Biology subjects

Horne, R. I.

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

7 recordsLinked to original sources

bioRxiv2025

Structure-based Generation of a Secondary Nucleation Inhibitor in alpha-Synuclein Aggregation Using a Conditional Diffusion Model

The process of -synuclein aggregation results in the formation of amyloid fibrils, which accumulate in the brain of patients affected by Parkinsons disease. Among possible therapeutic strategies to cure this disease, one approach is based on the development of compounds capable of inhibiting -synuclein aggregation. An effective inhibition could be achieved by blocking the nucleation sites on the surface of the amyloid fibrils that are responsible for their autocatalytic proliferation. Here, we report a strategy based on deep learning to achieve this goal, which uses an E(3)-equivariant conditional diffusion model. By using this approach, we designed and tested experimentally candidate small molecules. We found that one of these small molecules acts as a potent inhibitor of secondary nucleation in -synuclein aggregation. These results provide evidence that generative diffusion models offer effective tools for drug design.

biophysics↗

bioRxiv2025

Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay

The aggregation of tau into amyloid fibrils is associated with Alzheimers disease (AD) and related tauopathies. Since different tauopathies are characterised by the formation of distinct tau fibril morphologies, it is important to combine the search of tau aggregation inhibitors with the development of in vitro tau aggregation assays that recapitulate aggregation as it may occur in the brain. Here we address this problem by reporting an in vitro tau aggregation assay in which AD brain homogenates are used to seed the generation of first-generation tau fibrils in a polymorph-specific manner under quiescent conditions. These fibrils are then used to create amyloid seed libraries from which second-generation kinetic assays can be readily performed. Using this strategy, we illustrate an iterative machine learning method for the identification of small molecules for the polymorph-specific inhibition of the in vitro formation of tau fibrils. We further show that the small molecules selected by this procedure are potent inhibitors in a Drosophila tauopathy model.

biophysics↗

bioRxiv2023

Sequence-based drug design using transformers

Protein-ligand interactions play central roles in biological processes and are of key importance in drug design. Deep learning-based approaches are emerging as cost-effective alternatives to high-throughput experimental methods for the screening of large libraries of ligands. Here, to predict the binding affinity between proteins and small molecules, we introduce Ligand-Transformer, a deep learning framework based on the AlphaFold2 transformer architecture. We applied Ligand-Transformer to screen inhibitors targeting the mutant EGFRLTC kinase, identifying compounds with low nanomolar potency. We then used this approach to predict the conformational population shifts induced by ABL kinase inhibitors. To show the applicability of Ligand-Transformer to disordered proteins, we explored the binding of small molecules to the Alzheimers A{beta} peptide, identifying compounds that delayed its aggregation. Overall, Ligand-Transformer illustrates the potential of transformers in accurately predicting the interactions of small molecules with both ordered and disordered proteins, thus uncovering molecular mechanisms and facilitating the initial steps in drug discovery.

bioinformatics↗

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↗