Delayed entry and the utilization of higher education in Italian youth labour markets: evolution and involution

The article analyses the relation in Italy between education and labour status of highly educated people aged 20-29 over the years 1993-2009. A special labour market entry problem for young Italian graduates – it is argued – stands out in this long period. The article investigates and stresses a series of facts underlying the labour performance of young Italian graduates: the failure (at least so far) of the reform of the higher education system at the end of 1990s to accelerate the entry of young graduates into the labour market with the introduction of three-year degrees aimed at shortening university courses for a vast majority of students; the special difficulty in matching the demand for and supply of labour for graduates aged 20-24; the poor labour performances of first-level graduates aged 25-29 compared with that of second-level graduates and long programme diploma holders; the progress in the educational attainment of women and the consequent evolution in female labour status; and the enormous regional differences underlying the national data. Policy interventions to mitigate, if not eliminate, the special entry problem of first-level graduates – simplifying the organization of the two degree levels and removing restrictions on access to a range of professions, especially in the public sector – are required.

A multi-resolution model to capture both global fluctuations of an enzyme and molecular recognition in the ligand-binding site

In multi-resolution simulations, different system components are simultaneously modelled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of substrate binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modelled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically, without loss of accuracy in the atomistic description. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity via an exploration of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function

Advantages and challenges in coupling an ideal gas to atomistic models in adaptive resolution simulations

In adaptive resolution simulations, molecular fluids are modeled employing different levels of resolution in different subregions of the system. When traveling from one region to the other, particles change their resolution on the fly. One of the main advantages of such approaches is the computational efficiency gained in the coarse-grained region. In this respect the best coarse-grained system to employ in the low resolution region would be the ideal gas, making intermolecular force calculations in the coarse-grained subdomain redundant. In this case, however, a smooth coupling is challenging due to the high energetic imbalance between typical liquids and a system of non-interacting particles. In the present work, we investigate this approach, using as a test case the most biologically relevant fluid, water. We demonstrate that a successful coupling of water to the ideal gas can be achieved with current adaptive resolution methods, and discuss the issues that remain to be addressed

Spatially Resolved Thermodynamic Integration: An Efficient Method to Compute Chemical Potentials of Dense Fluids

Many popular methods for the calculation of chemical potentials rely on the insertion of test particles into the target system. In the case of liquids and liquid mixtures, this procedure increases in difficulty upon increasing density or concentration, and the use of sophisticated enhanced sampling techniques becomes inevitable. In this work we propose an alternative strategy, spatially resolved thermodynamic integration, or SPARTIAN for short. Here, molecules are described with atomistic resolution in a simulation subregion, and as ideal gas particles in a larger reservoir. All molecules are free to diffuse between subdomains adapting their resolution on the fly. To enforce a uniform density profile across the simulation box, a single-molecule external potential is computed, applied, and identified with the difference in chemical potential between the two resolutions. Since the reservoir is represented as an ideal gas bath, this difference exactly amounts to the excess chemical potential of the target system. The present approach surpasses the high density/concentration limitation of particle insertion methods because the ideal gas molecules entering the target system region spontaneously adapt to the local environment. The ideal gas representation contributes negligibly to the computational cost of the simulation, thus allowing one to make use of large reservoirs at minimal expenses. The method has been validated by computing excess chemical potentials for pure Lennard-Jones liquids and mixtures, SPC and SPC/E liquid water, and aqueous solutions of sodium chloride. The reported results well reproduce literature data for these systems

Open Boundary Simulations of Proteins and Their Hydration Shells by Hamiltonian Adaptive Resolution Scheme

The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS) allows to perform molecular simulations in an open boundary framework. It allows to change on the fly the resolution of specific subset of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Towards this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations.Comment: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright \c{opyright} American Chemical Society after peer review and technical editing by the publishe

Coarse-grained modelling of protein structure and internal dynamics: comparative methods and applications

The first chapter is devoted to a brief summary of the basic techniques commonly used to characterise protein's internal dynamics, and to perform those primary analyses which are the basis for our further developments. To this purpose we recall the basics of Principal Component Analysis of the covariance matrix of molecular dynamics (MD) trajectories. The overview is aimed at motivating and justifying a posteriori the introduction of coarse-grained models of proteins. In the second chapter we shall discuss dynamical features shared by different conformers of a protein. We'll review previously obtained results, concerning the universality of the vibrational spectrum of globular proteins and the self-similar free energy landscape of specific molecules, namely the G-protein and Adk. Finally, a novel technique will be discussed, based on the theory of Random Matrices, to extract the robust collective coordinates in a set of protein conformers by comparison with a stochastic reference model. The third chapter reports on an extensive investigation of protein internal dynamics modelled in terms of the relative displacement of quasi-rigid groups of amino acids. Making use of the results obtained in the previous chapters, we shall discuss the development of a strategy to optimally partition a protein in units, or domains, whose internal strain is negligible compared to their relative uctuation. These partitions will be used in turn to characterise the dynamical properties of proteins in the framework of a simplified, coarse-grained, description of their motion. In the fourth chapter we shall report on the possibility to use the collective uctuations of proteins as a guide to recognise relationships between them that may not be captured as significant when sequence or structural alignment methods are used. We shall review a method to perform the superposition of two proteins optimising the similarity of the structures as well as the dynamical consistency of the aligned regions; then, we shall next discuss a generalisation of this scheme to accelerate the dynamics-based alignment, in the perspective of dataset-wide applications. Finally, the fifth chapter focuses on a different topic, namely the occurrence of topologically-entangled states (knots) in proteins. Specifically, we shall investigate the sequence and structural properties of knotted proteins, reporting on an exhaustive dataset-wide comparison with unknotted ones. The correspondence, or the lack thereof, between knotted and unknotted proteins allowed us to identify, in knotted chains, small segments of the backbone whose `virtual' excision results in an unknotted structure. These `knot-promoting' loops are thus hypothesised to be involved in the formation of the protein knot, which in turn is likely to cover some role in the biological function of the knotted proteins

From Classical to Quantum and Back: Hamiltonian Adaptive Resolution Path Integral, Ring Polymer, and Centroid Molecular Dynamics

Path integral-based simulation methodologies play a crucial role for the investigation of nuclear quantum effects by means of computer simulations. However, these techniques are significantly more demanding than corresponding classical simulations. To reduce this numerical effort, we recently proposed a method, based on a rigorous Hamiltonian formulation, which restricts the quantum modeling to a small but relevant spatial region within a larger reservoir where particles are treated classically. In this work, we extend this idea and show how it can be implemented along with state-of-the-art path integral simulation techniques, such as ring polymer and centroid molecular dynamics, which allow the approximate calculation of both quantum statistical and quantum dynamical properties. To this end, we derive a new integration algorithm which also makes use of multiple time-stepping. The scheme is validated via adaptive classical--path-integral simulations of liquid water. Potential applications of the proposed multiresolution method are diverse and include efficient quantum simulations of interfaces as well as complex biomolecular systems such as membranes and proteins

Random Matrix approach to collective behavior and bulk universality in protein dynamics

Covariance matrices of amino acid displacements, commonly used to characterize the large-scale movements of proteins, are investigated through the prism of Random Matrix Theory. Bulk universality is detected in the local spacing statistics of noise-dressed eigenmodes, which is well described by a Brody distribution with parameter $\beta\simeq 0.8$. This finding, supported by other consistent indicators, implies a novel quantitative criterion to single out the collective degrees of freedom of the protein from the majority of high-energy, localized vibrations.Comment: 4 pages, 7 figure