Computational design of metalloproteins

Sn-Triggered Two-Dimensional Fast Protein Assembly with Emergent Functions

The discovery of a general strategy for organizing functional proteins into stable nanostructures with the desired dimension, shape, and function is an important focus in developing protein-based self-assembled materials, but the scalable synthesis of such materials and transfer to other substrates remain great challenges. We herein tackle this issue by creating a two-dimensional metal-protein hybrid nanofilm that is flexible and cost-effective with reliable self-recovery, stability, and multifunctionality. As it differs from traditional metal ions, we discover the capability of Sn²⁺ to initiate fast amyloid-like protein assembly (occurring in seconds) by effectively reducing the disulfide bonds of native globular proteins. The Sn²⁺-initiated lysozyme aggregation at the air/water interface leads to droplet flattening, a result never before reported in a protein system, which finally affords a multifunctional 2D Sn-doped hybrid lysozyme nanofilm with an ultralarge area (e.g., 0.2 m²) within a few minutes. The hybrid film is distinctive in its ease of coating on versatile material surfaces with endurable chemical and mechanical stability, optical transparency, and diverse end uses in antimicrobial and photo-/electrocatalytic scaffolds. Our approach provides not only insights into the effect of tin ions on macroscopic self-assembly of proteins but also a controllable and scalable synthesis of a potential biomimic framework for biomedical and biocatalytic applications.

Dehalogenases: From Improved Performance to Potential Microbial Dehalogenation Applications

The variety of halogenated substances and their derivatives widely used as pesticides, herbicides and other industrial products is of great concern due to the hazardous nature of these compounds owing to their toxicity, and persistent environmental pollution. Therefore, from the viewpoint of environmental technology, the need for environmentally relevant enzymes involved in biodegradation of these pollutants has received a great boost. One result of this great deal of attention has been the identification of environmentally relevant bacteria that produce hydrolytic dehalogenases—key enzymes which are considered cost-effective and eco-friendly in the removal and detoxification of these pollutants. These group of enzymes catalyzing the cleavage of the carbon-halogen bond of organohalogen compounds have potential applications in the chemical industry and bioremediation. The dehalogenases make use of fundamentally different strategies with a common mechanism to cleave carbon-halogen bonds whereby, an active-site carboxylate group attacks the substrate C atom bound to the halogen atom to form an ester intermediate and a halide ion with subsequent hydrolysis of the intermediate. Structurally, these dehalogenases have been characterized and shown to use substitution mechanisms that proceed via a covalent aspartyl intermediate. More so, the widest dehalogenation spectrum of electron acceptors tested with bacterial strains which could dehalogenate recalcitrant organohalides has further proven the versatility of bacterial dehalogenators to be considered when determining the fate of halogenated organics at contaminated sites. In this review, the general features of most widely studied bacterial dehalogenases, their structural properties, basis of the degradation of organohalides and their derivatives and how they have been improved for various applications is discussed.

d-Cysteine Ligands Control Metal Geometries within De Novo Designed Three-Stranded Coiled Coils

Although metal ion binding to naturally occurring l-amino acid proteins is well documented, understanding the impact of the opposite chirality (d-)amino acids on the structure and stereochemistry of metals is in its infancy. We examine the effect of a d-configuration cysteine within a designed l-amino acid three-stranded coiled coil in order to enforce a precise coordination number on a metal center. The d chirality does not alter the native fold, but the side-chain re-orientation modifies the sterics of the metal binding pocket. l-Cys side chains within the coiled-coil structure have previously been shown to rotate substantially from their preferred positions in the apo structure to create a binding site for a tetra-coordinate metal ion. However, here we show by X-ray crystallography that d-Cys side chains are preorganized within a suitable geometry to bind such a ligand. This is confirmed by comparison of the structure of Zn^II Cl(CSL16_D C)₃^2- to the published structure of Zn^II (H₂ O)(GRAND-CSL12AL16_L C)₃^- . Moreover, spectroscopic analysis indicates that the Cd^II geometry observed by using l-Cys ligands (a mixture of three- and four-coordinate Cd^II ) is altered to a single four-coordinate species when d-Cys is present. This work opens a new avenue for the control of the metal site environment in man-made proteins, by simply altering the binding ligand with its mirror-imaged d configuration. Thus, the use of non-coded amino acids in the coordination sphere of a metal promises to be a powerful tool for controlling the properties of future metalloproteins.

The Framework of Computational Protein Design

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein-protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.