Shurrush, K. | Search bioRxiv

bioRxiv2023

Lowering mutant huntingtin by small molecules relieves Huntington's disease symptoms and progression

Huntingtons disease (HD) is an incurable inherited disorder caused by repeat expansion in the huntingtin gene (Htt). The mutant protein causes neuronal degeneration leading to severe motor and psychological abnormalities. Selective downregulation of the mutant Htt expression is considered the leading therapeutic approach for HD. We report the identification of novel small molecule inhibitors of Spt5-Pol II, SPI-24 and SPI-77, which selectively lower mutant Htt mRNA and protein levels in HD cells. In the BACHD mouse model, their direct delivery to the striatum diminished mutant Htt levels, ameliorated mitochondrial dysfunction, restored BDNF expression and improved motor and anxious-like phenotypes. Pharmacokinetic studies revealed that these SPIs pass the blood-brain-barrier and prolonged subcutaneous injection or oral administration to early-stage mice significantly delayed disease deterioration. SPI-24 long-term treatment had no side effects or global changes in gene expression. Thus, lowering mutant Htt levels by small molecules can be an effective therapeutic strategy for HD.

molecular biology↗

bioRxiv2022

Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes

The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem - identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders - and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function.

bioinformatics↗