We have applied solid phase peptide synthesis methods for the construction of peptide ligands that coordinate Fe(ii) ions and fold into chiral peptide helicates that show great affinity and chiral selectivity for three-way DNA junctions and promising cell-internalization properties.

In recent years, the development of synthetic metalloenzymes based on the insertion of inorganic catalysts into biological macromolecules has become a vivid field of investigation. The success of the design of these composites is highly dependent on an atomic understanding of the recognition process between inorganic and biological entities. Despite facing several challenging complexities, molecular modelling techniques could be particularly useful in providing such knowledge. This study aims to discuss how the prediction of the structural and energetic properties of the host-cofactor interactions can be performed by computational means. To do so, we designed a protocol that combines several methodologies like protein-ligand dockings and QM/MM techniques. The overall approach considers fundamental bioinorganic questions like the participation of the amino acids of the receptor to the first coordination sphere of the metal, the impact of the receptor/cofactor flexibility on the structure of the complex, the cost of inserting the inorganic catalyst in place of the natural ligand/substrate into the host and how experimental knowledge can improve or invalidate a theoretical model. As a real case system, we studied an artificial metalloenzyme obtained by the insertion of a Fe(Schiff base) moiety into the heme oxygenase of Corynebacterium diphtheriae. The experimental structure of this species shows a distorted cofactor leading to an unusual octahedral configuration of the iron with two proximal residues chelating the metal and no external ligand. This geometry is far from the conformation adopted by similar cofactors in other hosts and shows that a fine tuning exists between the coordination environment of the metal, the deformability of its organic ligand and the conformational adaptability of the receptor. In a field where very little structural information is yet available, this work should help in building an initial molecular modelling framework for the discovery, design and optimization of inorganic cofactors. Moreover, the approach used in this study also lays the groundwork for the development of computational methods adequate for studying several metal mediated biological processes like the generation of realistic three dimensional models of metalloproteins bound to their natural cofactor or the folding of metal containing peptides.

The development of artificial enzymes aims at expanding the scope of biocatalysis. Over recent years, artificial metalloenzymes based on the insertion of homogeneous catalysts in biomolecules have received an increasing amount of attention. Rational or pseudorational design of these composites is a challenging task because of the complexity of the identification of efficient complementarities among the cofactor, the substrate, and the biological partner. Molecular modeling represents an interesting alternative to help in this task. However, little attention has been paid to this field so far. In this manuscript, we aim at reviewing our efforts in developing strategies efficient to computationally drive the design of artificial metalloenzymes. From protein−ligand dockings to multiscale approaches, we intend to demonstrate that modeling could be useful at the different steps of the design. This Perspective ultimately aims at providing computational chemists with illustration of the applications of their tools for artificial metalloenzymes and convincing enzyme designers of the capabilities, qualitative and quantitative, of computational methodologies.

Organometallic compounds are increasingly used as molecular scaffolds in drug development projects; their structural and electronic properties offering novel opportunities in protein-ligand complementarities. Interestingly, while protein-ligand dockings have long become a spearhead in computer assisted drug design, no benchmarking nor optimization have been done for their use with organometallic compounds. Pursuing our efforts to model metal mediated recognition processes, we herein present a systematic study of the capabilities of the program GOLD to predict the interactions of protein with organometallic compounds. The study focuses on inert systems for which no alteration of the first coordination sphere of the metal occurs upon binding. Several scaffolds are used as test systems with different docking schemes and scoring functions. We conclude that ChemScore is the most robust scoring function with ASP and ChemPLP providing with good results too and GoldScore slightly underperforming. This study shows that current state-of-the-art protein-ligand docking techniques are reliable for the docking of inert organometallic compounds binding to protein.

Stress testing is one of the most important parts of the drug development process, helping to foresee stability problems and to identify degradation products. One of the processes involving stress testing is represented by forced degradation studies, which can predict the impact of certain conditions of pH, moisture, heat, or other negative effects due to transportation or packaging issues on drug potency and purity, ensuring patient safety. Regulatory agencies have been working on a standardization of laboratory procedures since the past two decades. One of the results of those years of intensive research is the International Conference on Harmonization (ICH) guidelines, which clearly define which forced degradation studies should be performed on new drugs, which become a routine work in pharmaceutical laboratories. Since used techniques based on high-performance liquid chromatography coupled with high-resolution mass spectrometry have been developed years ago and are now mastered by pharmaceutical scientists, automation of data analysis, and thus data processing, is becoming a hot topic nowadays. In this work, we present MassChemSite and WebChembase as a tandem to automatize the routine analysis studies without missing information quality, using as a case study the degradation of lansoprazole under acidic, oxidative, basic, and neutral stress conditions.