Rational Design of Allosteric and Selective Inhibitors of the Molecular Chaperone TRAP1

Honokiol Bis-Dichloroacetate Is a Selective Allosteric Inhibitor of the Mitochondrial Chaperone TRAP1

Aims: TNF receptor-associated protein 1 (TRAP1), the mitochondrial paralog of the heat shock protein 90 (Hsp90) family of molecular chaperones, is required for neoplastic growth in several tumor cell models, where it inhibits succinate dehydrogenase (SDH) activity, thus favoring bioenergetic rewiring, maintenance of redox homeostasis, and orchestration of a hypoxia-inducible factor 1-alpha (HIF1α)-mediated pseudohypoxic program. Development of selective TRAP1 inhibitors is instrumental for targeted development of antineoplastic drugs, but it has been hampered up to now by the high degree of homology among catalytic pockets of Hsp90 family members. The vegetal derivative honokiol and its lipophilic bis-dichloroacetate ester, honokiol DCA (HDCA), are small-molecule compounds with antineoplastic activity. HDCA leads to oxidative stress and apoptosis in in vivo tumor models and displays an action that is functionally opposed to that of TRAP1, as it induces both SDH and the mitochondrial deacetylase sirtuin-3 (SIRT3), which further enhances SDH activity. We investigated whether HDCA could interact with TRAP1, inhibiting its chaperone function, and the effects of HDCA on tumor cells harboring TRAP1. Results: An allosteric binding site in TRAP1 is able to host HDCA, which inhibits TRAP1 but not Hsp90 ATPase activity. In neoplastic cells, HDCA reverts TRAP1-dependent downregulation of SDH, decreases proliferation rate, increases mitochondrial superoxide levels, and abolishes tumorigenic growth. Innovation: HDCA is a potential lead compound for the generation of antineoplastic approaches based on the allosteric inhibition of TRAP1 chaperone activity. Conclusions: We have identified a selective TRAP1 inhibitor that can be used to better dissect TRAP1 biochemical functions and to tailor novel tumor-targeting strategies.

Exploiting Folding and Degradation Machineries To Target Undruggable Proteins: What Can a Computational Approach Tell Us?

Advances in genomics and proteomics have unveiled an ever-growing number of key proteins and provided mechanistic insights into the genesis of pathologies. This wealth of data showed that changes in expression levels of specific proteins, mutations, and post-translational modifications can result in (often subtle) perturbations of functional protein-protein interaction networks, which ultimately determine disease phenotypes. Although many such validated pathogenic proteins have emerged as ideal drug targets, there are also several that escape traditional pharmacological regulation; these proteins have thus been labeled "undruggable". The challenges posed by undruggable targets call for new sorts of molecular intervention. One fascinating solution is to perturb a pathogenic protein's expression levels, rather than blocking its activities. In this Concept paper, we shall discuss chemical interventions aimed at recruiting undruggable proteins to the ubiquitin proteasome system, or aimed at disrupting protein-protein interactions in the chaperone-mediated cellular folding machinery: both kinds of intervention lead to a decrease in the amount of active pathogenic protein expressed. Specifically, we shall discuss the role of computational strategies in understanding the molecular determinants characterizing the function of synthetic molecules typically designed for either type of intervention. Finally, we shall provide our perspectives and views on the current limitations and possibilities to expand the scope of rational approaches to the design of chemical regulators of protein levels.

The Mitochondrial Chaperone TRAP1 as a Candidate Target of Oncotherapy

Tumor necrosis factor receptor-associated protein 1 (TRAP1), a member of the heat shock protein 90 (Hsp90) chaperone family, protects cells against oxidative stress and maintains mitochondrial integrity. To date, numerous studies have focused on understanding the relationship between aberrant TRAP1 expression and tumorigenesis. Mitochondrial TRAP1 is a key regulatory factor involved in metabolic reprogramming in tumor cells that favors the metabolic switch of tumor cells toward the Warburg phenotype. In addition, TRAP1 is involved in dual regulation of the mitochondrial apoptotic pathway and exerts an antiapoptotic effect on tumor cells. Furthermore, TRAP1 is involved in many cellular pathways by disrupting the cell cycle, increasing cell motility, and promoting tumor cell invasion and metastasis. Thus, TRAP1 is a very important therapeutic target, and treatment with TRAP1 inhibitors combined with chemotherapeutic agents may become a new therapeutic strategy for cancer. This review discusses the molecular mechanisms by which TRAP1 regulates tumor progression, considers its role in apoptosis, and summarizes recent advances in the development of selective, targeted TRAP1 and Hsp90 inhibitors.

Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD–ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support in silico mechanistic studies and drug design.

Visualizing the Dynamics of a Protein Folding Machinery: The Mechanism of Asymmetric ATP Processing in Hsp90 and its Implications for Client Remodelling

The Hsp90 chaperone system interacts with a wide spectrum of client proteins, forming variable and dynamic multiprotein complexes that involve the intervention of cochaperone partners. Recent results suggest that the role of Hsp90 complexes is to establish interactions that suppress unwanted client activities, allow clients to be protected from degradation and respond to biochemical signals. Cryo-electron microscopy (cryoEM) provided the first key molecular picture of Hsp90 in complex with a kinase, Cdk4, and a cochaperone, Cdc37. Here, we use a combination of molecular dynamics (MD) simulations and advanced comparative analysis methods to elucidate key aspects of the functional dynamics of the complex, with different nucleotides bound at the N-terminal Domain of Hsp90. The results reveal that nucleotide-dependent structural modulations reverberate in a striking asymmetry of the dynamics of Hsp90 and identify specific patterns of long-range coordination between the nucleotide binding site, the client binding pocket, the cochaperone and the client. Our model establishes a direct atomic-resolution cross-talk between the ATP-binding site, the client region that is to be remodeled and the surfaces of the Cdc37-cochaperone.

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.

Dynamically Shaping Chaperones. Allosteric Modulators of HSP90 Family as Regulatory Tools of Cell Metabolism in Neoplastic Progression

Molecular chaperones have recently emerged as fundamental regulators of salient biological routines, including metabolic adaptations to environmental changes. Yet, many of the molecular mechanisms at the basis of their functions are still unknown or at least uncertain. This is in part due to the lack of chemical tools that can interact with the chaperones to induce measurable functional perturbations. In this context, the use of small molecules as modulators of protein functions has proven relevant for the investigation of a number of biomolecular systems. Herein, we focus on the functions, interactions and signaling pathways of the HSP90 family of molecular chaperones as possible targets for the discovery of new molecular entities aimed at tuning their activity and interactions. HSP90 and its mitochondrial paralog, TRAP1, regulate the activity of crucial metabolic circuitries, making cells capable of efficiently using available energy sources, with relevant implications both in healthy conditions and in a variety of disease states and especially cancer. The design of small-molecules targeting the chaperone cycle of HSP90 and able to inhibit or stimulate the activity of the protein can provide opportunities to finely dissect their biochemical activities and to obtain lead compounds to develop novel, mechanism-based drugs.

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.

mTOR–mLST8 interaction: hot spot identification through quantum biochemistry calculations

Quantum calculation of mTOR–mLST8 interaction.