Energy landscape in protein folding and unfolding

We use (1)H NMR to probe the energy landscape in the protein folding and unfolding process. Using the scheme [Formula: see text] reversible unfolded (intermediate) [Formula: see text] irreversible unfolded (denatured) state, we study the thermal denaturation of hydrated lysozyme that occurs when the temperature is increased. Using thermal cycles in the range [Formula: see text] K and following different trajectories along the protein energy surface, we observe that the hydrophilic (the amide NH) and hydrophobic (methyl CH(3) and methine CH) peptide groups evolve and exhibit different behaviors. We also discuss the role of water and hydrogen bonding in the protein configurational stability

Polarization-dependent optomechanics mediated by chiral microresonators.

Chirality is one of the most prominent and intriguing aspects of nature, from spiral galaxies down to aminoacids. Despite the wide range of living and non-living, natural and artificial chiral systems at different scales, the origin of chirality-induced phenomena is often puzzling. Here we assess the onset of chiral optomechanics, exploiting the control of the interaction between chiral entities. We perform an experimental and theoretical investigation of the simultaneous optical trapping and rotation of spherulite-like chiral microparticles. Due to their shell structure (Bragg dielectric resonator), the microparticles function as omnidirectional chiral mirrors yielding highly polarization-dependent optomechanical effects. The coupling of linear and angular momentum, mediated by the optical polarization and the microparticles chiral reflectance, allows for fine tuning of chirality-induced optical forces and torques. This offers tools for optomechanics, optical sorting and sensing and optofluidics

The role of water in protein's behavior: The two dynamical crossovers studied by NMR and FTIR techniques

The role the solvent plays in determining the biological activity of proteins is of primary importance. Water is the solvent of life and proteins need at least a water monolayer covering their surface in order to become biologically active. We study how the properties of water and the effect of its coupling with the hydrophilic moieties of proteins govern the regime of protein activity. In particular we follow, by means of Fourier Transform Infrared spectroscopy, the thermal evolution of the amide vibrational modes of hydrated lysozyme in the temperature interval 180K < T < 350K. In such a way we are able to observe the thermal limit of biological activity characterizing hydrated lysozyme. Finally we focus on the region of lysozyme thermal denaturation by following the evolution of the proton Nuclear Magnetic Resonance (NMR) spectra for 298K < T < 366K with the High-Resolution Magic Angle Spinning probe. Our data suggest that the hydrogen bond coupling between hydration water and protein hydrophilic groups is crucial in triggering the main mechanisms that define the enzymatic activity of proteins

Study of Wettability of Polyethylene Membranes for Food Packaging

In this study, the wettability of PET membranes (prepared with different pore sizes) treated by UV irradiation, thermal annealing or doping with metal nanoparticles was investigated. The wettability was studied using the contact angle method based on the optical microscopy. The membranes were analyzed before and after pore etching, and after each applied treatment. It turned out that membranes with different pore sizes exhibit different wetting behavior. Of particular interest are membranes with 0.53 &mu;m pores. When pristine, they show high hydrophobicity (a high contact angle), but after treatment (some of which can be considered as an accelerated aging), their wetting characteristics swap between a hydrophobic and hydrophilic state. Interactions between packaging material and food and the external environment through fine control of wettability could have a major impact on maintaining product quality

1H HR-MAS NMR Spectroscopy and the Metabolite Determination of Typical Foods in Mediterranean Diet

NMR spectroscopy has become an experimental technique widely used in food science. The experimental procedures that allow precise and quantitative analysis on different foods are relatively simple. For a better sensitivity and resolution, NMR spectroscopy is usually applied to liquid sample by means of extraction procedures that can be addressed to the observation of particular compounds. For the study of semisolid systems such as intact tissues, High-Resolution Magic Angle Spinning (HR-MAS) has received great attention within the biomedical area and beyond. Metabolic profiling and metabolism changes can be investigated both in animal organs and in foods. In this work we present a proton HR-MAS NMR study on the typical vegetable foods of Mediterranean diet such as the Protected Geographical Indication (PGI) cherry tomato of Pachino, the PGI Interdonato lemon of Messina, several Protected Designation of Origin (PDO) extra virgin olive oils from Sicily, and the Traditional Italian Food Product (PAT) red garlic of Nubia. We were able to identify and quantify the main metabolites within the studied systems that can be used for their characterization and authentication

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy

Calorimetric analysis points out the physical-chemistry of organic olive oils and reveals the geographical origin

The thermal properties of many organic extra Virgin Olive Oils (eVOOs) coming from different countries of the world were investigated by Differential Scanning Calorimetry (DSC). This technique, through a series of heating and cooling cycles, provides a specific curve, i.e., a thermogram, which represents the fingerprint of each eVOO sample. In fact, variations due to the different cultivars, geographical origin or chemical composition can be highlighted because they produce changes in the corresponding thermogram. In particular, in this work, we show the results of an unsupervised multivariate statistical analysis applied to the DSC thermograms of many organic eVOOs. This analysis allows us to discriminate the geographical origin of the different studied samples in terms of the peculiar features shown by the melting profiles of the triacylglycerol moieties

HR-MAS and NMR towards Foodomics

The integrated approaches of Foodomics, defined as a discipline that studies food and nutrition domains through the application of advanced omic technologies, represent the new frontier to explain some critical issues in food science. Among several applications of omic sciences in food analysis, Nuclear Magnetic Resonance (NMR) spectroscopy is the most used for the instrumental sensitivity and precision. Furthermore, in the last years the NMR technique known as high-resolution magic angle spinning (HR-MAS) has been successfully applied in the field of food science. In particular, this powerful technique is unique for the analysis of talis qualis samples of food products. The results reported in this review showed the validity of this technique for food characterization and authentication but also for the comprehension of the changes in the metabolic profile due to different condition