite 2. It was further established that AuMBA behaves as a "mild denaturant" of thrombin leading to catalytic dysfunction over time. Conversely, AuECYN resembles a proper allosteric effector leading to partial and reversible inhibition of the activity. Collectively, our findings reveal how the distinct binding modes of different AuNP types may uniquely influence thrombin structure and catalysis. The present study further contributes to our understanding of how synthetic nanomaterials could be exploited in the allosteric regulation of enzymes.

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.

Experimentally-driven protein structure modeling

Revolutions in natural and exact sciences started at the dawn of last century have led to the explosion of theoretical, experimental, and computational approaches to determine structures of molecules, complexes, as well as their rich conformational dynamics. Since different experimental methods produce information that is attributed to specific time and length scales, corresponding computational methods have to be tailored to these scales and experiments. These methods can be then combined and integrated in scales, hence producing a fuller picture of molecular structure and motion from the "puzzle pieces" offered by various experiments. Here, we describe a number of computational approaches to utilize experimental data to glance into structure of proteins and understand their dynamics. We will also discuss the limitations and the resolution of the constraints-based modeling approaches. SIGNIFICANCE: Experimentally-driven computational structure modeling and determination is a rapidly evolving alternative to traditional approaches for molecular structure determination. These new hybrid experimental-computational approaches are proving to be a powerful microscope to glance into the structural features of intrinsically or partially disordered proteins, dynamics of molecules and complexes. In this review, we describe various approaches in the field of experimentally-driven computational structure modeling.

Peptide-enabled receptor-binding-quantum dots for enhanced detection and migration inhibition of cancer cells

We report the efforts to construct active targeting quantum dots using receptor-binding peptide for enhanced detection and migration inhibition of cancer cells. Peptide E5 has specific binding with chemokine receptor 4 (CXCR4), which is a transmembrane G-coupled receptor involved in the metastasis of various types of cancers. E5 was introduced to the surface of CdSe/ZnS quantum dots via biotin-streptavidin interactions. The constructed CXCR4-targeting quantum dots (E5@QDs) was observed to display improved detection sensitivity and significantly enhanced binding affinity for CXCR4 over-expressed cancer cells, and the ability to inhibit cancer cells migration induced by CXCL12.

Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Direct coupling analysis of epistasis in allosteric materials

In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such allosteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find in our model the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well the cost of point mutations but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in consistence with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects.

Allostery in proteins is the property of highly specific responses to ligand binding at a distant site. To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations. In this work we consider allosteric architectures artificially evolved to optimize the cooperativity of binding at allosteric and active site. We first characterize the emergent pattern of epistasis as well as the underlying mechanical phenomena, finding the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range. The numerical evolution of these allosteric architectures allows us to benchmark Direct Coupling Analysis, a method which relies on co-evolution in sequence data to infer direct evolutionary couplings, in connection to allostery. We show that Direct Coupling Analysis predicts quantitatively point mutation costs but underestimates strong long-range epistasis. We provide an argument, based on a simplified model, illustrating the reasons for this discrepancy. Our analysis suggests neural networks as more promising tool to measure epistasis.

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.

Allosteric Motions of the CRISPR-Cas9 HNH Nuclease Probed by NMR and Molecular Dynamics

CRISPR-Cas9 is a widely employed genome-editing tool with functionality reliant on the ability of the Cas9 endonuclease to introduce site-specific breaks in double-stranded DNA. In this system, an intriguing allosteric communication has been suggested to control its DNA cleavage activity through flexibility of the catalytic HNH domain. Here, solution NMR experiments and a novel Gaussian-accelerated molecular dynamics (GaMD) simulation method are used to capture the structural and dynamic determinants of allosteric signaling within the HNH domain. We reveal the existence of a millisecond time scale dynamic pathway that spans HNH from the region interfacing the adjacent RuvC nuclease and propagates up to the DNA recognition lobe in full-length CRISPR-Cas9. These findings reveal a potential route of signal transduction within the CRISPR-Cas9 HNH nuclease, advancing our understanding of the allosteric pathway of activation. Further, considering the role of allosteric signaling in the specificity of CRISPR-Cas9, this work poses the mechanistic basis for novel engineering efforts aimed at improving its genome-editing capability.

Insulated conjugated bimetallopolymer with sigmoidal response by dual self-controlling system as a biomimetic material

Biological systems are known to spontaneously adjust the functioning of neurotransmitters, ion channels, and the immune system, being promoted or regulated through allosteric effects or inhibitors, affording non-linear responses to external stimuli. Here we report that an insulated conjugated bimetallopolymer, in which Ru(II) and Pt(II) complexes are mutually connected with insulated conjugations, exhibits phosphorescence in response to CO gas. The net profile corresponds to a sigmoidal response with a dual self-controlling system, where drastic changes were exhibited at two threshold concentrations. The first threshold for activation of the system is triggered by the depolymerization of the non-radiative conjugated polymer to luminescent monomers, while the second one for regulation is triggered by the switch in the rate-determining step of the Ru complex. Such a molecular design with cooperative multiple transition metals would provide routes for the development of higher-ordered artificial molecular systems bearing bioinspired responses with autonomous modulation.

pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations

The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances-a measure of the extent of percolation on perturbation-and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers.

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.

Energy Transport Pathways in Proteins: A Non-equilibrium Molecular Dynamics Simulation Study

To facilitate the observation of biomolecular energy transport in real time and with single-residue resolution, recent experiments by Baumann et al. ( Angew. Chem. Int. Ed. 2019 , 58 , 2899 , DOI: 10.1002/anie.201812995 ) have used unnatural amino acids β-(1-azulenyl)alanine (Azu) and azidohomoalanine (Aha) to site-specifically inject and probe vibrational energy in proteins. To aid the interpretation of such experiments, non-equilibrium molecular dynamics simulations of the anisotropic energy flow in proteins TrpZip2 and PDZ3 domains are presented. On this account, an efficient simulation protocol is established that accurately mimics the excitation and probing steps of Azu and Aha. The simulations quantitatively reproduce the experimentally found cooling times of the solvated proteins at room temperature and predict that the cooling slows by a factor 2 below the glass temperature of water. In PDZ3, vibrational energy is shown to travel from the initially excited peptide ligand via a complex network of inter-residue contacts and backbone transport to distal regions of the protein. The supposed connection of these energy transport pathways with pathways of allosteric communication is discussed.

Computationally Guided Design of Single-Chain Variable Fragment Improves Specificity of Chimeric Antigen Receptors

Chimeric antigen receptor (CAR)-T cell-based immunotherapy of malignant disease relies on the specificity and association constant of single-chain variable fragments (scFvs). The latter are synthesized from parent antibodies by fusing their light (V_L) and heavy (V_H)-chain variable domains into a single chain using a flexible linker peptide. The fusion of V_L and V_H domains can distort their relative orientation, thereby compromising specificity and association constant of scFv, and reducing the lytic efficacy of CAR-T cells. Here, we circumvent the complications of domains' fusion by designing scFv mutants that stabilize interaction between scFv and its target, thereby rescuing scFv efficacy. We employ an iterative approach, based on structural modeling and mutagenesis driven by computational protein design. To demonstrate the power of this approach, we use the scFv derived from an antibody specific to a human leukocyte antigen A2 (HLA-A2)-HER2-derived peptide complex. Whereas the parental antibody is highly specific to its target, the scFv showed reduced specificity. Using our approach, we design mutations into scFvs that restore specificity of the original antibody.

Ligand-Binding-Site Structure Shapes Allosteric Signal Transduction and the Evolution of Allostery in Protein Complexes

The structure of ligand-binding sites has been shown to profoundly influence the evolution of function in homomeric protein complexes. Complexes with multichain binding sites (MBSs) have more conserved quaternary structure, more similar binding sites and ligands between homologs, and evolve new functions slower than homomers with single-chain binding sites (SBSs). Here, using in silico analyses of protein dynamics, we investigate whether ligand-binding-site structure shapes allosteric signal transduction pathways, and whether the structural similarity of binding sites influences the evolution of allostery. Our analyses show that: 1) allostery is more frequent among MBS complexes than in SBS complexes, particularly in homomers; 2) in MBS homomers, semirigid communities and critical residues frequently connect interfaces and thus they are characterized by signal transduction pathways that cross protein-protein interfaces, whereas SBS homomers usually not; 3) ligand binding alters community structure differently in MBS and SBS homomers; and 4) except MBS homomers, allosteric proteins are more likely to have homologs with similar binding site than nonallosteric proteins, suggesting that binding site similarity is an important factor driving the evolution of allostery.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Inter-Active Site Communication Mediated by the Dimer Interface β-Sheet in the Half-the-Sites Enzyme, Thymidylate Synthase

Thymidylate synthase (TS) is a dimeric enzyme conserved in all life forms that exhibits the allosteric feature of half-the-sites activity. Neither the reason for nor the mechanism of this phenomenon is understood. We used a combined nuclear magnetic resonance (NMR) and molecular dynamics approach to study a stable intermediate preceding hydride transfer, which is the rate-limiting and half-the-sites step. In NMR titrations with ligands leading to this intermediate, we measured chemical shifts of the apoenzyme (lig₀), the saturated holoenzyme (lig₂), and the typically elusive singly bound (lig₁) states. Approximately 40 amides showed quartet patterns providing direct NMR evidence of coupling between the active site and probes >30 Å away in the distal subunit. Quartet peak patterns have symmetrical character, indicating reciprocity in communicating the first and second binding events to the distal protomer. Quartets include key catalytic residues and map to the dimer interface β-sheet, which also represents the shortest path between the two active sites. Simulations corroborate the coupling observed in solution in that there is excellent overlap between quartet residues and main-chain atoms having intersubunit cross-correlated motions. Simulations identify five hot spot residues, three of which lie at the kink in the unique β-bulge abutting the active sites on either end of the sheet. Interstrand cross-correlated motions become more organized and pronounced as the enzyme progresses from lig₀ to lig₁ and ultimately lig₂. Coupling in the apparently symmetrical complex has implications for half-the-sites reactivity and potentially resolves the paradox of inequivalent TS active sites despite the vast majority of X-ray structures appearing to be symmetrical.

DynOmics: dynamics of structural proteome and beyond

DynOmics (dynomics.pitt.edu) is a portal developed to leverage rapidly growing structural proteomics data by efficiently and accurately evaluating the dynamics of structurally resolved systems, from individual molecules to large complexes and assemblies, in the context of their physiological environment. At the core of the portal is a newly developed server, ENM 1.0, which permits users to efficiently generate information on the collective dynamics of any structure in PDB format, user-uploaded or database-retrieved. ENM 1.0 integrates two widely used elastic network models (ENMs)—the Gaussian Network Model (GNM) and the Anisotropic Network Model (ANM), extended to take account of molecular environment. It enables users to assess potentially functional sites, signal transduction or allosteric communication mechanisms, and protein–protein and protein–DNA interaction poses, in addition to delivering ensembles of accessible conformers reconstructed at atomic details based on the global modes of motions predicted by the ANM. The ‘environment’ is defined in a flexible manner, from lipid bilayer and crystal contacts, to substrate or ligands bound to a protein, or surrounding subunits in a multimeric structure or assembly. User-friendly interactive features permit users to easily visualize how the environment alter the intrinsic dynamics of the query systems. ENM 1.0 can be accessed at http://enm.pitt.edu/ or http://dyn.life.nthu.edu.tw/oENM/.

Coordinated Network Changes across the Catalytic Cycle of Alpha Tryptophan Synthase

Networks of noncovalent interactions are important for protein structural dynamics. We used nuclear magnetic resonance chemical shift covariance analyses on an inactive variant of the alpha subunit of tryptophan synthase to map amino acid interaction networks across its catalytic cycle. Although some network connections were common to every enzyme state, many of the network connections strengthened or weakened over the catalytic cycle; these changes were highly coordinated. These results suggest a higher level of network organization. Our analyses identified periodic, second-order networks that show highly coordinated interaction changes across the catalytic cycle. These periodic networks may help synchronize the sequence of structural transitions necessary for enzyme function. Molecular dynamics simulations identified interaction changes across the catalytic cycle, including those involving the catalytic residue Glu49, which may help drive other interaction changes throughout the enzyme structure. Similar periodic networks may direct structural transitions and allosteric interactions in other proteins.

A functional substitution in the L-aromatic amino acid decarboxylase enzyme worsens somatic symptoms via a serotonergic pathway

OBJECTIVE

Heightened somatic symptoms are reported by a wide range of patients with chronic pain and have been associated with emotional distress and physical dysfunction. Despite their clinical significance, molecular mechanisms leading to their manifestation are not understood.

METHODS

We used an association study design based on a curated list of 3,295 single nucleotide polymorphisms mapped to 358 genes to test somatic symptoms reporting using the Pennebaker Inventory of Limbic Languidness questionnaire from a case-control cohort of orofacial pain (n = 1,607). A replication meta-analysis of 3 independent cohorts (n = 3,189) was followed by functional validation, including in silico molecular dynamics, in vitro enzyme assays, and measures of serotonin (5-HT) plasma concentration.

RESULTS

An association with the T allele of rs11575542 coding for an arginine to glutamine substitution in the L-aromatic amino acid decarboxylase (AADC) enzyme was replicated in a meta-analysis of 3 independent cohorts. In a combined meta-analysis of all cohorts, this association reached p = 6.43 × 10^-8 . In silico studies demonstrated that this substitution dramatically reduces the conformational dynamics of AADC, potentially lowering its binding capacity to a cofactor. in vitro enzymatic assays showed that this substitution reduces the maximum kinetic velocity of AADC, hence lowering 5-HT levels. Finally, plasma samples from 90 subjects showed correlation between low 5-HT levels and heightened somatic symptoms.

INTERPRETATION

Using functional genomics approaches, we identified a polymorphism in the AADC enzyme that contributes to somatic symptoms through reduced levels of 5-HT. Our findings suggest a molecular mechanism underlying the pathophysiology of somatic symptoms and opens up new treatment options targeting the serotonergic system. ANN NEUROL 2019;86:168-180.

Uncovered Dynamic Coupling Resolves the Ambiguous Mechanism of Phenylalanine Hydroxylase Oxygen Binding

Phenylalanine hydroxylase (PAH) is an iron enzyme catalyzing the oxidation of l-Phe to l-Tyr during phenylalanine catabolism. Dysfunction of PAH leads to the debilitating condition phenylketonuria (PKU), which prompted research into the structure and function of PAH over the last 50 years. Despite intensive study, there is no consensus on the atomistic details of the mechanism of O₂ binding and splitting by wild-type (WT) PAH and how it varies with PKU-inducing mutations, Arg158Gln and Glu280Lys. We studied structures involved in a proposed mechanism for the WT and mutants using extensive mixed quantum-classical molecular dynamics simulations. Simulations reveal a previously unobserved dynamic coupling between the active site and the mutation sites, suggesting how they can affect the catalytic performance of PAH. Furthermore, the effect of the coupling on the PAH structure agrees with and expands our understanding of the experimentally observed differences in activity between the WT and mutants.

Allostery in Its Many Disguises: From Theory to Applications

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

Chokepoints in Mechanical Coupling Associated with Allosteric Proteins: The Pyruvate Kinase Example

Although the critical role of allostery in controlling enzymatic processes is well appreciated, there is a current dearth in our understanding of its underlying mechanisms, including communication between binding sites. One potential key aspect of intersite communication is the mechanical coupling between residues in a protein. Here, we introduce a graph-based computational approach to investigate the mechanical coupling between distant parts of a protein, highlighting effective pathways via which protein motion can transfer energy between sites. In this method, each residue is treated as a node on a weighted, undirected graph, in which the edges are defined by locally correlated motions of those residues and weighted by the strength of the correlation. The method was validated against experimental data on allosteric regulation in the human liver pyruvate kinase as obtained from full-protein alanine-scanning mutagenesis (systematic mutation) studies, as well as computational data on two G-protein-coupled receptors. The method provides semiquantitative information on the regulatory importance of specific structural elements. It is shown that these elements are key for the mechanical coupling between distant parts of the protein by providing effective pathways for energy transfer. It is also shown that, although there are a multitude of energy transfer pathways between distant parts of a protein, these pathways share a few common nodes that represent effective "chokepoints" for the communication.

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

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

Photoswitchable affinity reagents: Computational design and efficient red-light switching

Photo-controlled affinity reagents seek to provide modular spatiotemporal control of bioactivity by conferring photo-switchability of function on an affinity reagent scaffold. Here we used Rosetta-based computational methods to screen for sites on the Fynomer affinity reagent structure for attachment of photoswitchable cross-linkers. Both established UV-based cross-linkers (azobenzene-iodoacetamide (IAC)) and an azonium-based efficient red light switchable cross-linker, piperazino-tetra-ortho-methoxy azobenzene (PIP), were then tested experimentally. Several sites compatible with Fynomer function were identified, including sites showing rapid (<10s) red light (633 nm) modulation of function. While a range of overall target binding affinities were observed, the degree of photo-switchability of Fynomer function was generally small (<2-fold). Computational models suggest that local flexibility limits the degree of switching seen in these designs.

Toward Comprehensive Allosteric Control over Protein Activity

Universality of allosteric signaling in proteins, molecular machines, and receptors complemented by the great advantages of prospected allosteric drugs in the highly specific, non-competitive, and modulatory nature of their actions calls for deeper theoretical understanding of allosteric communication. We present a computational model that makes it possible to tackle the problem of modulating the energetics of protein allosteric communication. In the context of the energy landscape paradigm, allosteric signaling is always a result of perturbations, such as ligand binding, mutations, and intermolecular interactions. The calculation of local partition functions in the protein harmonic model with perturbations allows us to evaluate the energetics of allosteric communication at the single-residue level. In this framework, Allosteric Signaling Maps are proposed as a tool to exhaustively describe allosteric communication in the protein, to tune already existing signaling, and to design new elements of regulation for taking the protein activity under allosteric control.

Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach

Trypanosoma cruzi is the causative agent of Chagas disease, a neglected infection affecting millions of people in tropical regions. There are several chemotherapeutic agents for the treatment of this disease, but most of them are highly toxic and generate resistance. Currently, the development of allosteric inhibitors constitutes a promising research field, since it can improve the accessibility to more selective and less toxic medicines. To date, the allosteric drugs prediction is a state-of-the-art topic in rational structure-based computational design. In this work, a simulation strategy was developed for computational discovery of allosteric inhibitors, and it was applied to cruzain, a promising target and the major cysteine protease of T. cruzi. Molecular dynamics simulations, binding free energy calculations and network-based modelling of residue interactions were combined to characterize and compare molecular distinctive features of the apo form and the cruzain-allosteric inhibitor complexes. By using geometry-based criteria on trajectory snapshots, we predicted two main allosteric sites suitable for drug targeting. The results suggest dissimilar mechanisms exerted by the same allosteric site when binding different potential allosteric inhibitors. Finally, we identified the residues involved in suboptimal paths linking the identified site and the orthosteric site. The present study constitutes the first approximation to the design of cruzain allosteric inhibitors and may serve for future pharmacological intervention. Here, no major effects on active site structure were observed due to compound binding (modification of distance and angles between catalytic residues), which indicates that allosteric regulation in cruzain might be mediated via alterations of its dynamical properties similarly to allosteric inhibition of human cathepsin K (HCatK). The current findings are particularly relevant for the design of allosteric modulators of papain-like cysteine proteases.

Identifying coupled clusters of allostery participants through chemical shift perturbations

Allosteric couplings underlie many cellular signaling processes and provide an exciting avenue for development of new diagnostics and therapeutics. A general method for identifying important residues in allosteric mechanisms would be very useful, but remains elusive due to the complexity of long-range phenomena. Here, we introduce an NMR method to identify residues involved in allosteric coupling between two ligand-binding sites in a protein, which we call chemical shift detection of allostery participants (CAP). Networks of functional groups responding to each ligand are defined through correlated NMR perturbations. In this process, we also identify allostery participants, groups that respond to both binding events and likely play a role in the coupling between the binding sites. Such residues exhibit multiple functional states with distinct NMR chemical shifts, depending on binding status at both binding sites. Such a strategy was applied to the prototypical ion channel KcsA. We had previously shown that the potassium affinity at the extracellular selectivity filter is strongly dependent on proton binding at the intracellular pH sensor. Here, we analyzed proton and potassium binding networks and identified groups that depend on both proton and potassium binding (allostery participants). These groups are viewed as candidates for transmitting information between functional units. The vital role of one such identified amino acid was validated through site-specific mutagenesis, electrophysiology functional studies, and NMR-detected thermodynamic analysis of allosteric coupling. This strategy for identifying allostery participants is likely to have applications for many other systems.

Fluctuation correlations as major determinants of structure- and dynamics-driven allosteric effects

Both structure- and dynamics-driven allosteric effects are determined by the correlation of distance fluctuations in proteins.

Progress in Allosteric Database

An allosteric mechanism refers to the biological regulation process wherein macromolecules propagate the effect of ligand binding at one site to a spatially distant orthosteric locus, thus affecting activity. The theory has remained a trending topic in biology research for over 50 years, since the understanding of allostery is fundamental for gleaning numerous biological processes and developing new drug therapies. In the past two decades, the allosteric paradigm has evolved into more descriptive models, with ever-expanding amounts of experimental data pertaining to newly identified allosteric molecules. The AlloSteric Database (ASD, accessible at http://mdl.shsmu.edu.cn/ASD ), which is a comprehensive knowledge repository, has provided the public with integrated information encompassing allosteric proteins, modulators, sites, pathways, and networks to investigate allostery since 2009. In this chapter, we introduce the history and usage of the ASD and give attention to specific applications that have benefited from the ASD.

Allosteric inhibition of α-thrombin enzymatic activity with ultrasmall gold nanoparticles

The catalytic activity of enzymes can be regulated by interactions with synthetic nanoparticles (NPs) in a number of ways. To date, however, the potential use of NPs as allosteric effectors has not been investigated in detail. Importantly, targeting allosteric (distal) sites on the enzyme surface could afford unique ways to modulate the activity, allowing for either enzyme activation, partial or full inhibition. Using p-mercaptobenzoic acid-coated ultrasmall gold NPs (AuMBA) and human α-thrombin as a model system, here we experimentally tested the hypothesis that enzyme activity could be regulated through ultrasmall NP interactions at allosteric sites. We show that AuMBA interacted selectively and reversibly around two positively charged regions of the thrombin surface (exosites 1 and 2) and away from the active site. NP complexation at the exosites transmitted long-range structural changes over to the active site, altering both substrate binding affinity and catalysis. Significantly, thrombin activity was partially reduced - but not completely inhibited - by interactions with AuMBA. These findings indicate that interactions of proteins with ultrasmall NPs may mimic a typical biomolecular complexation event, and suggest the prospect of using ultrasmall particles as synthetic receptors to allosterically regulate protein function.

Millisecond Timescale Motions Connect Amino Acid Interaction Networks in Alpha Tryptophan Synthase

Tryptophan synthase is a model system for understanding allosteric regulation within enzyme complexes. Amino acid interaction networks were previously delineated in the isolated alpha subunit (αTS) in the absence of the beta subunit (βTS). The amino acid interaction networks were different between the ligand-free enzyme and the enzyme actively catalyzing turnover. Previous X-ray crystallography studies indicated only minor localized changes when ligands bind αTS, and so, structural changes alone could not explain the changes to the amino acid interaction networks. We hypothesized that the network changes could instead be related to changes in conformational dynamics. As such, we conducted nuclear magnetic resonance relaxation studies on different substrate- and products-bound complexes of αTS. Specifically, we collected ¹⁵N R₂ relaxation dispersion data that reports on microsecond-to-millisecond timescale motion of backbone amide groups. These experiments indicated that there are conformational exchange events throughout αTS. Substrate and product binding change specific motional pathways throughout the enzyme, and these pathways connect the previously identified network residues. These pathways reach the αTS/βTS binding interface, suggesting that the identified dynamic networks may also be important for communication with the βTS subunit.

Exploring allosteric communication in multiple states of the bacterial ribosome using residue network analysis

Antibiotic resistance is one of the most important problems of our era and hence the discovery of new effective therapeutics is urgent. At this point, studying the allosteric communication pathways in the bacterial ribosome and revealing allosteric sites/residues is critical for designing new inhibitors or repurposing readily approved drugs for this enormous machine. To shed light onto molecular details of the allosteric mechanisms, here we construct residue networks of the bacterial ribosomal complex at four different states of translation by using an effective description of the intermolecular interactions. Centrality analysis of these networks highlights the functional roles of structural components and critical residues on the ribosomal complex. High betweenness scores reveal pathways of residues connecting numerous sites on the structure. Interestingly, these pathways assemble highly conserved residues, drug binding sites, and known allosterically linked regions on the same structure. This study proposes a new residue-level model to test how distant sites on the molecular machine may be linked through hub residues that are critically located on the contact topology to inherently form communication pathways. Findings also indicate intersubunit bridges B1b, B3, B5, B7, and B8 as critical targets to design novel antibiotics.

Seeking allosteric networks in PDZ domains

Ever since Ranganathan and coworkers subjected the covariation of amino acid residues in the postsynaptic density-95/Discs large/Zonula occludens 1 (PDZ) domain family to a statistical correlation analysis, PDZ domains have represented a paradigmatic family to explore single domain protein allostery. Nevertheless, several theoretical and experimental studies in the past two decades have contributed contradicting results with regard to structural localization of the allosteric networks, or even questioned their actual existence in PDZ domains. In this review, we first describe theoretical and experimental approaches that were used to probe the energetic network(s) in PDZ domains. We then compare the proposed networks for two well-studied PDZ domains namely the third PDZ domain from PSD-95 and the second PDZ domain from PTP-BL. Our analysis highlights the contradiction between the different methods and calls for additional work to better understand these allosteric phenomena.

Modulation of allosteric coupling by mutations: from protein dynamics and packing to altered native ensembles and function

A large body of work has gone into understanding the effect of mutations on protein structure and function. Conventional treatments have involved quantifying the change in stability, activity and relaxation rates of the mutants with respect to the wild-type protein. However, it is now becoming increasingly apparent that mutational perturbations consistently modulate the packing and dynamics of a significant fraction of protein residues, even those that are located >10–15 Å from the mutated site. Such long-range modulation of protein features can distinctly tune protein stability and the native conformational ensemble contributing to allosteric modulation of function. In this review, I summarize a series of experimental and computational observations that highlight the incredibly pliable nature of proteins and their response to mutational perturbations manifested via the intra-protein interaction network. I highlight how an intimate understanding of mutational effects could pave the way for integrating stability, folding, cooperativity and even allostery within a single physical framework.

Singular value decomposition for the correlation of atomic fluctuations with arbitrary angle

Many proteins exhibit a critical property called allostery, which enables intra-molecular transmission of information between distal sites. Microscopically, allosteric response is closely related to correlated atomic fluctuations. Conventional correlation analysis correlates the atomic fluctuations at two sites by taking the dot product (DP) between the fluctuations, which accounts only for the parallel and antiparallel components. Here, we present a singular value decomposition (SVD) method that analyzes the correlation coefficient of fluctuation dynamics with an arbitrary angle between the correlated directions. In a model allosteric system, the second PDZ domain (PDZ2) in the human PTP1E protein, approximately one third of the strong correlations have near-perpendicular directions, which are underestimated in the conventional method. The discrimination becomes more prominent for residue pairs with larger separation. The results of the proposed SVD method are more consistent with the experimentally determined PDZ2 dynamics than those of conventional method. In addition, the SVD method improved the prediction accuracy of the allosteric sites in a dataset of 23 known allosteric monomer proteins. The proposed method may inspire extended investigation not only into allostery, but also into protein dynamics and drug design.

A non-equilibrium approach to allosteric communication

While the theory of protein folding is well developed, including concepts such as rugged energy landscape, folding funnel, etc., the same degree of understanding has not been reached for the description of the dynamics of allosteric transitions in proteins. This is not only due to the small size of the structural change upon ligand binding to an allosteric site, but also due to challenges in designing experiments that directly observe such an allosteric transition. On the basis of recent pump-probe-type experiments (Buchli et al. 2013 Proc. Natl Acad. Sci. USA110, 11 725-11 730. (doi:10.1073/pnas.1306323110)) and non-equilibrium molecular dynamics simulations (Buchenberg et al. 2017 Proc. Natl Acad. Sci. USA114, E6804-E6811. (doi:10.1073/pnas.1707694114)) studying an photoswitchable PDZ2 domain as model for an allosteric transition, we outline in this perspective how such a description of allosteric communication might look. That is, calculating the dynamical content of both experiment and simulation (which agree remarkably well with each other), we find that allosteric communication shares some properties with downhill folding, except that it is an 'order-order' transition. Discussing the multiscale and hierarchical features of the dynamics, the validity of linear response theory as well as the meaning of 'allosteric pathways', we conclude that non-equilibrium experiments and simulations are a promising way to study dynamical aspects of allostery.This article is part of a discussion meeting issue 'Allostery and molecular machines'.