The interaction of metallic species with proteins has been fundamental in evolution and is key in many physiological processes. How metals bind to proteins also holds promise in many fields, such as the design of new biocatalysts or the fight against pathogens. Nonetheless, uncovering the mechanism under which proteins recruit metal ions is far from understood and is one of the challenges in bioinorganic chemistry and structural biology. Computational methods are among the most promising tools for this endeavor. Only a handful of efficient structural predictors of metal binding sites exist. Most of the work focuses on identifying the most stable binding sites in the protein scaffolds. Although these methods are interesting, they do not consider the exploration of transient, suboptimal binding sites that could be relevant in metal binding pathways in proteins. At the far end of modeling capabilities nowadays, we introduce BioBrigit, a hybrid Machine Learningknowledge-based approach that suggests metal binding pathways in proteins. To demonstrate the method's viability, we apply it to the dicopper tyrosinase from Streptomyces castaneoglobisporus, a system for which crystallographic experiments allowed the identification of a series of transient sites of the copper in its path from a chaperone to the final catalytic site. Combined with homology modeling and large-scale molecular dynamics, BioBrigit allows for computational characterization of all experimental sites and a better understanding of the copper recruitment mechanism. BioBrigit appears as an asset in a field full of unknowns such as metal binding to proteins and opens the way to further algorithms in this area.

Among the important questions in supramolecular peptide self-assemblies are their interactions with metallic compounds and ions. In the last decade, intensive efforts have been devoted to understanding the structural properties of these interactions including their dynamical and catalytic impact in natural and de novo systems. Since structural insights from experimental approaches could be particularly challenging, computational chemistry methods are interesting complementary tools. Here, we present the general multiscale strategies we developed and applied for the study of metallopeptide assemblies. These strategies include prediction of metal binding site, docking of metallic moieties, classical and accelerated molecular dynamics and finally QM/MM calculations. The systems of choice for this chapter are, on one side, peptides involved in neurodegenerative diseases and, on the other, de novo fibrillar systems with catalytic properties. Both successes and remaining challenges are highlighted so that the protocol could be apply to other system of this kind.

One of the main hallmarks of Alzheimer's Disease is the formation of beta-amyloid plaques, whose formation may be enhanced by metal binding or the appearance of familial mutations. In the present study, the simultaneous effect of familial mutations (E22Q, E22G, E22K, and D23N) and binding to metal ions (Cu(II) or Al(III)) is studied at the A beta(42) monomeric and fibrillar levels. With the application of GaMD and MD simulations, it is observed that the effects of metal binding and mutations differ in the monomeric and fibrillar forms. In the monomeric structures, without metal binding, all mutations reduce the amount of alpha-helix and increase, in some cases, the beta-sheet content. In the presence of Cu(II) and Al(III) metal ions, the peptide becomes less flexible, and the beta-sheet content decreases in favor of forming alpha-helix motifs that stabilize the system through interhelical contacts. Regarding the fibrillar structures, mutations decrease the opening of the fiber in the vertical axis, thereby stabilizing the S-shaped structure of the fiber. This effect is, in general, enhanced upon metal binding. These results may explain the different A beta(42) aggregation patterns observed in familial mutations.

One of Alzheimer's disease major hallmarks is the aggregation of β-amyloid peptide, a process in which metal ions play an important role. In the present work, an integrative computational study has been performed to identify the metal-binding regions and determine the conformational impact of Cu(II) and Al(III) ion binding to the β-amyloid (Aβ42) fibrillary structure. Through classical and Gaussian accelerated molecular dynamics, it has been observed that the metal-free fiber shows a hinge fan-like motion of the S-shaped structure, maintaining the general conformation. Upon metal coordination, distinctive patterns are observed depending on the metal. Cu(II) binds to the flexible N-terminal region and induces structural changes that could ultimately disrupt the fibrillary structure. In contrast, Al(III) binding takes place with the residues Glu22 and Asp23, and its binding reinforces the core stability of the system. These results give clues on the molecular impact of the interaction of metal ions with the aggregates and sustain their non-innocent roles in the evolution of the illness.

Enzymes typically fold into defined 3D protein structures exhibiting a high catalytic efficiency and selectivity. It has been proposed that the earliest enzymes may have arisen from the self-assembly of short peptides into supramolecular amyloid-like structures. Several artificial amyloids have been shown to display catalytic activity while offering advantages over natural enzymes in terms of modularity, flexibility, stability, and reusability. Hydrolases, especially esterases, are the most common artificial amyloid-like nanozymes with some reported to act as carbonic anhydrases (CA). Their hydrolytic activity is often dependent on the binding of metallic cofactors through a coordination triad composed of His residues in the β-strands, which mimic the arrangement found in natural metalloenzymes. Tyr residues contribute to the coordination of metal ions in the active center of metalloproteins; however, their use has been mostly neglected in the design of metal-containing amyloid-based nanozymes. We recently reported that four different polar prion-inspired heptapeptides spontaneously self-assembled into amyloid fibrils. Their sequences lack His but contain three alternate Tyr residues exposed to solvent. We combine experiments and simulations to demonstrate that the amyloid fibrils formed by these peptides can efficiently coordinate and retain different divalent metal cations, functioning as both metal scavengers and nanozymes. The metallized fibrils exhibit esterase and CA activities without the need for a histidine triad. These findings highlight the functional versatility of prion-inspired peptide assemblies and provide a new sequential context for the creation of artificial metalloenzymes. Furthermore, our data support amyloid-like structures acting as ancestral catalysts at the origin of life.

RAS oncoproteins are molecular switches associated with critical signaling pathways that regulate cell proliferation and differentiation. Mutations in the RAS family, mainly in the KRAS isoform, are responsible for some of the deadliest cancers, which has made this protein a major target in biomedical research. Here we demonstrate that a designed bis-histidine peptide derived from the αH helix of the cofactor SOS1 binds to KRAS with high affinity upon coordination to Pd(II). NMR spectroscopy and MD studies demonstrate that Pd(II) has a nucleating effect that facilitates the access to the bioactive α-helical conformation. The binding can be suppressed by an external metal chelator and recovered again by the addition of more Pd(II), making this system the first switchable KRAS binder, and demonstrates that folding-upon-binding mechanisms can operate in metal-nucleated peptides. In vitro experiments show that the metallopeptide can efficiently internalize into living cells and inhibit the MAPK kinase cascade.

Metal ions have been found to play an important role in the formation of extracellular β-amyloid plaques, a major hallmark of Alzheimer's disease. In the present study, the conformational landscape of Aβ42 with Al(iii) and Cu(ii) has been explored using Gaussian accelerated molecular dynamics. Both metals reduce the flexibility of the peptide and entail a higher structural organization, although to different degrees. As a general trend, Cu(ii) binding leads to an increased α-helix content and to the formation of two α-helices that tend to organize in a U-shape. By contrast, most Al(iii) complexes induce a decrease in helical content, leading to more extended structures that favor the appearance of transitory β-strands.

Peptide engineering has been extremely successful in creating new structures with defined properties and functions. Although generally overlooked in this context, coordination chemistry offers an additional set of interactions that opens unexplored design opportunities for developing complex molecular structures. With this in mind, we report the development of new artificial peptide ligands that fold into chiral and discrete supramolecular helicates in the presence of labile metal ions such as Fe(II) and Co(II). By selecting appropriate heterochiral β‑turn promoting sequences, we can encode the stereoselective folding of the peptide ligand, and define the physicochemical properties of their corresponding metal complexes. The study of these metallopeptides by CD and NMR spectroscopy, combined with computational methods allowed us to identify and determine the structure of two isochiral ΛΛ-helicates, folded as topological isomers. We also show that these new peptide helicates, dynamically selected in the presence of labile Co(II) ions, can be locked as kinetically-inert species by in situ oxidation to Co(III). Finally, in addition to the in vitro characterization of their selective binding to three-way DNA, cell microscopy experiments demonstrated that a rhodamine-labeled Fe(II) helicate was internalized and selectively stains DNA replication factories in functional cells.

With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding.