HSPB6: A lipid-dependent molecular chaperone inhibits α-synuclein aggregation.

The process of protein misfolding and aggregation is associated with various cytotoxic effects. Understanding how this phenomenon is regulated by the protein homeostasis system, however, is difficult, since it takes place through a complex non-linear network of coupled microscopic steps, including primary nucleation, fibril elongation, and secondary nucleation, which depend on environmental factors. To address this problem, we studied how the aggregation of α-synuclein, a protein associated with Parkinson's disease, is modulated by molecular chaperones and lipid membranes. We focused on small heat shock proteins (sHSPs/HSPBs), which interact with proteins and lipids and are upregulated during aging, a major risk factor for protein misfolding diseases. HSPBs act on different microscopic steps to prevent α-synuclein aggregation, with HSPB6 showing a lipid-dependent chaperone activity. Our findings provide an example of how HSPBs diversified their mechanisms of action to reach an efficient regulation of protein misfolding and aggregation within the complex cellular environment.

Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method.

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.

AlphaFold2-based prediction of the co-condensation propensity of proteins.

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

α-Synuclein oligomers form by secondary nucleation.

Oligomeric species arising during the aggregation of α-synuclein are implicated as a major source of toxicity in Parkinson's disease, and thus a major potential drug target. However, both their mechanism of formation and role in aggregation are largely unresolved. Here we show that, at physiological pH and in the absence of lipid membranes, α-synuclein aggregates form by secondary nucleation, rather than simple primary nucleation, and that this process is enhanced by agitation. Moreover, using a combination of single molecule and bulk level techniques, we identify secondary nucleation on the surfaces of existing fibrils, rather than formation directly from monomers, as the dominant source of oligomers. Our results highlight secondary nucleation as not only the key source of oligomers, but also the main mechanism of aggregate formation, and show that these processes take place under conditions which recapitulate the neutral pH and ionic strength of the cytosol.

Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery.

Recent advances in machine learning methods for materials science have significantly enhanced accurate predictions of the properties of novel materials. Here, we explore whether these advances can be adapted to drug discovery by addressing the problem of prospective validation - the assessment of the performance of a method on out-of-distribution data. First, we tested whether k-fold n-step forward cross-validation could improve the accuracy of out-of-distribution small molecule bioactivity predictions. We found that it is more helpful than conventional random split cross-validation in describing the accuracy of a model in real-world drug discovery settings. We also analyzed discovery yield and novelty error, finding that these two metrics provide an understanding of the applicability domain of models and an assessment of their ability to predict molecules with desirable bioactivity compared to other small molecules. Based on these results, we recommend incorporating a k-fold n-step forward cross-validation and these metrics when building state-of-the-art models for bioactivity prediction in drug discovery.

Preparation and Characterization of Zn(II)-Stabilized Aß42 Oligomers.

Aß oligomers are being investigated as cytotoxic agents in Alzheimer's disease (AD). Because of their transient nature and conformational heterogeneity, the relationship between the structure and activity of these oligomers is still poorly understood. Hence, methods for stabilizing Aß oligomeric species relevant to AD are needed to uncover the structural determinants of their cytotoxicity. Here, we build on the observation that metal ions and metabolites have been shown to interact with Aß, influencing its aggregation and stabilizing its oligomeric species. We thus developed a method that uses zinc ions, Zn(II), to stabilize oligomers produced by the 42-residue form of Aß (Aß42), which is dysregulated in AD. These Aß42-Zn(II) oligomers are small in size, spanning the 10-30 nm range, stable at physiological temperature, and with a broad toxic profile in human neuroblastoma cells. These oligomers offer a tool to study the mechanisms of toxicity of Aß oligomers in cellular and animal AD models.

The αC-ß4 loop controls the allosteric cooperativity between nucleotide and substrate in the catalytic subunit of protein kinase A.

Allosteric cooperativity between ATP and substrates is a prominent characteristic of the cAMP-dependent catalytic subunit of protein kinase A (PKA-C). This long-range synergistic action is involved in substrate recognition and fidelity, and it may also regulate PKA's association with regulatory subunits and other binding partners. To date, a complete understanding of this intramolecular mechanism is still lacking. Here, we integrated NMR(Nuclear Magnetic Resonance)-restrained molecular dynamics simulations and a Markov State Model to characterize the free energy landscape and conformational transitions of PKA-C. We found that the apoenzyme populates a broad free energy basin featuring a conformational ensemble of the active state of PKA-C (ground state) and other basins with lower populations (excited states). The first excited state corresponds to a previously characterized inactive state of PKA-C with the αC helix swinging outward. The second excited state displays a disrupted hydrophobic packing around the regulatory (R) spine, with a flipped configuration of the F100 and F102 residues at the αC-ß4 loop. We validated the second excited state by analyzing the F100A mutant of PKA-C, assessing its structural response to ATP and substrate binding. While PKA-CF100A preserves its catalytic efficiency with Kemptide, this mutation rearranges the αC-ß4 loop conformation, interrupting the coupling of the two lobes and abolishing the allosteric binding cooperativity. The highly conserved αC-ß4 loop emerges as a pivotal element to control the synergistic binding of nucleotide and substrate, explaining how mutations or insertions near or within this motif affect the function and drug sensitivity in homologous kinases.

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.

RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds.

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

A Relationship between the Structures and Neurotoxic Effects of Aß Oligomers Stabilized by Different Metal Ions.

Oligomeric assemblies of the amyloid ß peptide (Aß) have been investigated for over two decades as possible neurotoxic agents in Alzheimer's disease. However, due to their heterogeneous and transient nature, it is not yet fully established which of the structural features of these oligomers may generate cellular damage. Here, we study distinct oligomer species formed by Aß40 (the 40-residue form of Aß) in the presence of four different metal ions (Al3+, Cu2+, Fe2+, and Zn2+) and show that they differ in their structure and toxicity in human neuroblastoma cells. We then describe a correlation between the size of the oligomers and their neurotoxic activity, which provides a type of structure-toxicity relationship for these Aß40 oligomer species. These results provide insight into the possible role of metal ions in Alzheimer's disease by the stabilization of Aß oligomers.

Misfolded protein oligomers: mechanisms of formation, cytotoxic effects, and pharmacological approaches against protein misfolding diseases.

The conversion of native peptides and proteins into amyloid aggregates is a hallmark of over 50 human disorders, including Alzheimer's and Parkinson's diseases. Increasing evidence implicates misfolded protein oligomers produced during the amyloid formation process as the primary cytotoxic agents in many of these devastating conditions. In this review, we analyze the processes by which oligomers are formed, their structures, physicochemical properties, population dynamics, and the mechanisms of their cytotoxicity. We then focus on drug discovery strategies that target the formation of oligomers and their ability to disrupt cell physiology and trigger degenerative processes.

Maturation-dependent changes in the size, structure and seeding capacity of Aß42 amyloid fibrils.

Many proteins self-assemble to form amyloid fibrils, which are highly organized structures stabilized by a characteristic cross-ß network of hydrogen bonds. This process underlies a variety of human diseases and can be exploited to develop versatile functional biomaterials. Thus, protein self-assembly has been widely studied to shed light on the properties of fibrils and their intermediates. A still open question in the field concerns the microscopic processes that underlie the long-time behaviour and properties of amyloid fibrillar assemblies. Here, we use atomic force microscopy with angstrom-sensitivity to observe that amyloid fibrils undergo a maturation process, associated with an increase in both fibril length and thickness, leading to a decrease of their density, and to a change in their cross-ß sheet content. These changes affect the ability of the fibrils to catalyse the formation of new aggregates. The identification of these changes helps us understand the fibril maturation processes, facilitate the targeting of amyloid fibrils in drug discovery, and offer insight into the development of biocompatible and sustainable protein-based materials.

Modulation of α-synuclein in vitro aggregation kinetics by its alternative splice isoforms.

The misfolding and aggregation of α-synuclein is linked to a family of neurodegenerative disorders known as synucleinopathies, the most prominent of which is Parkinson's disease (PD). Understanding the aggregation process of α-synuclein from a mechanistic point of view is thus of key importance. SNCA, the gene encoding α-synuclein, comprises six exons and produces various isoforms through alternative splicing. The most abundant isoform is expressed as a 140-amino acid protein (αSyn-140), while three other isoforms, αSyn-126, αSyn-112, and αSyn-98, are generated by skipping exon 3, exon 5, or both exons, respectively. In this study, we performed a detailed biophysical characterization of the aggregation of these four isoforms. We found that αSyn-112 and αSyn-98 exhibit accelerated aggregation kinetics compared to αSyn-140 and form distinct aggregate morphologies, as observed by transmission electron microscopy. Moreover, we observed that the presence of relatively small amounts of αSyn-112 accelerates the aggregation of αSyn-140, significantly reducing the aggregation half-time. These results indicate a potential role of alternative splicing in the pathological aggregation of α-synuclein and provide insights into how this process could be associated with the development of synucleinopathies.

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.

Bayesian Optimization in the Latent Space of a Variational Autoencoder for the Generation of Selective FLT3 Inhibitors.

The process of drug design requires the initial identification of compounds that bind their targets with high affinity and selectivity. Advances in generative modeling of small molecules based on deep learning are offering novel opportunities for making this process faster and cheaper. Here, we propose an approach to achieve this goal, where predictions of binding affinity are used in conjunction with the Junction Tree Variational Autoencoder (JTVAE) whose latent space is used to facilitate the efficient exploration of the chemical space using a Bayesian optimization strategy. The exploration identifies small molecules predicted to have both high affinity and high selectivity by using an objective function that optimizes the binding to the target while penalizing the binding to off-targets. The framework is demonstrated for FMS-like tyrosine kinase 3 (FLT3) and shown to predict small molecules with predicted affinity and selectivity comparable to those of clinically approved drugs for this target.

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.

Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .

Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation.

The phenomenon of protein phase separation (PPS) underlies a wide range of cellular functions. Correspondingly, the dysregulation of the PPS process has been associated with numerous human diseases. To enable therapeutic interventions based on the regulation of this association, possible targets should be identified. For this purpose, we present an approach that combines the multiomic PandaOmics platform with the FuzDrop method to identify PPS-prone disease-associated proteins. Using this approach, we prioritize candidates with high PandaOmics and FuzDrop scores using a profiling method that accounts for a wide range of parameters relevant for disease mechanism and pharmacological intervention. We validate the differential phase separation behaviors of three predicted Alzheimer's disease targets (MARCKS, CAMKK2, and p62) in two cell models of this disease. Overall, the approach that we present generates a list of possible therapeutic targets for human diseases associated with the dysregulation of the PPS process.

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.