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.

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.

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.

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.

Selenium Silk Nanostructured Films with Antifungal and Antibacterial Activity.

The rapid emergence of drug-resistant bacteria and fungi poses a threat for healthcare worldwide. The development of novel effective small molecule therapeutic strategies in this space has remained challenging. Therefore, one orthogonal approach is to explore biomaterials with physical modes of action that have the potential to generate antimicrobial activity and, in some cases, even prevent antimicrobial resistance. Here, to this effect, we describe an approach for forming silk-based films that contain embedded selenium nanoparticles. We show that these materials exhibit both antibacterial and antifungal properties while crucially also remaining highly biocompatible and noncytotoxic toward mammalian cells. By incorporating the nanoparticles into silk films, the protein scaffold acts in a 2-fold manner; it protects the mammalian cells from the cytotoxic effects of the bare nanoparticles, while also providing a template for bacterial and fungal eradication. A range of hybrid inorganic/organic films were produced and an optimum concentration was found, which allowed for both high bacterial and fungal death while also exhibiting low mammalian cell cytotoxicity. Such films can thus pave the way for next-generation antimicrobial materials for applications such as wound healing and as agents against topical infections, with the added benefit that bacteria and fungi are unlikely to develop antimicrobial resistance to these hybrid materials.

Activity, stability and 3-D structure of the Cu(ii) form of a chitin-active lytic polysaccharide monooxygenase from Bacillus amyloliquefaciens.

The enzymatic deconstruction of recalcitrant polysaccharide biomass is central to the conversion of these substrates for societal benefit, such as in biofuels. Traditional models for enzyme-catalysed polysaccharide degradation involved the synergistic action of endo-, exo- and processive glycoside hydrolases working in concert to hydrolyse the substrate. More recently this model has been succeeded by one featuring a newly discovered class of mononuclear copper enzymes: lytic polysaccharide monooxygenases (LPMOs; classified as Auxiliary Activity (AA) enzymes in the CAZy classification). In 2013, the structure of an LPMO from Bacillus amyloliquefaciens, BaAA10, was solved with the Cu centre photoreduced to Cu(i) in the X-ray beam. Here we present the catalytic activity of BaAA10. We show that it is a chitin-active LPMO, active on both α and ß chitin, with the Cu(ii) binding with low nM KD, and the substrate greatly increasing the thermal stability of the enzyme. A spiral data collection strategy has been used to facilitate access to the previously unobservable Cu(ii) state of the active centre, revealing a coordination geometry around the copper which is distorted from axial symmetry, consistent with the previous findings from EPR spectroscopy.

A novel thermostable xylanase GH10 from Malbranchea pulchella expressed in Aspergillus nidulans with potential applications in biotechnology.

BACKGROUND: The search for novel thermostable xylanases for industrial use has intensified in recent years, and thermophilic fungi are a promising source of useful enzymes. The present work reports the heterologous expression and biochemical characterization of a novel thermostable xylanase (GH10) from the thermophilic fungus Malbranchea pulchella, the influence of glycosylation on its stability, and a potential application in sugarcane bagasse hydrolysis. RESULTS: Xylanase MpXyn10A was overexpressed in Aspergillus nidulans and was active against birchwood xylan, presenting an optimum activity at pH 5.8 and 80°C. MpXyn10A was 16% glycosylated and thermostable, preserving 85% activity after 24 hours at 65°C, and deglycosylation did not affect thermostability. Circular dichroism confirmed the high alpha-helical content consistent with the canonical GH10 family (ß/α)8 barrel fold observed in molecular modeling. Primary structure analysis revealed the existence of eight cysteine residues which could be involved in four disulfide bonds, and this could explain the high thermostability of this enzyme even in the deglycosylated form. MpXyn10A showed promising results in biomass degradation, increasing the amount of reducing sugars in bagasse in natura and in three pretreated sugarcane bagasses. CONCLUSIONS: MpXyn10A was successfully secreted in Aspergillus nidulans, and a potential use for sugarcane bagasse biomass degradation was demonstrated.

The copper active site of CBM33 polysaccharide oxygenases.

The capacity of metal-dependent fungal and bacterial polysaccharide oxygenases, termed GH61 and CBM33, respectively, to potentiate the enzymatic degradation of cellulose opens new possibilities for the conversion of recalcitrant biomass to biofuels. GH61s have already been shown to be unique metalloenzymes containing an active site with a mononuclear copper ion coordinated by two histidines, one of which is an unusual τ-N-methylated N-terminal histidine. We now report the structural and spectroscopic characterization of the corresponding copper CBM33 enzymes. CBM33 binds copper with high affinity at a mononuclear site, significantly stabilizing the enzyme. X-band EPR spectroscopy of Cu(II)-CBM33 shows a mononuclear type 2 copper site with the copper ion in a distorted axial coordination sphere, into which azide will coordinate as evidenced by the concomitant formation of a new absorption band in the UV/vis spectrum at 390 nm. The enzyme's three-dimensional structure contains copper, which has been photoreduced to Cu(I) by the incident X-rays, confirmed by X-ray absorption/fluorescence studies of both aqueous solution and intact crystals of Cu-CBM33. The single copper(I) ion is ligated in a T-shaped configuration by three nitrogen atoms from two histidine side chains and the amino terminus, similar to the endogenous copper coordination geometry found in fungal GH61.