Biology subjects

Staats, R.

Publications and source records attributed to Staats, R..

4 recordsLinked to original sources

bioRxiv2024

Global kinetic model of lipid-induced alpha-synuclein aggregation and its inhibition by small molecules

The aggregation of -synuclein into amyloid fibrils is a hallmark of Parkinsons disease. This process has been shown to directly involve interactions between proteins and lipid surfaces when the latter are present. Despite this importance, the molecular mechanisms of lipid-induced amyloid aggregation have remained largely elusive. Here, we present a global kinetic model to describe lipid-induced amyloid aggregation of -synuclein. Using this framework we find that -synuclein fibrils form via a two-step primary nucleation mechanism and that lipid molecules are directly involved in both the nucleation and fibril elongation steps, giving rise to lipid-protein coaggregates. To illustrate the applicability of this kinetic approach to drug discovery, we identify the mechanism of action of squalamine, a known inhibitor of -synuclein aggregation, finding that this small molecule reduces the rate of lipid-dependent primary nucleation. Our work will likely guide the rational design of -synuclein aggregation inhibitors. Significance StatementAmyloid aggregation is a hallmark of a diverse range of diseases including Parkinsons Disease, where the protein -synuclein is a major con-stituent of proteinaceous deposits found in patients. It is well established that interactions between -synuclein and lipids modulate aggregation. However, the molecular mechanisms driving this lipid-induced aggregation have remained largely elusive, which has frustrated so far the discovery of drugs that prevent lipid-induced aggregation. In this work, we present the first global kinetic model describing lipid-induced aggregation and by integrating this theoretical framework with in vitro experimental data of lipid-induced -synuclein aggregation, we reveal the role of lipid membranes in the aggregation process and uncover the mechanism by which small molecule inhibitors interfere with this process.

biophysics↗

bioRxiv2021

Structure-based discovery of small molecule inhibitors of the autocatalytic proliferation of alpha-synuclein aggregates

The presence of amyloid fibrils of -synuclein is closely associated with Parkinsons disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of -synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a combination of kinetic and thermodynamic assays for their ability to bind -synuclein fibrils and prevent the further generation of toxic oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of -synuclein aggregates.

biophysics↗

bioRxiv2021

A Machine Learning Approach to Identify Specific Small Molecule Inhibitors of Secondary Nucleation in alpha-Synuclein Aggregation

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 -synuclein aggregation, a process implicated in Parkinsons disease and other synucleinopathies. Because the proliferation of -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.

biophysics↗

bioRxiv2019

Comparison Between Normobaric Hypoxia Altitude Simulation Test and Altitude Hypoxia Predictive Equations in Cystic Fibrosis Patients

BackgroundThe Hypoxia Altitude Simulation Test (HAST) is the Gold Standard to evaluate hypoxia in response to altitude and to decide on in-flight requirements for oxygen supplementation. Several equations are available to predict PaO2 in altitude (PaO2alt), but it remains unclear whether their predictive value is equivalent. We aimed to compare the results obtained by the available methods in a population of cystic fibrosis (CF) adults.\n\nMethodsEighty-eight adults (58 healthy controls and 30 CF patients) performed a spirometry followed by an HAST. HAST results were compared with the predicted PaO2alt made by five equations: 1st: PaO2alt= 0,410 x PaO2ground + 1,7652; 2nd: PaO2alt= 0,519 x PaO2ground + 11,855 x FEV1 (L) - 1,760; 3rd: PaO2alt= 0,453 x PaO2ground + 0,386 x FEV1 (%) + 2,44; 4th: PaO2alt= 0,88 + 0,68 x PaO2ground; 5th: PaO2alt= PaO2ground - 26,6.\n\nResultsNone of the controls required in-flight oxygen neither by HAST or by the five predictive equations. Eleven CF-patients had PaO2alt < 50 mmHg, accessed by HAST. The positive predictive value was 50% (1st), 87.5% (2nd and 3rd), 77.78% (4th) and 58.33% (5th). Areas under the curve were 78.95% (1st), 84.69% (2nd), 88.04% (3rd) and 78.95% (4th and 5th). FEV1 and PaO2ground were correlated with HAST results.\n\nConclusionsThe 3rd equation gave the best predictions in comparison with results obtained by HAST. However, because the individual differences found were substantial for all equations, we still recommend performing a HAST whenever possible to confidently access in-flight hypoxia and the need for oxygen.

pathology↗