SARS-CoV-2 virion stabilization by Zn binding

Zinc plays a crucial role in the process of virion maturation inside the host cell. The accessory Cys-rich proteins expressed in SARS-CoV-2 by genes ORF7a and ORF8 are likely involved in zinc binding and in interactions with cellular antigens activated by extensive disulfide bonds. In this report we provide a proof of concept for the feasibility of a structural study of orf7a and orf8 proteins. A conceivable hypothesis is that lack of cellular zinc, or substitution thereof, might lead to a significant slowing down of viral maturation

Multi-scale theoretical approach to X-ray absorption spectra in disordered systems: an application to the study of Zn(II) in water

We develop a multi-scale theoretical approach aimed at calculating from first principles X-ray absorption spectra of liquid solutions and disordered systems. We test the method by considering the paradigmatic case of Zn(II) in water which, besides being relevant in itself, is also of interest for biology. With the help of classical molecular dynamics simulations we start by producing bunches of configurations differing for the Zn(II)-water coordination mode. Different coordination modes are obtained by making use of the so-called dummy atoms method. From the collected molecular dynamics trajectories, snapshots of a more manageable subsystem encompassing the metal site and two solvation layers are cut out. Density functional theory is used to optimize and relax these reduced system configurations employing a uniform dielectric to mimic the surrounding bulk liquid water. On the resulting structures, fully quantum mechanical X-ray absorption spectra calculations are performed by including core-hole effects and core-level shifts. The proposed approach does not rely on any guessing or fitting of the force field or of the atomic positions of the system. The comparison of the theoretically computed spectrum with the experimental Zn K-edge XANES data unambiguously demonstrates that among the different a priori possible geometries, Zn(II) in water lives in an octahedral coordination mode.Comment: 8 pages, 3 figure

Modeling the interplay of glycine protonation and multiple histidine binding of copper in the prion protein octarepeat subdomains

The octarepeat region of the prion protein can bind Cu(2+) ions up to full occupancy (one ion per octarepeat) at neutral pH. While crystallographic data show that the HGGG octarepeat subdomain is the basic binding unit, multiple histidine coordination at lower Cu occupancy has been reported by X-ray absorption spectroscopy, EPR, and potentiometric experiments. In this paper we investigate, with first principles Car-Parrinello simulations, the first step for the formation of the Cu low-level binding mode, where four histidine side chains are coordinated to the same Cu(2+) ion. This step involves the further binding of a second histidine to an already HGGG domain bonded Cu(2+) ion. The influence of the pH on the ability of Cu to bind two histidine side chains was taken into account by simulating different protonation states of the amide N atoms of the two glycines lying nearest to the first histidine. Multiple histidine coordination is also seen to occur when glycine deprotonation occurs and the presence of the extra histidine stabilizes the Cu-peptide complex. Though the stabilization effect slightly decreases with the number of deprotonated glycines (reaching a minimum when both N atoms of the two nearest glycines are available as Cu ligands), the system is still capable of binding the second histidine in a 4N tetrahedral (though slightly distorted) coordination, whose energy is very near to that of the crystallographic square-planar 3N1O coordination. This result suggests that at low metal concentration the reorganization energy associated with Cu(II)/Cu(I) reduction is small also at pH approximately 7, when glycines are deprotonated

Modelling Protein Plasticity: The Example of Frataxin and Its Variants

Frataxin (FXN) is a protein involved in storage and delivery of iron in the mitochondria. Single-point mutations in the FXN gene lead to reduced production of functional frataxin, with the consequent dyshomeostasis of iron. FXN variants are at the basis of neurological impairment (the Friedreich’s ataxia) and several types of cancer. By using altruistic metadynamics in conjunction with the maximal constrained entropy principle, we estimate the change of free energy in the protein unfolding of frataxin and of some of its pathological mutants. The sampled configurations highlight differences between the wild-type and mutated sequences in the stability of the folded state. In partial agreement with thermodynamic experiments, where most of the analyzed variants are characterized by lower thermal stability compared to wild type, the D104G variant is found with a stability comparable to the wild-type sequence and a lower water-accessible surface area. These observations, obtained with the new approach we propose in our work, point to a functional switch, affected by single-point mutations, of frataxin from iron storage to iron release. The method is suitable to investigate wide structural changes in proteins in general, after a proper tuning of the chosen collective variable used to perform the transition

Predicting the Structure of Enzymes with Metal Cofactors: The Example of [FeFe] Hydrogenases

The advent of deep learning algorithms for protein folding opened a new era in the ability of predicting and optimizing the function of proteins once the sequence is known. The task is more intricate when cofactors like metal ions or small ligands are essential to functioning. In this case, the combined use of traditional simulation methods based on interatomic force fields and deep learning predictions is mandatory. We use the example of [FeFe] hydrogenases, enzymes of unicellular algae promising for biotechnology applications to illustrate this situation. [FeFe] hydrogenase is an iron–sulfur protein that catalyzes the chemical reduction of protons dissolved in liquid water into molecular hydrogen as a gas. Hydrogen production efficiency and cell sensitivity to dioxygen are important parameters to optimize the industrial applications of biological hydrogen production. Both parameters are related to the organization of iron–sulfur clusters within protein domains. In this work, we propose possible three-dimensional structures of Chlorella vulgaris 211/11P [FeFe] hydrogenase, the sequence of which was extracted from the recently published genome of the given strain. Initial structural models are built using: (i) the deep learning algorithm AlphaFold; (ii) the homology modeling server SwissModel; (iii) a manual construction based on the best known bacterial crystal structure. Missing iron–sulfur clusters are included and microsecond-long molecular dynamics of initial structures embedded into the water solution environment were performed. Multiple-walkers metadynamics was also used to enhance the sampling of structures encompassing both functional and non-functional organizations of iron–sulfur clusters. The resulting structural model provided by deep learning is consistent with functional [FeFe] hydrogenase characterized by peculiar interactions between cofactors and the protein matrix

Advancing Raman Spectroscopy Resolution towards the Nanoscale: Fundamentals, Advantages, and Applications of Tip-Enhanced Raman Spectroscopy

Raman spectroscopy has long been a powerful analytical technique for studying molecular interactions and chemical properties of materials. However, its limited spatial resolution has posed a challenge in observing nanoscale phenomena. In response to this challenge, the field of Tip-Enhanced Raman Spectroscopy (TERS) has emerged as a revolutionary approach, pushing the boundaries of Raman spectroscopy resolution down to the nanoscale. This presentation delves into the fundamentals of TERS, highlighting its advantages over traditional Raman spectroscopy, and explores its diverse applications across various scientific disciplines (electronics, green transitions, biology, ....)

Dealing with Cu reduction in X-ray absorption spectroscopy experiments

International audienceIn this paper we prove in the exemplary case of the Amyloid-b peptide in complex with Cu(II) that at the current low temperatures employed in XAS experiments, the time needed for collecting a good quality XAS spectrum is significantly shorter than the time after which structural damages become appreciable. Our method takes advantage of the well-known circumstance that the transition of Cu from the oxidized to the reduced form under ionizing radiation can be quantified by monitoring a characteristic peak in the pre-edge region. We show that there exists a sufficiently large time window in which good XAS spectra can be acquired before the structure around the oxidized Cu(II) ion reorganizes itself into the reduced Cu(I) “resting” structure. We suggest that similar considerations apply to other cases of biological interest, especially when dealing with macromolecules in complex with transition metal ions

Strain characterization in SiGe epitaxial samples by Tip Enhanced Raman Spectroscopy

The progressive downsizing of semiconductors is driving information processing technology into a broader spectrum of new applications and capabilities. Strained silicon has become one of the best solutions for integrated circuits thanks to the advantages in terms of miniaturization. Indeed, a biaxial tensile stress applied to the silicon in the channel region of a MOSFET increases the mobility of carriers. This stress can be imposed by doping the silicon underneath with germanium, causing a mismatch between the lattice constant thus improving the electrons’ mobility [1]. Over the years, there has been an increasing need, especially in the industrial sector, to develop faster and non-destructive characterization techniques to monitor strain during the manufacturing phases of semiconductor devices. Currently, Tip-Enhanced Raman Spectroscopy (TERS) is one of most powerful methods for strain characterization, as it is a non-contact and non-destructive technique with a lateral resolution of a few nanometers and the capability of analyzing and collecting signals from the most superficial layer of a sample. The enhanced field is strongly restricted to the surface plasmons region, just a few nanometers deep [2], thanks to the simultaneous use of a nanometric tip of an Atomic Force Microscope (AFM) and a laser from a Raman spectrometer [3]. The analyzed sample was provided by CEA-Leti (Laboratoire d'électronique des technologies de l'information, Grenoble) and consists of a (001) silicon substrate where an epitaxial layer of Si0.7Ge0.3 with thickness of 17 nm is grown following several patterns. The AFM probe employed is characterized by an innovative coating which enables its implementation in the clean room for in-line characterization. TERS is used to map the variation in the position of the silicon peak in the local Raman spectrum (≈520.5 cm-1) along the sample pattern in order to identify the strain profile with a resolution of a few nanometers. The results confirm that TERS represents a powerful tool in monitoring the quality of production lines in the semiconductor industry and currently provides the best resolution among the Raman techniques for the strain characterization. References [1] P. Dobrosz et all, Surface and Coatings Technology, 2005, 200, 1755–1760. [2] F. Shao, R. Zenobi, Analytical and Bioanalytical Chemistry, 2019, 411, 37–61. [3] N.Hayazawa et al., Nanosensing Materials Devices, and Systems III, 2007, Proc. of SPIE Vol. 6769, 67690P

Challenges - real time nano characterization related technlogieeS

The project CHALLENGES – Real-time nano-CHAracterization reLatEd techNoloGiEeS – aims to develop innovative Non-Destructive Techniques (NDTs) for reliable inline multiscale measurements down to the nanoscale, and fully compatible with different factory environments. The developed metrology technologies will enable the increase of speed, resolution, sensitivity, spectral range and compatibility within different nano-related production environments, finally improving products performance, quality and reliability, with the consequent bosting of competitiveness. The CHALLENGES’s innovation will be developed exploiting the plasmonic enhancement of optical signals. It will provide a non-destructive approach based on the use of multipurpose nano-optical techniques to enable a reliable real-time nano-scale characterization in the factory floor, using plasmonic enhanced Raman, InfraRed (IR) and Photoluminescence signals. CHALLENGES is focused on broadening the scope of AFM techniques useable in semiconductor manufacturing by implementing suitable plasmonic-based technologies to bring in industrial environments the capabilities of optical spectroscopies at the nanoscale, already demonstrated at lab level. Such composite and hybrid measurement techniques will enable new applications for the already proven industrial AFM-based characterization technologies. Signal amplification by localized plasmon resonance at a sharp AFM tip will allow improving both the spatial resolution well beyond the optical diffraction limit and the local signal intensity with an improvement of the signal/noise ratio. Improvements are also expected concerning the time scale resolving capabilities. The improvement of the spatial resolution will allow to obtain nanoscale spectral maps, compatible with the size of the current electronic devices, while the improved signal-to-noise ratio will allow for a faster and reliable punctual analysis. The final goal is to develop nanoscale metrological NDTs based on SPM platforms, for doping, annealing, metal contamination, dangling bonds presence and strain measurement directly within the production lines with real-time capabilities