Molecular modeling techniques have become indispensable in many fields of molecular sciences in which the details related to mechanisms and reactivity need to be studied at an atomistic level. This review article provides a collection of computational modeling works on a topic of enormous interest and urgent relevance: the properties of metalloenzymes involved in the degradation and valorization of natural biopolymers and synthetic plastics on the basis of both circular biofuel production and bioremediation strategies. In particular, we will focus on lytic polysaccharide monooxygenase, laccases, and various heme peroxidases involved in the processing of polysaccharides, lignins, rubbers, and some synthetic polymers. Special attention will be dedicated to the interaction between these enzymes and their substrate studied at different levels of theory, starting from classical molecular docking and molecular dynamics techniques up to techniques based on quantum chemistry.

Determining the redox potentials of protein cofactors and how they are influenced by their molecular neighborhoods is essential for basic research and many biotechnological applications, from biosensors and biocatalysis to bioremediation and bioelectronics. The laborious determination of redox potential with current experimental technologies pushes forward the need for computational approaches that can reliably predict it. Although current computational approaches based on quantum and molecular mechanics are accurate, their large computational costs hinder their usage. In this work, we explored the possibility of using more efficient QSPR models based on machine learning (ML) for the prediction of protein redox potential, as an alternative to classical approaches. As a proof of concept, we focused on flavoproteins, one of the most important families of enzymes directly involved in redox processes. To train and test different ML models, we retrieved a dataset of flavoproteins with a known midpoint redox potential (E_m) and 3D structure. The features of interest, accounting for both short- and long-range effects of the protein matrix on the flavin cofactor, have been automatically extracted from each protein PDB file. Our best ML model (XGB) has a performance error below 1 kcal/mol (∼36 mV), comparing favorably to more sophisticated computational approaches. We also provided indications on the features that mostly affect the E_m value, and when possible, we rationalized them on the basis of previous studies.

This perspective aims at illustrating a computational viewpoint on some specific issues concerning structure–activity relationships related to [FeFe]-hydrogenase ([FeFe]-H₂ase) biomimicry. Most of the research outlined herein has been addressed by means of density functional theory (DFT) based tools, in some cases in conjunction with experimental techniques. The number of computational, experimental and mixed approach studies on the “hydrogenase models” topic is extraordinarily high. Accordingly, several comprehensive reviews have been published over the last twenty years. Moreover, the number of iron compounds of increasing nuclearity that have been considered as “biomimetic models” of [FeFe]-H₂ase has grown exponentially over time. Additionally, the issues regarding some mismatches existing between the natural system and related synthetic analogues, are quite variegated. As a consequence of the countless examples that could be considered as related to “hydrogenase mimicry”, the intent of the present contribution is to provide an account of a limited number of recent study cases, in which DFT has been employed to elucidate redox properties, reactivity issues of selected examples of diiron biomimicry. Herein the treated topics have been explicitly chosen to show how DFT has allowed us to rationalize and complement experimental observations, with a special focus on electrocatalysis aspects. Finally, the specific purpose of this perspective is to show how mutually intertwined are the various issues concerning a fascinating branch of biomimetic research.

The reactivity of laccases towards non-phenolic polycyclic aromatic hydrocarbons remains unclear. Using molecular modelling the oxidation of anthracene and benzo[a]pyrene by Trametes versicolor fungal laccase is investigated.