Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates

Pharmacological inhibition of α-synuclein aggregation within liquid condensates

Aggregated forms of α-synuclein constitute the major component of Lewy bodies, the proteinaceous aggregates characteristic of Parkinson's disease. Emerging evidence suggests that α-synuclein aggregation may occur within liquid condensates formed through phase separation. This mechanism of aggregation creates new challenges and opportunities for drug discovery for Parkinson's disease, which is otherwise still incurable. Here we show that the condensation-driven aggregation pathway of α-synuclein can be inhibited using small molecules. We report that the aminosterol claramine stabilizes α-synuclein condensates and inhibits α-synuclein aggregation within the condensates both in vitro and in a Caenorhabditis elegans model of Parkinson's disease. By using a chemical kinetics approach, we show that the mechanism of action of claramine is to inhibit primary nucleation within the condensates. These results illustrate a possible therapeutic route based on the inhibition of protein aggregation within condensates, a phenomenon likely to be relevant in other neurodegenerative disorders.

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

A closer look at amyloid ligands, and what they tell us about protein aggregates

The accumulation of amyloid fibrils is characteristic of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease. Detecting these fibrils with fluorescent or radiolabelled ligands is one strategy for diagnosing and better understanding these diseases. A vast number of amyloid-binding ligands have been reported in the literature as a result. To obtain a better understanding of how amyloid ligands bind, we have compiled a database of 3457 experimental dissociation constants for 2076 unique amyloid-binding ligands. These ligands target Aβ, tau, or αSyn fibrils, as well as relevant biological samples including AD brain homogenates. From this database significant variation in the reported dissociation constants of ligands was found, possibly due to differences in the morphology of the fibrils being studied. Ligands were also found to bind to Aβ(1-40) and Aβ(1-42) fibrils with similar affinities, whereas a greater difference was found for binding to Aβ and tau or αSyn fibrils. Next, the binding of ligands to fibrils was shown to be largely limited by the hydrophobic effect. Some Aβ ligands do not fit into this hydrophobicity-limited model, suggesting that polar interactions can play an important role when binding to this target. Finally several binding site models were outlined for amyloid fibrils that describe what ligands target what binding sites. These models provide a foundation for interpreting and designing site-specific binding assays.

Navigating α-Synuclein Aggregation Inhibition: Methods, Mechanisms, and Molecular Targets

Parkinson's disease is a yet incurable, age-related neurodegenerative disorder characterized by the aggregation of small neuronal protein α-synuclein into amyloid fibrils. Inhibition of this process is a prospective strategy for developing a disease-modifying treatment. We overview here small molecule, peptide, and protein inhibitors of α-synuclein fibrillization reported to date. Special attention was paid to the specificity of inhibitors and critical analysis of their action mechanisms. Namely, the importance of oxidation of polyphenols and cross-linking of α-synuclein into inhibitory dimers was highlighted. We also compared strategies of targeting monomeric, oligomeric, and fibrillar α-synuclein species, thoroughly discussed the strong and weak sides of different approaches to testing the inhibitors.

Ultrastructures of α-Synuclein Filaments in Synucleinopathy Brains and Experimental Models

Intracellular α-synuclein (α-syn) inclusions are a neuropathological hallmark of Lewy body disease (LBD) and multiple system atrophy (MSA), both of which are termed synucleinopathies. LBD is defined by Lewy bodies and Lewy neurites in neurons, while MSA displays glial cytoplasmic inclusions in oligodendrocytes. Pathological α-syn adopts an ordered filamentous structure with a 5-10 nm filament diameter, and this conformational change has been suggested to be involved in the disease onset and progression. Synucleinopathies also exhibit characteristic ultrastructural and biochemical properties of α-syn filaments, and α-syn strains with distinct conformations have been identified. Numerous experimental studies have supported the idea that pathological α-syn self-amplifies and spreads throughout the brain, during which processes the conformation of α-syn filaments may drive the disease specificity. In this review, we summarize the ultrastructural features and heterogeneity of α-syn filaments in the brains of patients with synucleinopathy and in experimental models of seeded α-syn aggregation.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Passive Immunotherapies Targeting Amyloid-β in Alzheimer's Disease: A Quantitative Systems Pharmacology Perspective

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by amyloid-β (Aβ) protein accumulation in the brain. Passive immunotherapies using monoclonal antibodies for targeting Aβ have shown promise for AD treatment. Indeed, recent US Food and Drug Administration approval of aducanumab and lecanemab, alongside positive donanemab Phase III results demonstrated clinical efficacy after decades of failed clinical trials for AD. However, the pharmacological basis distinguishing clinically effective from ineffective therapies remains unclear, impeding development of potent therapeutics. This study aimed to provide a quantitative perspective for effectively targeting Aβ with antibodies. We first reviewed the contradicting results associated with the amyloid hypothesis and the pharmacological basis of Aβ immunotherapy. Subsequently, we developed a quantitative systems pharmacology (QSP) model that describes the non-linear progression of Aβ pathology and the pharmacologic actions of the Aβ-targeting antibodies. Using the QSP model, we analyzed various scenarios for effective passive immunotherapy for AD. The model revealed that binding exclusively to the Aβ monomer has minimal effect on Aβ aggregation and plaque reduction, making the antibody affinity toward Aβ monomer unwanted, as it could become a distractive mechanism for plaque reduction. Neither early intervention, high brain penetration, nor increased dose could yield significant improvement of clinical efficacy for antibodies targeting solely monomers. Antibodies that bind all Aβ species but lack effector function exhibited moderate effects in plaque reduction. Our model highlights the importance of binding aggregate Aβ species and incorporating effector functions for efficient and early plaque reduction, guiding the development of more effective therapies for this devastating disease. SIGNIFICANCE STATEMENT: Despite previous unsuccessful attempts spanning several decades, passive immunotherapies utilizing monoclonal antibodies for targeting amyloid-beta (Aβ) have demonstrated promise with two recent FDA approvals. However, the pharmacological basis that differentiates clinically effective therapies from ineffective ones remains elusive. Our study offers a quantitative systems pharmacology perspective, emphasizing the significance of selectively targeting specific Aβ species and importance of antibody effector functions. This perspective sheds light on the development of more effective therapies for this devastating disease.

Multiplexed Digital Characterization of Misfolded Protein Oligomers via Solid-State Nanopores

Misfolded protein oligomers are of central importance in both the diagnosis and treatment of Alzheimer's and Parkinson's diseases. However, accurate high-throughput methods to detect and quantify oligomer populations are still needed. We present here a single-molecule approach for the detection and quantification of oligomeric species. The approach is based on the use of solid-state nanopores and multiplexed DNA barcoding to identify and characterize oligomers from multiple samples. We study α-synuclein oligomers in the presence of several small-molecule inhibitors of α-synuclein aggregation as an illustration of the potential applicability of this method to the development of diagnostic and therapeutic methods for Parkinson's disease.

Secondary Processes Dominate the Quiescent, Spontaneous Aggregation of α-Synuclein at Physiological pH with Sodium Salts

The accurate recapitulation in an in vitro assay of the aggregation process of α-synuclein in Parkinson's disease has been a significant challenge. As α-synuclein does not aggregate spontaneously in most currently used in vitro assays, primary nucleation is triggered by the presence of surfaces such as lipid membranes or interfaces created by shaking, to achieve aggregation on accessible time scales. In addition, secondary nucleation is typically only observed by lowering the pH below 5.8. Here we investigated assay conditions that enables spontaneous primary nucleation and secondary nucleation at pH 7.4. Using 400 mM sodium phosphate, we observed quiescent spontaneous aggregation of α-synuclein and established that this aggregation is dominated by secondary processes. Furthermore, the presence of potassium ions enhanced the reproducibility of quiescent α-synuclein aggregation. This work provides a framework for the study of spontaneous α-synuclein aggregation at physiological pH.

Optimization of a small molecule inhibitor of secondary nucleation in α-synuclein aggregation

Parkinson's disease is characterised by the deposition in the brain of amyloid aggregates of α-synuclein. The surfaces of these amyloid aggregates can catalyse the formation of new aggregates, giving rise to a positive feedback mechanism responsible for the rapid proliferation of α-synuclein deposits. We report a procedure to enhance the potency of a small molecule to inhibit the aggregate proliferation process using a combination of in silico and in vitro methods. The optimized small molecule shows potency already at a compound:protein stoichiometry of 1:20. These results illustrate a strategy to accelerate the optimisation of small molecules against α-synuclein aggregation by targeting secondary nucleation.

Inhibition of Protein Aggregation and Endoplasmic Reticulum Stress as a Targeted Therapy for α-Synucleinopathy

α-synuclein (α-syn) is an intrinsically disordered protein abundant in the central nervous system. Physiologically, the protein regulates vesicle trafficking and neurotransmitter release in the presynaptic terminals. Pathologies related to misfolding and aggregation of α-syn are referred to as α-synucleinopathies, and they constitute a frequent cause of neurodegeneration. The most common α-synucleinopathy, Parkinson's disease (PD), is caused by abnormal accumulation of α-syn in the dopaminergic neurons of the midbrain. This results in protein overload, activation of endoplasmic reticulum (ER) stress, and, ultimately, neural cell apoptosis and neurodegeneration. To date, the available treatment options for PD are only symptomatic and rely on dopamine replacement therapy or palliative surgery. As the prevalence of PD has skyrocketed in recent years, there is a pending issue for development of new disease-modifying strategies. These include anti-aggregative agents that target α-syn directly (gene therapy, small molecules and immunization), indirectly (modulators of ER stress, oxidative stress and clearance pathways) or combine both actions (natural compounds). Herein, we provide an overview on the characteristic features of the structure and pathogenic mechanisms of α-syn that could be targeted with novel molecular-based therapies.

Thermodynamic and kinetic approaches for drug discovery to target protein misfolding and aggregation

INTRODUCTION

Protein misfolding diseases, including Alzheimer's and Parkinson's diseases, are characterized by the aberrant aggregation of proteins. These conditions are still largely untreatable, despite having a major impact on our healthcare systems and societies.

AREAS COVERED

We describe drug discovery strategies to target protein misfolding and aggregation. We compare thermodynamic approaches, which are based on the stabilization of the native states of proteins, with kinetic approaches, which are based on the slowing down of the aggregation process. This comparison is carried out in terms of the current knowledge of the process of protein misfolding and aggregation, the mechanisms of disease and the therapeutic targets.

EXPERT OPINION

There is an unmet need for disease-modifying treatments that target protein misfolding and aggregation for the over 50 human disorders known to be associated with this phenomenon. With the approval of the first drugs that can prevent misfolding or inhibit aggregation, future efforts will be focused on the discovery of effective compounds with these mechanisms of action for a wide range of conditions.

Exploration and Exploitation Approaches Based on Generative Machine Learning to Identify Potent Small Molecule Inhibitors of α-Synuclein Secondary Nucleation

The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson's disease, for which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement learning to identify small molecules that perturb the kinetics of aggregation in a manner that reduces the production of oligomeric species. Training data were obtained by an assay reporting on the degree of inhibition of secondary nucleation, which is the most important mechanism of α-synuclein oligomer production. This approach resulted in the identification of small molecules with high potency against secondary nucleation.

Development of Small Molecules Targeting α-Synuclein Aggregation: A Promising Strategy to Treat Parkinson’s Disease

Parkinson’s disease, the second most common neurodegenerative disorder worldwide, is characterized by the accumulation of protein deposits in the dopaminergic neurons. These deposits are primarily composed of aggregated forms of α-Synuclein (α-Syn). Despite the extensive research on this disease, only symptomatic treatments are currently available. However, in recent years, several compounds, mainly of an aromatic character, targeting α-Syn self-assembly and amyloid formation have been identified. These compounds, discovered by different approaches, are chemically diverse and exhibit a plethora of mechanisms of action. This work aims to provide a historical overview of the physiopathology and molecular aspects associated with Parkinson’s disease and the current trends in small compound development to target α-Syn aggregation. Although these molecules are still under development, they constitute an important step toward discovering effective anti-aggregational therapies for Parkinson’s disease.

Protein dynamics: The future is bright and complicated!

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