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.

Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates.

The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's 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 α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein 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 α-synuclein aggregates.

Multiscale Transient Absorption Study of the Fluorescent Protein Dreiklang and Two Point Variants Provides Insight into Photoswitching and Nonproductive Reaction Pathways.

Dreiklang is a reversibly photoswitchable fluorescent protein used as a probe in advanced fluorescence imaging. It undergoes a unique and still poorly understood photoswitching mechanism based on the reversible addition of a water molecule to the chromophore. We report the first comprehensive study of the dynamics of this reaction by transient absorption spectroscopy from 100 fs to seconds in the original Dreiklang protein and two point variants. The picture that emerges from our work is that of a competition between photoswitching and nonproductive reaction pathways. We found that photoswitching had a low quantum yield of 0.4%. It involves electron transfer from a tyrosine residue (Tyr203) to the chromophore and is completed in 33 ns. Nonproductive deactivation pathways comprise recombination of a charge transfer intermediate, excited-state proton transfer from the chromophore to a histidine residue (His145), and decay to the ground state via micro-/millisecond-lived intermediates.