Search

Three hexacarbonyl diiron dithiolate complexes [Fe2(CO)6(µ-(SCH2)2X)] with different substituted bridgeheads (X = CH2, CEt2, CBn2 (Bn = CH2C6H5)), have been studied under the same experimental conditions by cyclic voltammetry in dichloromethane [NBu4][PF6] 0.2 M. DFT calculations were performed to rationalize the mechanism of reduction of these compounds. The three complexes undergo a two-electron transfer whose the mechanism depend on the bulkiness of the dithiolate bridge, which involves a different timing of the structural changes (Fe-S bond cleavage, inversion of conformation and CO bridging) vs redox steps. The introduction of a bulky group in the dithiolate linker has obviously an effect on normally ordered (as for propanedithiolate (pdt)) or inverted (pdtEt2, pdtBn2) reduction potentials. Et → CH2Ph replacement is not theoretically predicted to alter the geometry and energy of the most stable mono-reduced and bi-reduced forms but such a replacement alters the kinetics of the electron transfer vs the structural changes.

Flavodoxins are enzymes that contain the redox-active flavin mononucleotide (FMN) cofactor and play a crucial role in numerous biological processes, including energy conversion and electron transfer. Since the redox characteristics of flavodoxins are significantly impacted by the molecular environment of the FMN cofactor, the evaluation of the interplay between the redox properties of the flavin cofactor and its molecular surroundings in flavoproteins is a critical area of investigation for both fundamental research and technological advancements, as the electrochemical tuning of flavoproteins is necessary for optimal interaction with redox acceptor or donor molecules. In order to facilitate the rational design of biomolecular devices, it is imperative to have access to computational tools that can accurately predict the redox potential of both natural and artificial flavoproteins. In this study, we have investigated the feasibility of using non-equilibrium thermodynamic integration protocols to reliably predict the redox potential of flavodoxins. Using as a test set the wild-type flavodoxin from Clostridium Beijerinckii and eight experimentally characterized single-point mutants, we have computed their redox potential. Our results show that 75% (6 out of 8) of the calculated reaction free energies are within 1 kcal/mol of the experimental values, and none exceed an error of 2 kcal/mol, confirming that non-equilibrium thermodynamic integration is a trustworthy tool for the quantitative estimation of the redox potential of this biologically and technologically significant class of enzymes.

Nitrogen gas is a highly inert molecule and its activation under mild conditions represents a crucial goal in current research. In a recent study, the discovery of low-valence Ca(i) compounds capable of coordinating and reducing N2 was reported [B. Rösch, T. X. Gentner, J. Langer, C. Färber, J. Eyselein, L. Zhao, C. Ding, G. Frenking and S. Harder, Science, 2021, 371, 1125]. The study of low-valence alkaline earth complexes represents a new horizon in inorganic chemistry and demonstrates examples of spectacular reactivity. For example, complexes of the [BDI]2Mg2 type are selective reducing reagents in both organic and inorganic synthesis reactions. To date, however, no activity of Mg(i) complexes in the activation of the nitrogen molecule has been reported. By computational studies, in the present work, we investigated the analogies and differences of low-valence Ca(i) and Mg(i) complexes in the coordination, activation and protonation of N2. We have shown that the possibility of alkaline earth metals to employ atomic orbitals of the d type is reflected in the differences in the N2 binding energy and its coordination mode (end-on vs. side-on), as well as in the spin state of the resulting adduct (singlet vs. triplet). These divergences are finally observed in the subsequent protonation reaction, which turned out to be prohibitive in the presence of Mg.

In endemic regions (West Africa and the Congo Basin), the genetic diversity of monkeypox virus (MPXV) is geographically structured into two major clades (Clades I and II) that differ in virulence and host associations. Clade IIb is closely related to the B.1 lineage, which is dominating a worldwide outbreak initiated in 2022. Lineage B.1 has however accumulated mutations of unknown significance that most likely result from apolipoprotein B mRNA editing catalytic polypeptide-like 3 (APOBEC3) editing. We applied a population genetics - phylogenetics approach to investigate the evolution of MPXV during historical viral spread in Africa and to infer the distribution of fitness effects. We observed a high preponderance of codons evolving under strong purifying selection, particularly in viral genes involved in morphogenesis and replication or transcription. However, signals of positive selection were also detected and were enriched in genes involved in immunomodulation and/or virulence. In particular, several genes showing evidence of positive selection were found to hijack different steps of the cellular pathway that senses cytosolic DNA. Also, a few selected sites in genes that are not directly involved in immunomodulation are suggestive of antibody escape or other immune-mediated pressures. Because orthopoxvirus host range is primarily determined by the interaction with the host immune system, we suggest that the positive selection signals represent signatures of host adaptation and contribute to the different virulence of Clade I and II MPXVs. We also used the calculated selection coefficients to infer the effects of mutations that define the predominant human MPXV1 (hMPXV1) lineage B.1, as well as the changes that have been accumulating during the worldwide outbreak. Results indicated that a proportion of deleterious mutations were purged from the predominant outbreak lineage, whose spread was not driven by the presence of beneficial changes. Polymorphic mutations w

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.

De novo metalloprotein design is a remarkable approach to shape protein scaffolds toward specific functions. Here, we report the design and characterization of Due Rame 1 (DR1), a de novo designed protein housing a di-copper site and mimicking the Type 3 (T3) copper-containing polyphenol oxidases (PPOs). To achieve this goal, we hierarchically designed the first and the second di-metal coordination spheres to engineer the di-copper site into a simple four-helix bundle scaffold. Spectroscopic, thermodynamic, and functional characterization revealed that DR1 recapitulates the T3 copper site, supporting different copper redox states, and being active in the O2-dependent oxidation of catechols to o-quinones. Careful design of the residues lining the substrate access site endows DR1 with substrate recognition, as revealed by Hammet analysis and computational studies on substituted catechols. This study represents a premier example in the construction of a functional T3 copper site into a designed four-helix bundle protein.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the loss of the upper and lower motor neurons (MNs). About 10% of patients have a family history (familial, fALS); however, most patients seem to develop the sporadic form of the disease (sALS). SOD1 (Cu/Zn superoxide dismutase-1) is the first studied gene among the ones related to ALS. Mutant SOD1 can adopt multiple misfolded conformation, lose the correct coordination of metal binding, decrease structural stability, and form aggregates. For all these reasons, it is complicated to characterize the conformational alterations of the ALS-associated mutant SOD1, and how they relate to toxicity. In this work, we performed a multilayered study on fibroblasts derived from two ALS patients, namely SOD1L145F and SOD1S135N, carrying the p.L145F and the p.S135N missense variants, respectively. The patients showed diverse symptoms and disease progression in accordance with our bioinformatic analysis, which predicted the different effects of the two mutations in terms of protein structure. Interestingly, both mutations had an effect on the fibroblast energy metabolisms. However, while the SOD1L145F fibroblasts still relied more on oxidative phosphorylation, the SOD1S135N fibroblasts showed a metabolic shift toward glycolysis. Our study suggests that SOD1 mutations might lead to alterations in the energy metabolism.

Four endemic coronaviruses infect humans and cause mild symptoms. Because previous analyses were based on a limited number of sequences and did not control for effects that affect molecular dating, we re-assessed the timing of endemic coronavirus emergence. After controlling for recombination, selective pressure, and molecular clock model, we obtained similar tMRCA (time to the most recent common ancestor) estimates for the four coronaviruses, ranging from 72 (HCoV-229E) to 54 (HCoV-NL63) years ago. The split times of HCoV-229E and HCoV-OC43 from camel alphacoronavirus and bovine coronavirus were dated ~268 and ~99 years ago. The split times of HCoV-HKU1 and HCoV-NL63 could not be calculated, as their zoonoticic sources are unknown. To compare the timing of coronavirus emergence to that of another respiratory virus, we recorded the occurrence of influenza pandemics since 1500. Although there is no clear relationship between pandemic occurrence and human population size, the frequency of influenza pandemics seems to intensify starting around 1700, which corresponds with the initial phase of exponential increase of human population and to the emergence of HCoV-229E. The frequency of flu pandemics in the 19th century also suggests that the concurrence of HCoV-OC43 emergence and the Russian flu pandemic may be due to chance.

Coronaviruses (CoVs) have complex genomes that encode a fixed array of structural and nonstructural components, as well as a variety of accessory proteins that differ even among closely related viruses. Accessory proteins often play a role in the suppression of immune responses and may represent virulence factors. Despite their relevance for CoV phenotypic variability, information on accessory proteins is fragmentary. We applied a systematic approach based on homology detection to create a comprehensive catalogue of accessory proteins encoded by CoVs. Our analyses grouped accessory proteins into 379 orthogroups and 12 super-groups. No orthogroup was shared by the four CoV genera and very few were present in all or most viruses in the same genus, reflecting the dynamic evolution of CoV genomes. We observed differences in the distribution of accessory proteins in CoV genera. Alphacoronaviruses harboured the largest diversity of accessory open reading frames (ORFs), deltacoronaviruses the smallest. However, the average number of accessory proteins per genome was highest in betacoronaviruses. Analysis of the evolutionary history of some orthogroups indicated that the different CoV genera adopted similar evolutionary strategies. Thus, alphacoronaviruses and betacoronaviruses acquired phosphodiesterases and spike-like accessory proteins independently, whereas horizontal gene transfer from reoviruses endowed betacoronaviruses and deltacoronaviruses with fusion-associated small transmembrane (FAST) proteins. Finally, analysis of accessory ORFs in annotated CoV genomes indicated ambiguity in their naming. This complicates cross-communication among researchers and hinders automated searches of large data sets (e.g., PubMed, GenBank). We suggest that orthogroup membership is used together with a naming system to provide information on protein function.

Autosomal dominant sleep-related hypermotor epilepsy (ADSHE) is the familial form of a focal epilepsy characterized by hyperkinetic focal seizures, mainly arising during non-rapid eye movements (NREM) sleep. Mutations associated with ADSHE account for a small proportion of the genetically determined cases, suggesting the existence of other disease-causing genes. Here, we reported the results obtained by performing trio-based whole-exome sequencing (WES) in an Italian family showing ADSHE and investigated the structural impact of putative variants by in silico modeling analysis. We identified a p.(Trp276Gly) variant in MOXD1 gene encoding the monooxigenase DBH like 1 protein, cosegregating with the disease and annotated as VUS under the ACMG recommendations. Structural bioinformatic analysis predicted a high destabilizing effect of this variant, due to the loss of important hydrophilic bonds and an expansion of cavity volume in the protein hydrophobic core. Although our data support a functional effect of the p.(Trp276Gly) variant, we highlight the need to identify additional families carrying MOXD1 mutations or functional analyses in suitable models to clarify its role in ADSHE pathogenesis. Moreover, we discuss the importance of VUS reporting due to the low rate of pathogenic variant identification by NGS in epilepsy and for future reinterpretation studies.

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 (Em) 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 Emvalue, and when possible, we rationalized them on the basis of previous studies.