Ionic Liquid-Induced Modulation of Ubiquitin Stability: The Dominant Role of Hydrophobic Interactions.

Despite the widespread use of imidazolium-based ionic liquids (ILs) in biotechnology, pharmaceuticals, and green chemistry, their detailed interactions with proteins, particularly affecting structural stability, remain poorly understood. This study examines the effects of ILs on ubiquitin, a thermodynamically robust protein with a β-grasp structure. We found that IL-induced destabilization follows a consistent order with previous findings: [BMIM] > [BMPyr] > [EMIM] for cations and [BF] > [MeSO] > [Cl] for anions.

Protein Silencing with Self-Peptides.

Designing functional molecules which can recognize and modify the activity of a specific protein is a frequently encountered challenge in biology and pharmaceutical chemistry, and requires major effort for each specific protein target. Here we demonstrate that "self-peptides", parts of folded proteins which by their nature are recognizable by the rest of the protein, provide a general route to developing such molecules. Such a synthetic peptide with a chemically prestabilized conformation can incorporate into the target protein during its folding, and can potentially displace its native counterpart to cause functional deficits.

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.

The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known.

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space.

The question of molecular similarity is core in cheminformatics and is usually assessed via a comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity.