Leuschner, L. | Search bioRxiv

bioRxiv2026

Solvation Shapes the Conformational Landscape of a Therapeutically Relevant SMN2 Splice-Site Defect

The SMN2 exon 7 5 splice-site/U1 snRNA duplex contains an A-1 bulge that weakens splice-site recognition and represents a therapeutically relevant RNA connectivity defect, yet its conformational landscape and coupling to solvation remain poorly understood. Here, we performed enhanced-sampling Hamiltonian replica-exchange molecular dynamics simulations of the SMN2 splice-site duplex using four explicit-solvent models (OPC, TIP4P-Ew, TIP3P, and SPC/E) and characterized the sampled ensemble using linear and machine-learned latent representations. Across representations, the A-1 defect consistently populated three metastable conformational states distinguished by local duplex geometry, base stacking, hydrogen-bonding patterns, and solvent exposure. The relative populations of these states, together with first-shell hydration and Na+ distributions around the defect, varied substantially across water models, demonstrating that hydration and ion organization actively shape the equilibrium between locally accommodated and solvent-exposed conformations of the SMN2 splice-site bulge. Our results shed light on the conformational components of this therapeutic RNA target and highlight the impact of solvation model as an important consideration for molecular simulations of RNA splice-site recognition and small-molecule repair.

biophysics↗

bioRxiv2023

FSL-CP: A Benchmark for Small Molecule Activity Few-Shot Prediction using Cell Microscopy Images

Predicting small molecule activities using information from high-throughput microscopy images has been shown to tremendously increase hit rates and chemical diversity of the hits in previous drug discovery projects. However, due to high cost of acquiring data or ethical reasons, data sparsity remains a big challenge in drug discovery. This opens up the opportunity for few-shot prediction: fine-tuning a model on a low-data assay of interest after pretraining on other more populated assays. Previous efforts have been made to establish a benchmark for few-shot learning of molecules based on molecular structure. With cell images as a molecular representation, methods in the computer vision domain are also applicable for activity prediction. In this paper, we make two contributions: a) A public data set for few-shot learning with cell microscopy images for the scientific community, b) A range of baseline models encompassing different existing single-task, multi-task and meta-learning approaches.

pharmacology and toxicology↗