Allosteric Mechanisms of Nonadditive Substituent Contributions to Protein-Ligand Binding

Allosteric Regulation of Cyclic Nucleotide Dependent Protein Kinases

Kinases include a wide variety of valuable drug targets, but full therapeutic exploitation requires a high degree of selectivity. A promising avenue to engineer the desired kinase selectivity relies on allosteric sites. Here we provide a focused minireview of recent progress in allosteric modulation of cyclic nucleotide-dependent kinases, including protein kinases A and G. We show how apparently diverse emerging concepts such as allosteric pluripotency, allosteric non-additive binding and uncompetitive allosteric inhibition are all manifestations of complex conformational ensembles. Such ensembles include not only the typical apo-inactive and effector-bound-active states, but also mixed intermediates that feature attributes of the former states within a single molecule. We also discuss how allosteric responses are amplified by aggregation processes, thus establishing a novel interface between the signaling and amyloid fields. Finally, we critically evaluate the challenges and opportunities for clinical translation opened by these emerging allosteric concepts.

Divergent allostery reveals critical differences between structurally homologous regulatory domains of Plasmodium falciparum and human protein kinase G

Malaria is a life-threatening infectious disease primarily caused by the Plasmodium falciparum parasite. The increasing resistance to current antimalarial drugs and their side effects has led to an urgent need for novel malaria drug targets, such as the P. falciparum cGMP-dependent protein kinase (pfPKG). However, PKG plays an essential regulatory role also in the human host. Human PKG (hPKG) and pfPKG are controlled by structurally homologs cGMP-binding domains (CBDs). Here, we show that despite the structural similarities between the essential CBDs in pfPKG and hPKG, their respective allosteric networks differ significantly. Through comparative analyses of CHESCA, molecular dynamics simulations, and backbone internal dynamics measurements, we found that conserved allosteric elements within the essential CBDs are wired differently in pfPKG and hPKG to implement cGMP-dependent kinase activation. Such pfPKG vs. hPKG rewiring of allosteric networks was unexpected due to the structural similarity between the two essential CBDs. Yet, such finding provides crucial information on which elements to target for selective inhibition of pfPKG vs. hPKG, which may potentially reduce undesired side-effects in malaria treatments.

Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

An important goal of biological research is to explain and hopefully predict cell behavior from the molecular properties of cellular components. Accordingly, much work was done to build extensive “omic” datasets and develop theoretical methods, including computer simulation and network analysis to process as quantitatively as possible the parameters contained in these resources. Furthermore, substantial effort was made to standardize data presentation and make experimental results accessible to data scientists. However, the power and complexity of current experimental and theoretical tools make it more and more difficult to assess the capacity of gathered parameters to support optimal progress in our understanding of cell function. The purpose of this review is to focus on biomolecule interactions, the interactome, as a specific and important example, and examine the limitations of the explanatory and predictive power of parameters that are considered as suitable descriptors of molecular interactions. Recent experimental studies on important cell functions, such as adhesion and processing of environmental cues for decision-making, support the suggestion that it should be rewarding to complement standard binding properties such as affinity and kinetic constants, or even force dependence, with less frequently used parameters such as conformational flexibility or size of binding molecules.

Mutual Protein-Ligand Conformational Selection Drives cGMP vs. cAMP Selectivity in Protein Kinase G

Protein kinase G (PKG) is a major receptor of cGMP, and controls signaling pathways distinct from those regulated by cAMP. However, the contributions of the two substituents that differentiate cGMP from cAMP (i.e. 6-oxo and 2-NH₂) to the cGMP-versus-cAMP selectivity of PKG remain unclear. Here, using NMR to map how binding affinity and dynamics of the protein and ligand vary along a ligand double-substitution cycle, we show that the contributions of the two substituents to binding affinity are surprisingly non-additive. Such non-additivity stems primarily from mutual protein-ligand conformational selection, whereby not only does the ligand select for a preferred protein conformation upon binding, but also, the protein selects for a preferred ligand conformation. The 6-oxo substituent mainly controls the conformational equilibrium of the bound protein, while the 2-NH₂ substituent primarily controls the conformational equilibrium of the unbound ligand (i.e. syn versus anti). Therefore, understanding the conformational dynamics of both the protein and ligand is essential to explain the cGMP-versus-cAMP selectivity of PKG.

State-selective frustration as a key driver of allosteric pluripotency

Allosteric pluripotency arises when an allosteric effector switches from agonist to antagonist depending on the experimental conditions. For example, the Rp-cAMPS ligand of Protein Kinase A (PKA) switches from agonist to antagonist as the MgATP concentration increases and/or the kinase substrate affinity or concentration decreases. Understanding allosteric pluripotency is essential to design effective allosteric therapeutics with minimal side effects. Allosteric pluripotency of PKA arises from divergent allosteric responses of two homologous tandem cAMP-binding domains, resulting in a free energy landscape for the Rp-cAMPS-bound PKA regulatory subunit R1a in which the ground state is kinase inhibition-incompetent and the kinase inhibition-competent state is excited. The magnitude of the free energy difference between the ground non-inhibitory and excited inhibitory states (ΔG_R,Gap) relative to the effective free energy of R1a binding to the catalytic subunit of PKA (ΔG_R:C) dictates whether the antagonism-to-agonism switch occurs. However, the key drivers of ΔG_R,Gap are not fully understood. Here, by analyzing an R1a mutant that selectively silences allosteric pluripotency, we show that a major determinant of ΔG_R,Gap unexpectedly arises from state-selective frustration in the ground inhibition-incompetent state of Rp-cAMPS-bound R1a. Such frustration is caused by steric clashes between the phosphate-binding cassette and the helices preceding the lid, which interact with the phosphate and base of Rp-cAMPS, respectively. These clashes are absent in the excited inhibitory state, thus reducing the ΔG_R,Gap to values comparable to ΔG_R:C, as needed for allosteric pluripotency to occur. The resulting model of allosteric pluripotency is anticipated to assist the design of effective allosteric modulators.

The Rp-cAMPS ligand of protein kinase A switches from agonist to antagonist depending on metabolite and proteomic contexts. We show that the state-selective frustration is a key driver of this allosteric pluripotency phenomenon.

Dynamical Basis of Allosteric Activation for the Plasmodium falciparum Protein Kinase G

The Plasmodium falciparum cGMP-dependent protein kinase (PfPKG) is required for the progression of the Plasmodium's life cycle and is therefore a promising malaria drug target. PfPKG includes four cGMP-binding domains (CBD-A to CBD-D). CBD-D plays a crucial role in PfPKG regulation as it is the primary determinant for the inhibition and cGMP-dependent activation of the catalytic domain. Hence, it is critical to understand how CBD-D is allosterically regulated by cGMP. Although the apo versus holo conformational changes of CBD-D have been reported, information on the intermediates of the activation pathway is currently lacking. Here, we employed molecular dynamics simulations to model four key states along the thermodynamic cycle for the cGMP-dependent activation of the PfPKG CBD-D domain. The simulations were compared to NMR data, and they revealed that the PfPKG CBD-D activation pathway samples a compact intermediate in which the N- and C-terminal helices approach the central β-barrel. In addition, by comparing the cGMP-bound active and inactive states, the essential binding interactions that differentiate these states were identified. The identification of structural and dynamical features unique to the cGMP-bound inactive state provides a promising basis to design PfPKG-selective allosteric inhibitors as a viable treatment for malaria.

Allosteric inhibition explained through conformational ensembles sampling distinct "mixed" states

Allosteric modulation provides an effective avenue for selective and potent enzyme inhibition. Here, we summarize and critically discuss recent advances on the mechanisms of allosteric partial agonists for three representative signalling enzymes activated by cyclic nucleotides: the cAMP-dependent protein kinase (PKA), the cGMP-dependent protein kinase (PKG), and the exchange protein activated by cAMP (EPAC). The comparative analysis of partial agonism in PKA, PKG and EPAC reveals a common emerging theme, i.e. the sampling of distinct “mixed” conformational states, either within a single domain or between distinct domains. Here, we show how such “mixed” states play a crucial role in explaining the observed functional response, i.e. partial agonism and allosteric pluripotency, as well as in maximizing inhibition while minimizing potency losses. In addition, by combining Nuclear Magnetic Resonance (NMR), Molecular Dynamics (MD) simulations and Ensemble Allosteric Modeling (EAM), we also show how to map the free-energy landscape of conformational ensembles containing “mixed” states. By discussing selected case studies, we illustrate how MD simulations and EAM complement NMR to quantitatively relate protein dynamics to function. The resulting NMR- and MD-based EAMs are anticipated to inform not only the design of new generations of highly selective allosteric inhibitors, but also the choice of multidrug combinations.