Predicting cracks in metalloproteins

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

Out-of-Equilibrium Measurements of Kinetic Constants on a Biosensor

Conventional measurements of kinetic constants currently in use are performed at equilibrium and may require large volumes, especially at a low association rate constant k_on. If the measurements are made out of equilibrium, the values obtained may be biased by dilution of the sample with the flow of the running buffer. In some applications, the available sample volume can be very critical and requires the development of tools to measure kinetic constants with low volumes. In this paper, by combining an experimental, numerical and modeling approach, we propose a surface plasmon resonance-based method that relies on an out-of-equilibrium measurement using the effect of dilution by flow to its advantage. This new method should have a significant impact in biochemistry and medical research.