Allostery and population shift in drug discovery

Advances in Bioinspired Artificial System Enabling Biomarker-Driven Therapy

Bioinspired molecular engineering strategies have emerged as powerful tools that significantly enhance the development of novel therapeutics, improving efficacy, specificity, and safety in disease treatment. Recent advancements have focused on identifying and utilizing disease-associated biomarkers to optimize drug activity and address challenges inherent in traditional therapeutics, such as frequent drug administrations, poor patient adherence, and increased risk of adverse effects. In this review, we provide a comprehensive overview of the latest developments in bioinspired artificial systems (BAS) that use molecular engineering to tailor therapeutic responses to drugs in the presence of disease-specific biomarkers. We examine the transition from open-loop systems, which rely on external cues, to closed-loop feedback systems capable of autonomous self-regulation in response to disease-associated biomarkers. We detail various BAS modalities designed to achieve biomarker-driven therapy, including activatable prodrug molecules, smart drug delivery platforms, autonomous artificial cells, and synthetic receptor-based cell therapies, elucidating their operational principles and practical in vivo applications. Finally, we discuss the current challenges and future perspectives in the advancement of BAS-enabled technology and envision that ongoing advancements toward more programmable and customizable BAS-based therapeutics will significantly enhance precision medicine.

Modulation of the conformational landscape of the PDZ3 domain by perturbation on a distal non-canonical α3 helix: decoding the microscopic mechanism of allostery in the PDZ3 domain

While allosteric signal transduction is crucial for protein signaling and regulation, the dynamic process of allosteric communication remains poorly understood. The third PDZ domain (PDZ stands for the common structural domain shared by the postsynaptic density protein (PSD95), Drosophila disc large tumor suppressor (DlgA), and zonula occludens-1 protein (ZO-1)) serves as a classic example of a single-domain allosteric protein, demonstrating a long-range coupling between the C-terminal α helix (known as the α3 helix) and ligand binding. A molecular level understanding of how the α3 helix modulates the ligand binding affinity of the PDZ3 domain is still lacking. In this study, extensive molecular dynamics simulations corroborated with principal component analysis (PCA), ligand binding free energy calculations, energetic frustration analysis and Markov state model analysis are employed to uncover such molecular details. We demonstrate the definite presence of a binding competent closed-like state in the conformational landscape of wild-type PDZ3. The population modulations of this closed state and other binding incompetent states in the landscape due to α3-truncation/mutation of PDZ3 are explored. A correlation between the closed state population and calculated binding free energy is established, which supports the conformation selection mechanism. Covariance analysis identified the presence of correlated motion between two distant loops (β1-β2 and β2-β3) in the wild-type PDZ3 system, which weakened due to truncation/mutation in the distant α3 helix. It has also been observed that whenever the α3 helix was perturbed, the β2-β3 loop got further away from the binding groove and it is found to be correlated with the binding free energy values. Energetic frustration analysis of the PDZ3 domain also showed that the β2-β3 loop is highly frustrated. Finally, MSM analysis revealed a relevant timescale (closed to open state transition), which is similar to the observed experimental signal transduction timescale for the system. These observations led to the conclusion that the distantly located α3 helix plays a pivotal role in regulating the conformational landscape of the PDZ3 domain, determining the ligand binding affinity and resulting in allosteric behavior of the domain.

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.

Protein conformational ensembles in function: roles and mechanisms

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

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Electrochemical Biosensors for Whole Blood Analysis: Recent Progress, Challenges, and Future Perspectives

Whole blood, as one of the most significant biological fluids, provides critical information for health management and disease monitoring. Over the past 10 years, advances in nanotechnology, microfluidics, and biomarker research have spurred the development of powerful miniaturized diagnostic systems for whole blood testing toward the goal of disease monitoring and treatment. Among the techniques employed for whole-blood diagnostics, electrochemical biosensors, as known to be rapid, sensitive, capable of miniaturization, reagentless and washing free, become a class of emerging technology to achieve the target detection specifically and directly in complex media, e.g., whole blood or even in the living body. Here we are aiming to provide a comprehensive review to summarize advances over the past decade in the development of electrochemical sensors for whole blood analysis. Further, we address the remaining challenges and opportunities to integrate electrochemical sensing platforms.

Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase

Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.

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.

Biological Calorimetry: Old Friend, New Insights

Calorimetry is an old experimental technique (first instrument developed in S. XVIII), but it is broadly used and still provides key information for understanding biological processes at the molecular level, particularly, cooperative phenomena in protein interactions. Here, we review and highlight some key aspects of biological calorimetry. Several biological systems will be described in which calorimetry was instrumental for modeling the behavior of the protein and obtaining further biological insight.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

A guide to designing photocontrol in proteins: methods, strategies and applications

Light is essential for various biochemical processes in all domains of life. In its presence certain proteins inside a cell are excited, which either stimulates or inhibits subsequent cellular processes. The artificial photocontrol of specifically proteins is of growing interest for the investigation of scientific questions on the organismal, cellular and molecular level as well as for the development of medicinal drugs or biocatalytic tools. For the targeted design of photocontrol in proteins, three major methods have been developed over the last decades, which employ either chemical engineering of small-molecule photosensitive effectors (photopharmacology), incorporation of photoactive non-canonical amino acids by genetic code expansion (photoxenoprotein engineering), or fusion with photoreactive biological modules (hybrid protein optogenetics). This review compares the different methods as well as their strategies and current applications for the light-regulation of proteins and provides background information useful for the implementation of each technique.

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

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

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery

Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.

Engineering an Allosteric Control of Protein Function

Allosteric regulation in proteins is fundamental to many important biological processes. Allostery has been employed to control protein functions by regulating protein activity. Engineered allosteric regulation allows controlling protein activity in subsecond time scale and has a broad range of applications, from dissecting spatiotemporal dynamics in biochemical cascades to applications in biotechnology and medicine. Here, we review the concept of allostery in proteins and various approaches to identify allosteric sites and pathways. We then provide an overview of strategies and tools used in allosteric protein regulation and their utility in biological applications. We highlight various classes of proteins, where regulation is achieved through allostery. Finally, we analyze the current problems, critical challenges, and future prospective in achieving allosteric regulation in proteins.

Inhibition of Nonfunctional Ras

Intuitively, functional states should be targeted; not nonfunctional ones. So why could drugging the inactive K-Ras4BG12Cwork-but drugging the inactive kinase will likely not? The reason is the distinct oncogenic mechanisms. Kinase driver mutations work by stabilizing the active state and/or destabilizing the inactive state. Either way, oncogenic kinases are mostly in the active state. Ras driver mutations work by quelling its deactivation mechanisms, GTP hydrolysis, and nucleotide exchange. Covalent inhibitors that bind to the inactive GDP-bound K-Ras4BG12C conformation can thus work. By contrast, in kinases, allosteric inhibitors work by altering the active-site conformation to favor orthosteric drugs. From the translational standpoint this distinction is vital: it expedites effective pharmaceutical development and extends the drug classification based on the mechanism of action. Collectively, here we postulate that drug action relates to blocking the mechanism of activation, not to whether the protein is in the active or inactive state.

Disrupting enzyme fluidity

A combination of X-ray crystallography, NMR, and mass spectrometry has revealed how diverse small-molecule inhibitors bind Bruton’s tyrosine kinase and alter the conformation of this enzyme.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

Wrangling Shape-Shifting Morpheeins to Tackle Disease and Approach Drug Discovery

Homo-multimeric proteins that can come apart, change shape, and reassemble differently with functional consequences have been called morpheeins and/or transformers; these provide a largely unexplored context for understanding disease and developing allosteric therapeutics. This article describes such proteins within the context of protein structure dynamics, provides one detailed example related to an inborn error of metabolism and potential herbicide development, and describes the context for applying these ideas for understanding disease and designing bioactive molecules, such as therapeutics.

Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy

Allostery, which is one of the most direct and efficient methods to fine-tune protein functions, has gained increasing recognition in drug discovery. However, there are several challenges associated with the identification of allosteric sites, which is the fundamental cornerstone of drug design. Previous studies on allosteric site predictions have focused on communication signals propagating from the allosteric sites to the orthosteric sites. However, recent biochemical studies have revealed that allosteric coupling is bidirectional and that orthosteric perturbations can modulate allosteric sites through reversed allosteric communication. Here, we proposed a new framework for the prediction of allosteric sites based on reversed allosteric communication using a combination of computational and experimental strategies (molecular dynamics simulations, Markov state models, and site-directed mutagenesis). The desirable performance of our approach was demonstrated by predicting the known allosteric site of the small molecule MDL-801 in nicotinamide dinucleotide (NAD⁺)-dependent protein lysine deacetylase sirtuin 6 (Sirt6). A potential novel cryptic allosteric site located around the L116, R119, and S120 residues within the dynamic ensemble of Sirt6 was identified. The allosteric effect of the predicted site was further quantified and validated using both computational and experimental approaches. This study proposed a state-of-the-art computational pipeline for detecting allosteric sites based on reversed allosteric communication. This method enabled the identification of a previously uncharacterized potential cryptic allosteric site on Sirt6, which provides a starting point for allosteric drug design that can aid the identification of candidate pockets in other therapeutic targets.

Using reversed allosteric communication, we performed MD simulations, MSMs, and mutagenesis experiments, to discover allosteric sites. It reproduced the known allosteric site for MDL-801 on Sirt6 and uncovered a novel cryptic allosteric Pocket X.

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

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

Factor XIIIa inhibitors as potential novel drugs for venous thromboembolism

Human factor XIIIa (FXIIIa) is a multifunctional transglutaminase with a significant role in hemostasis. FXIIIa catalyzes the last step in the coagulation process. It stabilizes the blood clot by cross-linking the α- and γ-chains of fibrin. It also protects the newly formed clot from plasmin-mediated fibrinolysis, primarily by cross-linking α2-antiplasmin to fibrin. Furthermore, FXIIIa is a major determinant of clot size and clot's red blood cells content. Therefore, inhibitors targeting FXIIIa have been considered to develop a new generation of anticoagulants to prevent and/or treat venous thromboembolism. Several inhibitors of FXIIIa have been discovered or designed including active site and allosteric site small molecule inhibitors as well as natural and modified polypeptides. This work reviews the structural, biochemical, and pharmacological aspects of FXIIIa inhibitors so as to advance their molecular design to become more clinically relevant.

Inhibitors of blood coagulation factor XIII

The blood coagulation factor XIII (FXIII) plays an essential role in the stabilization of fibrin clots. This factor, belonging to the class of transglutaminases, catalyzes the final step of secondary hemostasis, i.e. the crosslinking of fibrin polymers. These crosslinks protect the clots against premature fibrinolysis. Consequently, FXIII is an interesting target for the therapeutic treatment of cardiovascular diseases. In this context, inhibitors can influence FXIII in the activation process of the enzyme itself or in its catalytic activity. To date, there is no FXIII inhibitor in medical application, but several studies have been conducted in the past. These studies provided a better understanding of FXIII and identified new lead structures for FXIII inhibitors. Next to small molecule inhibitors, the most promising candidates for the development of clinically applicable FXIII inhibitors are the peptide inhibitors tridegin and transglutaminase-inhibiting Michael acceptors (TIMAs) due to their selectivity towards activated FXIII (FXIIIa). In this review, select FXIII inhibitors and their pharmacological potential are discussed.

Quantitative structural assessment of graded receptor agonism

Ligand-receptor interactions, which are ubiquitous in physiology, are described by theoretical models of receptor pharmacology. Structural evidence for graded efficacy receptor conformations predicted by receptor theory has been limited but is critical to fully validate theoretical models. We applied quantitative structure-function approaches to characterize the effects of structurally similar and structurally diverse agonists on the conformational ensemble of nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ). For all ligands, agonist functional efficacy is correlated to a shift in the conformational ensemble equilibrium from a ground state toward an active state, which is detected by NMR spectroscopy but not observed in crystal structures. For the structurally similar ligands, ligand potency and affinity are also correlated to efficacy and conformation, indicating ligand residence times among related analogs may influence receptor conformation and function. Our results derived from quantitative graded activity-conformation correlations provide experimental evidence and a platform with which to extend and test theoretical models of receptor pharmacology to more accurately describe and predict ligand-dependent receptor activity.

How Electrostatic Coupling Enables Conformational Plasticity in a Tyrosine Kinase

Protein kinases are important cellular signaling molecules involved in cancer and a multitude of other diseases. It is well-known that inactive kinases display a remarkable conformational plasticity; however, the molecular mechanisms remain poorly understood. Conformational heterogeneity presents an opportunity but also a challenge in kinase drug discovery. The ability to predictively model various conformational states could accelerate selective inhibitor design. Here we performed a proton-coupled molecular dynamics study to explore the conformational landscape of a c-Src kinase. Starting from a completely inactive structure, the simulations captured all major types of conformational states without the use of a target structure, mutation, or bias. The simulations allowed us to test the experimental hypotheses regarding the mechanism of DFG flip, its coupling to the αC-helix movement, and the formation of regulatory spine. Perhaps the most significant finding is how key titratable residues, such as DFG-Asp, αC-Glu, and HRD-Asp, change protonation states dependent on the DFG, αC, and activation loop conformations. Our data offer direct evidence to support a long-standing hypothesis that protonation of Asp favors the DFG-out state and explain why DFG flip is also possible in simulations with deprotonated Asp. The simulations also revealed intermediate states, among which a unique DFG-out/α-C state formed as DFG-Asp is moved into a back pocket forming a salt bridge with catalytic Lys, which can be tested in selective inhibitor design. Our finding of how proton coupling enables the remarkable conformational plasticity may shift the paradigm of computational studies of kinases which assume fixed protonation states. Understanding proton-coupled conformational dynamics may hold a key to further innovation in kinase drug discovery.

Molecular Dynamics Simulations of Adenosine Receptors: Advances, Applications and Trends

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Adenosine receptors (ARs) are transmembrane proteins that belong to the G protein-coupled receptors (GPCRs) superfamily and mediate the biological functions of adenosine. To date, four AR subtypes are known, namely A1, A2A, A2B and A3 that exhibit different signaling pathways, tissue localization, and mechanisms of activation. Moreover, the widespread ARs and their implication in numerous physiological and pathophysiological conditions had made them pivotal therapeutic targets for developing clinically effective agents.

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The crystallographic success in identifying the 3D crystal structures of A2A and A1 ARs has dramatically enriched our understanding of their structural and functional properties such as ligand binding and signal transduction. This, in turn, has provided a structural basis for a larger contribution of computational methods, particularly molecular dynamics (MD) simulations, toward further investigation of their molecular properties and designing bioactive ligands with therapeutic potential. MD simulation has been proved to be an invaluable tool in investigating ARs and providing answers to some critical questions. For example, MD has been applied in studying ARs in terms of ligand-receptor interactions, molecular recognition, allosteric modulations, dimerization, and mechanisms of activation, collectively aiding in the design of subtype selective ligands.

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In this review, we focused on the advances and different applications of MD simulations utilized to study the structural and functional aspects of ARs that can foster the structure-based design of drug candidates. In addition, relevant literature was briefly discussed which establishes a starting point for future advances in the field of drug discovery to this pivotal group of drug targets.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

Protein-Ligand Complexes as Constrained Dynamical Systems

This study focuses on how the low-frequency end of the vibrational spectrum related to the functional motions changes as a protein binds to a small ligand(s). Our recently proposed residue-specific (RESPEC) elastic network model provides a natural laboratory for this aim due to its systematic mixed coarse-graining approach and parametrization. Current analysis on a large data set of protein-ligand complexes reveals a universal curve enclosing the frequency distributions, which bears the features of previous computational and experimental studies. We mostly observe positive frequency shifts in the collective modes of the protein upon ligand binding. This observation, conforming to the Rayleigh-Courant-Fisher theorem, points to a constraining effect imposed by ligands on protein dynamics, which may be accompanied by a negative vibrational entropy difference. Positive frequency shifts in the global modes can thus be linked to the harmonic well getting steeper, because of interactions with the ligand(s).

Quantifying correlations between mutational sites in the catalytic subunit of γ-secretase

Presenilin 1 (PS1) is the catalytic subunit of the γ-secretase complex which is involved in the generation of amyloid-β peptides (Aβ). Single point mutations in PS1 alter the cleavage pattern of the amyloid precursor protein (APP) and lead to the formation of aberrant Aβ peptides. To date, more than two hundred mutations distributed among almost a third of PS1's amino acids have been associated to the development of Alzheimer's disease (AD). Nevertheless, the mechanism by which mutations far from the catalytic site alter the γ-secretase's cleavage pattern remains unclear. In this work we analyzed correlated motions between amino acids in the wild type (WT) enzyme and 13 γ-secretase mutant models employing a multi-scale molecular dynamics approach. The effect of the protonation state of key catalytic residue Asp385 on the correlation networks was also evaluated. We observed that the strength and number of correlations is highly influenced in all mutant models in both protonation state models. The biggest changes were observed in mutants I83T, W165G, H214Y and L435F; the latest has been proved to drastically reduce γ-secretase activity. Finally, we made a classification of the studied mutations according to their correlation networks with amino acids at: (1) the interfaces with the other γ-secretase components, (2) the catalytic site, (3) the substrate entry site and (4) the substrate recognition site. Overall, this work provides insight into the allosteric communication networks of PS1.

How calcium ion binding induces the conformational transition of the calmodulin N-terminal domain—an atomic level characterization

The Ca²⁺binding and triggering conformation transition of nCaM were detected in unbiased molecular dynamics simulations.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations

Dimensionality reduction methods are usually applied on molecular dynamics simulations of macromolecules for analysis and visualization purposes. It is normally desired that suitable dimensionality reduction methods could clearly distinguish functionally important states with different conformations for the systems of interest. However, common dimensionality reduction methods for macromolecules simulations, including predefined order parameters and collective variables (CVs), principal component analysis (PCA), and time-structure based independent component analysis (t-ICA), only have limited success due to significant key structural information loss. Here, we introduced the t-distributed stochastic neighbor embedding (t-SNE) method as a dimensionality reduction method with minimum structural information loss widely used in bioinformatics for analyses of macromolecules, especially biomacromolecules simulations. It is demonstrated that both one-dimensional (1D) and two-dimensional (2D) models of the t-SNE method are superior to distinguish important functional states of a model allosteric protein system for free energy and mechanistic analysis. Projections of the model protein simulations onto 1D and 2D t-SNE surfaces provide both clear visual cues and quantitative information, which is not readily available using other methods, regarding the transition mechanism between two important functional states of this protein.

REDAN: Relative Entropy-Based Dynamical Allosteric Network Model

Protein allostery is ubiquitous phenomena that are important for cellular signaling processes. Despite extensive methodology development, a quantitative model is still needed to accurately measure protein allosteric response upon external perturbation. Here, we introduced the relative entropy concept from information theory as a quantitative metric to develop a method for measurement of the population shift with regard to protein structure during allosteric transition. This method is referred to as relative entropy-based dynamical allosteric network (REDAN) model. Using this method, protein allostery could be evaluated at three mutually dependent structural levels: allosteric residues, allosteric pathways, and allosteric communities. All three levels are carried out using rigorous searching algorithms based on relative entropy. Application of the REDAN model on the second PDZ domain (PDZ2) in the human PTP1E protein provided metric-based insight into its allostery upon peptide binding.

Probing membrane protein ground and conformationally excited states using dipolar- and J-coupling mediated MAS solid state NMR experiments

The intrinsic conformational plasticity of membrane proteins directly influences the magnitude of the orientational-dependent NMR interactions such as dipolar couplings (DC) and chemical shift anisotropy (CSA). As a result, the conventional cross-polarization (CP)-based techniques mainly capture the more rigid regions of membrane proteins, while the most dynamic regions are essentially invisible. Nonetheless, dynamic regions can be detected using experiments in which polarization transfer takes place via J-coupling interactions. Here, we review our recent efforts to develop single and dual acquisition pulse sequences with either 1H or 13C detection that utilize both DC and J-coupling mediated transfer to detect both rigid and mobile regions of membrane proteins in native-like lipid environments. We show the application of these new methods for studying the conformational equilibrium of a single-pass membrane protein, phospholamban, which regulates the calcium transport across the sarcoplasmic reticulum (SR) membrane by interacting with the SR Ca2+-ATPase. We anticipate that these methods will be ideal to portray the complex dynamics of membrane proteins in their native environments.

E-hooks provide guidance and a soft landing for the microtubule binding domain of dynein

Macromolecular binding is a complex process that involves sensing and approaching the binding partner, adopting the proper orientation, and performing the physical binding. We computationally investigated the role of E-hooks, which are intrinsically disordered regions (IDRs) at the C-terminus of tubulin, on dynein microtubule binding domain (MTBD) binding to the microtubule as a function of the distance between the MTBD and its binding site on the microtubule. Our results demonstrated that the contacts between E-hooks and the MTBD are dynamical; multiple negatively charted patches of amino acids on the E-hooks grab and release the same positively charged patches on the MTBD as it approaches the microtubule. Even when the distance between the MTBD and the microtubule was greater than the E-hook length, the E-hooks sensed and guided MTBD via long-range electrostatic interactions in our simulations. Moreover, we found that E-hooks exerted electrostatic forces on the MTBD that were distance dependent; the force pulls the MTBD toward the microtubule at long distances but opposes binding at short distances. This mechanism provides a "soft-landing" for the MTBD as it binds to the microtubule. Finally, our analysis of the conformational states of E-hooks in presence and absence of the MTBD indicates that the binding process is a mixture of the induced-fit and lock-and-key macromolecular binding hypotheses. Overall, this novel binding mechanism is termed "guided-soft-binding" and could have broad-reaching impacts on the understanding of how IDRs dock to structured proteins.

Ancient thioredoxins evolved to modern-day stability-function requirement by altering native state ensemble

Thioredoxins (THRXs)-small globular proteins that reduce other proteins-are ubiquitous in all forms of life, from Archaea to mammals. Although ancestral thioredoxins share sequential and structural similarity with the modern-day (extant) homologues, they exhibit significantly different functional activity and stability. We investigate this puzzle by comparative studies of their (ancient and modern-day THRXs') native state ensemble, as quantified by the dynamic flexibility index (DFI), a metric for the relative resilience of an amino acid to perturbations in the rest of the protein. Clustering proteins using DFI profiles strongly resemble an alternative classification scheme based on their activity and stability. The DFI profiles of the extant proteins are substantially different around the α3, α4 helices and catalytic regions. Likewise, allosteric coupling of the active site with the rest of the protein is different between ancient and extant THRXs, possibly explaining the decreased catalytic activity at low pH with evolution. At a global level, we note that the population of low-flexibility (called hinges) and high-flexibility sites increases with evolution. The heterogeneity (quantified by the variance) in DFI distribution increases with the decrease in the melting temperature typically associated with the evolution of ancient proteins to their modern-day counterparts.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Conformational Plasticity in Tyrosine Kinase Inhibitor-Kinase Interactions Revealed with Fluorescence Spectroscopy and Theoretical Calculations

To understand drug-protein dynamics, it is necessary to account for drug molecular flexibility and binding site plasticity. Herein, we exploit fluorescence from a tyrosine kinase inhibitor, AG1478, as a reporter of its conformation and binding site environment when complexed with its cognate kinase. Water-soluble kinases, aminoglycoside phosphotransferase APH(3')-Ia and mitogen-activated protein kinase 14 (MAPK14), were chosen for this study. On the basis of our prior work, the AG1478 conformation (planar or twisted) was inferred from the fluorescence excitation spectrum and the polarity of the AG1478-binding site was deduced from the fluorescence emission spectrum, while red-edge excitation shift (REES) probed the heterogeneity of the binding site (protein conformation and hydration) distributions in the protein conformational ensemble. In the AG1478-APH(3')-Ia complex, both twisted (or partially twisted) and planar AG1478 conformations were evidenced from emission wavelength-dependent excitation spectra. The binding site environment provided by APH(3')-Ia was moderately polar (λ_max = 480 nm) with evidence for considerable heterogeneity (REES = 34 nm). In contrast, in the AG1478-MAPK14 complex, AG1478 was in a predominantly planar conformation with a lower degree of conformational heterogeneity. The binding site environment provided by the MAPK14 protein was of relatively low polarity (λ_max = 430 nm) with a smaller degree of heterogeneity (REES = 11 nm). The results are compared with the available X-ray data and discussed in the context of our current understanding of tyrosine kinase inhibitor conformation and protein conformational ensembles.

Single Binding Pockets Versus Allosteric Binding

An orthosteric site is commonly viewed as the primary, functionally binding pocket on a receptor. Signal molecules, endogenous agonists, and substrates are recognized by and bind to the orthosteric site of a specific target, resulting in a biological effect. A malfunctioning active site on a crucial receptor has been confirmed as the culprit that causes many metabolic disturbances, neurologic disorders, and genetic diseases. A competitive inhibitor that has a stronger binding affinity can outcompete an orthosteric ligand. An allosteric site, which is nonoverlapping and topographically distinct from the active pocket, can emerge as a potential regulatory site on the protein surface. An allosteric modulator interacts with a specific binding site, affecting the atoms of nearby residues, thus eliciting a series of conformational changes in the residues at the active site through propagation pathways. Allosteric regulation can potentiate or inhibit function instead of blocking it, and this is a promising strategy for drug design. In this chapter, we describe the tools and protocols for allosteric site analysis and allosteric ligand design.

1H-detected MAS solid-state NMR experiments enable the simultaneous mapping of rigid and dynamic domains of membrane proteins

Magic angle spinning (MAS) solid-state NMR (ssNMR) spectroscopy is emerging as a unique method for the atomic resolution structure determination of native membrane proteins in lipid bilayers. Although 13C-detected ssNMR experiments continue to play a major role, recent technological developments have made it possible to carry out 1H-detected experiments, boosting both sensitivity and resolution. Here, we describe a new set of 1H-detected hybrid pulse sequences that combine through-bond and through-space correlation elements into single experiments, enabling the simultaneous detection of rigid and dynamic domains of membrane proteins. As proof-of-principle, we applied these new pulse sequences to the membrane protein phospholamban (PLN) reconstituted in lipid bilayers under moderate MAS conditions. The cross-polarization (CP) based elements enabled the detection of the relatively immobile residues of PLN in the transmembrane domain using through-space correlations; whereas the most dynamic region, which is in equilibrium between folded and unfolded states, was mapped by through-bond INEPT-based elements. These new 1H-detected experiments will enable one to detect not only the most populated (ground) states of biomacromolecules, but also sparsely populated high-energy (excited) states for a complete characterization of protein free energy landscapes.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Discovery of Rab1 binding sites using an ensemble of clustering methods

Targeting non-native-ligand binding sites for potential investigative and therapeutic applications is an attractive strategy in proteins that share common native ligands, as in Rab1 protein. Rab1 is a subfamily member of Rab proteins, which are members of Ras GTPase superfamily. All Ras GTPase superfamily members bind to native ligands GTP and GDP, that switch on and off the proteins, respectively. Rab1 is physiologically essential for autophagy and transport between endoplasmic reticulum and Golgi apparatus. Pathologically, Rab1 is implicated in human cancers, a neurodegenerative disease, cardiomyopathy, and bacteria-caused infectious diseases. We have performed structural analyses on Rab1 protein using a unique ensemble of clustering methods, including multi-step principal component analysis, non-negative matrix factorization, and independent component analysis, to better identify representative Rab1 proteins than the application of a single clustering method alone does. We then used the identified representative Rab1 structures, resolved in multiple ligand states, to map their known and novel binding sites. We report here at least a novel binding site on Rab1, involving Rab1-specific residues that could be further explored for the rational design and development of investigative probes and/or therapeutic small molecules against the Rab1 protein. Proteins 2017; 85:859-871. © 2016 Wiley Periodicals, Inc.

Asymmetric configurations in a reengineered homodimer reveal multiple subunit communication pathways in protein allostery

Many allosteric proteins form homo-oligomeric complexes to regulate a biological function. In homo-oligomers, subunits establish communication pathways that are modulated by external stimuli like ligand binding. A challenge for dissecting the communication mechanisms in homo-oligomers is identifying intermediate liganded states, which are typically transiently populated. However, their identities provide the most mechanistic information on how ligand-induced signals propagate from bound to empty subunits. Here, we dissected the directionality and magnitude of subunit communication in a reengineered single-chain version of the homodimeric transcription factor cAMP receptor protein. By combining wild-type and mutant subunits in various asymmetric configurations, we revealed a linear relationship between the magnitude of cooperative effects and the number of mutant subunits. We found that a single mutation is sufficient to change the global allosteric behavior of the dimer even when one subunit was wild type. Dimers harboring two mutations with opposite cooperative effects had different allosteric properties depending on the arrangement of the mutations. When the two mutations were placed in the same subunit, the resulting cooperativity was neutral. In contrast, when placed in different subunits, the observed cooperativity was dominated by the mutation with strongest effects over cAMP affinity relative to wild type. These results highlight the distinct roles of intrasubunit interactions and intersubunit communication in allostery. Finally, dimers bound to either one or two cAMP molecules had similar DNA affinities, indicating that both asymmetric and symmetric liganded states activate DNA interactions. These studies have revealed the multiple communication pathways that homo-oligomers employ to transduce signals.