Explore the vast range of titles available.

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.

A recently proposed oxidative damage protection mechanism in proteins relies on hole hopping escape routes formed by redox-active amino acids. We present a computational tool to identify the dominant charge hopping pathways through these residues based on the mean residence times of the transferring charge along these hopping pathways. The residence times are estimated by combining a kinetic model with well-known rate expressions for the charge-transfer steps in the pathways. We identify the most rapid hole hopping escape routes in cytochrome P450 monooxygenase, cytochrome c peroxidase, and benzylsuccinate synthase (BSS). This theoretical analysis supports the existence of hole hopping chains as a mechanism capable of providing hole escape from protein catalytic sites on biologically relevant timescales. Furthermore, we find that pathways involving the [4Fe4S] cluster as the terminal hole acceptor in BSS are accessible on the millisecond timescale, suggesting a potential protective role of redox-active cofactors for preventing protein oxidative damage.

Recent work has highlighted the potential of metallocorroles as versatile platforms for the development of drugs and imaging agents, since the bioavailability, physicochemical properties and therapeutic activity can be dramatically altered by metal ion substitution and/or functional group replacement. Significant advances in cancer treatment and imaging have been reported based on work with a water-soluble bis-sulfonated gallium corrole in both cellular and rodent-based models. We now show that cytotoxicities increase in the order Ga < Fe < Al < Mn < Sb < Au for bis-sulfonated corroles; and, importantly, that they correlate with metallocorrole affinities for very low density lipoprotein (VLDL), the main carrier of lipophilic drugs. As chemotherapeutic potential is predicted to be enhanced by increased lipophilicity, we have developed a novel method for the preparation of cell-penetrating lipophilic metallocorrole/serum-protein nanoparticles (NPs). Cryo-TEM revealed an average core metallocorrole particle size of 32 nm, with protein tendrils extending from the core (conjugate size is ~100 nm). Optical imaging of DU-145 prostate cancer cells treated with corrole NPs (≤100 nM) revealed fast cellular uptake, very slow release, and distribution into the endoplasmic reticulum (ER) and lysosomes. The physical properties of corrole NPs prepared in combination with transferrin and albumin were alike, but the former were internalized to a greater extent by the transferrin-receptor-rich DU-145 cells. Our method of preparation of corrole/protein NPs may be generalizable to many bioactive hydrophobic molecules to enhance their bioavailability and target affinity.