Biology subjects

Horne, R. I.

Publications and source records attributed to Horne, R. I..

4 recordsLinked to original sources

bioRxiv2023

Molecular mechanism of α-synuclein aggregation on lipid membranes revealed.

The central hallmark of Parkinsons disease pathology is the aggregation of the -synuclein protein, which, in its healthy form, is associated with lipid membranes. Purified monomeric -synuclein is relatively stable in vitro, but its aggregation can be triggered by the presence of lipid vesicles. Despite this central importance of lipids in the context of -synuclein aggregation, their mechanistic role in this process has not been established to date. Here, we use chemical kinetics to develop a detailed mechanistic model that is able to globally describe the aggregation behaviour of -synuclein in the presence of DMPS lipid vesicles, across a range of lipid and protein concentrations. Through the application of our kinetic model to experimental data, we find that the reaction is a co-aggregation process involving both protein and lipids and that lipids promote aggregation predominantly by enabling the elongation process. Moreover, we find that the initial formation of aggregates, via primary nucleation, takes place not on the surface of lipid vesicles but at the interfaces present in vitro. Our model will enable mechanistic insights, also in other lipid-protein co-aggregation systems, which will be crucial in the rational design of drugs that inhibit aggregate formation and act at the key points in the -synuclein aggregation cascade.

biophysics↗

bioRxiv2023

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

Misfolded protein oligomers are of central importance in both the detection and treatment of Alzheimers and Parkinsons diseases. However, accurate high-throughput methods to identify and quantify oligomer populations are currently lacking. We present here a single-molecule approach for the detection of oligomeric species. The approach is based on the use of solid state nanopores and multiplexed DNA barcoding to identify and characterise oligomers from multiple samples. We study -synuclein oligomers in the presence of several small molecule inhibitors of -synuclein aggregation, as an illustration of the applicability of this method to assist the development of diagnostic and therapeutic methods for Parkinsons disease.

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↗