The free energy landscape of GABA binding to a pentameric ligand-gated ion channel and its disruption by mutations

Pentameric ligand-gated ion channels (pLGICs) of the Cys-loop superfamily are important neuroreceptors that mediate fast synaptic transmission. They are activated by the binding of a neurotransmitter, but the details of this process are still not fully understood. As a prototypical pLGIC, here we choose the insect resistance to dieldrin (RDL) receptor, involved in the resistance to insecticides, and investigate the binding of the neurotransmitter GABA to its extracellular domain at the atomistic level. We achieve this by means of $\mu$-sec funnel-metadynamics simulations, which efficiently enhance the sampling of bound and unbound states by using a funnel-shaped restraining potential to limit the exploration in the solvent. We reveal the sequence of events in the binding process, from the capture of GABA from the solvent to its pinning between the charged residues Arg111 and Glu204 in the binding pocket. We characterize the associated free energy landscapes in the wild-type RDL receptor and in two mutant forms, where the key residues Arg111 and Glu204 are mutated to Ala. Experimentally these mutations produce non-functional channels, which is reflected in the reduced ligand binding affinities, due to the loss of essential interactions. We also analyze the dynamical behaviour of the crucial loop C, whose opening allows the access of GABA to the binding site, while its closure locks the ligand into the protein. The RDL receptor shares structural and functional features with other pLGICs, hence our work outlines a valuable protocol to study the binding of ligands to pLGICs beyond conventional docking and molecular dynamics techniques.Comment: accepted (May 2016); 27 pages, 6 figures, Table of contents graphic, Journal of Chemical Theory and Computation (2016

A pre-posterior analysis framework for quantifying the value of seismic monitoring and inspections of buildings

Peer reviewe

Navigating the AI Paradox: Tackling Energy Poverty and Reducing Environmental Impact through Sustainable Artificial Intelligence Development

Il presente contributo esamina l’interazione tra l’Intelligenza Artificiale (AI) e lo sviluppo sostenibile, valutando criticamente l’impronta ambientale dell’AI, che comprende un notevole consumo energetico e altrettanto notevoli emissioni di CO2 durante l’addestramento dei modelli. Il contributo sottolinea l’importanza degli interventi legislativi, con un focus sull’integrazione dei criteri di sostenibilità nell’AI Act. In conclusione, il contributo riconosce il potenziale trasformativo dell’AI nel risolvere problemi globali urgenti come la povertà energetica, sottolineando la necessità del suo sviluppo sostenibile ed etico.This paper scrutinises the interplay between Artificial Intelligence (AI) and sustainable development, critically evaluating AI’s environmental footprint, which encompasses considerable energy consumption and CO2 emissions during model training. The paper underscores the importance of policy interventions, with a focus on the incorporation of sustainability criteria in the AI Act. In conclusion, the paper acknowledges AI’s transformative potential in addressing pressing global issues such as energy poverty, underscoring the necessity for its sustainable and ethical development

Synthetic Aperture Radar Interferometry for Structural Health Monitoring of Bridges: Potentialities and Open Research Questions

The development of synthetic aperture radar (SAR) interferometry has provided unprecedented opportunities to remotely analyze the behavior of civil structures, transcending traditional limitations associated with in-situ methods. However, while the effectiveness of SAR technology in monitoring wide-area geohazards is demonstrated in several applications, its extension to civil structures, which have a much smaller footprint, requires further investigation of several aspects. This paper investigates the potentialities and challenges connected with the use of SAR technology for civil engineering artifacts, fostered by the availability of remote satellite open data. Recently, the European Space Agency has introduced the European Ground Motion Service (EGMS) under the Copernicus program. This innovative and freely accessible resource provides comprehensive information regarding ground motion across Europe through multitemporal interferometric analysis of Sentinel-1 images acquired since 2015. In this paper the focus is on the Palatino Bridge in Rome, Italy. Data from the ascending and descending orbit are combined to obtain vertical and longitudinal displacements of the structure, allowing for a better estimation of the bridge's response to varying environmental conditions. Results are then compared with those obtained processing high resolution data from COSMO-SkyMed of the Italian Space Agency, showing the consistency of findings

Value of Information Analysis Accounting for Sensor Data Quality: focus on drift

Structural health monitoring plays a crucial role in assessing the condition of civil structures, providing information for regular maintenance and post-disaster emergency management. However, the reliability of structural health monitoring outcomes can be compromised by sensor malfunctions. Over the past two decades, sensor validation tools have been proposed to identify and discard abnormal measurements before extracting information from the structural health monitoring system. The long-term benefits of structural health monitoring systems are commonly evaluated without considering the possibility of faulty sensors. This can lead to suboptimal maintenance decisions. Recently, a Bayesian decision theory-based framework has been introduced to account for different data quality issues and quantify the benefit of implementing a sensor validation tool. This novel approach expands the traditional Value of Information concept to encompass multiple "functioning" states of the structural health monitoring system. This paper mainly focused on a specific data quality issue, i.e., bias or drift in the monitoring outcome. Previous applications of this framework regard simplified decision scenarios, where the monitoring system was either “damaged” or “undamaged”, considering a fixed drift value. In this paper, the impact of uncertain drift levels on the Value of Information in structural health monitoring is investigated, addressing real-world complexities. A numerical case study is considered to illustrate the practical implications of the VoI framework

AI & Civil Liability

When dealing with novel fast-evolving technologies that are deemed ever more complex, autonomous, capable of learning and modifying themselves, and thus opaque and unpredictable, it is essential to assess the adequacy of civil liability rules

Experimental verification of the interpolation method on a real damaged bridge

The identification of damage in a bridge from changes in its vibrational behavior is an inverse problem of important practical value. Significant advances have been obtained on this topic in the last two-three decades, both from the theoretical and applied point of view. One of the main problems when dealing with the assessment of vibration based damage identification methods is the lack of experimental data recorded on real damaged structures. Due to this, a large number of damage identification algorithms are tested using data simulated by numerical models. The availability of data recorded on a damaged bridge before its demolition gave the authors the uncommon chance to verify the sensitivity and reliability of the IDDM basing on data recorded on a real structure. Specifically data recorded on a reinforced concrete single-span supported bridge in the Municipality of Dogna (Friuli, Italy) were used to apply the damage localization algorithm. Harmonically forced tests were conducted after imposing artificial, increasing levels of localized damage. In this paper the sensitivity of the method is discussed with respect to the number of instrumented locations and to the severity of the damage scenarios considere

Funnel metadynamics as accurate binding free-energy method

A detailed description of the events ruling ligand/protein interac- tion and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have de- veloped a metadynamics-based approach, named funnel metady- namics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an effi- cient characterization of the binding free-energy surface and an accurate calculation of the absolute protein–ligand binding free energy. We illustrate our protocol in two systems, benzamidine/ trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray con- formation has been found as the lowest free-energy pose, and the computed protein–ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils im- portant information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel meta- dynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science