Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal

Methods for control over learning individual trajectory

The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects

LIFECYCLE MANAGEMENT, MONITORING AND ASSESSMENT FOR SAFE LARGE-SCALE INFRASTRUCTURES: CHALLENGES AND NEEDS

Many European infrastructures dating back to ’50 and ’60 of the last century like bridges and viaducts are approaching the end of their design lifetime. In most European countries costs related to maintenance of infrastructures reach a quite high percentage of the construction budget and additional costs in terms of traffic delay are due to downtime related to the inspection and maintenance interventions. In the last 30 years, the rate of deterioration of these infrastructures has increased due to increased traffic loads, climate change related events and man-made hazards. A sustainable approach to infrastructures management over their lifecycle plays a key role in reducing the impact of mobility on safety (over 50 000 fatalities in EU per year) and the impact of greenhouse gases emission related to fossil fuels. The events related to the recent collapse of the Morandi bridge in Italy tragically highlighted the sheer need to improve resilience of aging transport infrastructures, in order to increase the safety for people and goods and to reduce losses of functionality and the related consequences. In this focus Structural Health Monitoring (SHM) is one of the key strategies with a great potential to provide a new approach to performance assessment and maintenance over the life cycle for an efficient, safe, resilient and sustainable management of the infrastructures. In this paper research efforts, needs and challenges in terms of performance monitoring, assessment and standardization are described and discussed.The networking support of COST Action TU1402 on ‘Quantifying the Value of Structural Health Monitoring’ and of COST Action TU1406 on ‘Quality specifications for roadway bridges, standardization at a European level (BridgeSpec)

Towards Empathetic Social Robots: Investigating the Interplay between Facial Expressions and Brain Activity

The pursuit of creating empathetic social robots that can understand and respond to human emotions is a critical challenge in Robotics and Artificial Intelligence. Social robots, designed to interact with humans in various settings, from healthcare to customer service, require a sophisticated understanding of human emotional states to resonate and effectively assist truly. Our research contributes to this ambitious goal by exploring the relationship between natural facial expressions and brain activity in these human-robot interactions, as captured by electroencephalogram (EEG) signals. This paper presents our initial steps towards this attempt. We want to find which areas in the participant user’s brain are most activated and how these activations correlate with facial expressions. Understanding these correlations is essential for developing social robots that recognize and empathize with various human emotions. Our approach combines neuroscience and computer science, offering a novel perspective in the quest to enhance the emotional intelligence of social robots. We share some preliminary results on a new multimodal dataset that we are developing, providing valuable insights into the potential of our work to improve the personalization and emotional depth of social robot interactions

22q11.2 Deletion Syndrome. Impact of Genetics in the Treatment of Conotruncal Heart Defects

Congenital heart diseases represent one of the hallmarks of 22q11.2 deletion syndrome. In particular, conotruncal heart defects are the most frequent cardiac malformations and are often associated with other specific additional cardiovascular anomalies. These findings, together with extracardiac manifestations, may affect perioperative management and influence clinical and surgical outcome. Over the past decades, advances in genetic and clinical diagnosis and surgical treatment have led to increased survival of these patients and to progressive improvements in postoperative outcome. Several studies have investigated long-term follow-up and results of cardiac surgery in this syndrome. The aim of our review is to examine the current literature data regarding cardiac outcome and surgical prognosis of patients with 22q11.2 deletion syndrome. We thoroughly evaluate the most frequent conotruncal heart defects associated with this syndrome, such as tetralogy of Fallot, pulmonary atresia with major aortopulmonary collateral arteries, aortic arch interruption, and truncus arteriosus, highlighting the impact of genetic aspects, comorbidities, and anatomical features on cardiac surgical treatment

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model

Sampling protein motion and solvent effect during ligand binding.

An exhaustive description of the molecular recognition mechanism between a ligand and its biological target is of great value because it provides the opportunity for an exogenous control of the related process. Very often this aim can be pursued using high resolution structures of the complex in combination with inexpensive computational protocols such as docking algorithms. Unfortunately, in many other cases a number of factors, like protein flexibility or solvent effects, increase the degree of complexity of ligand/protein interaction and these standard techniques are no longer sufficient to describe the binding event. We have experienced and tested these limits in the present study in which we have developed and revealed the mechanism of binding of a new series of potent inhibitors of Adenosine Deaminase. We have first performed a large number of docking calculations, which unfortunately failed to yield reliable results due to the dynamical character of the enzyme and the complex role of the solvent. Thus, we have stepped up the computational strategy using a protocol based on metadynamics. Our approach has allowed dealing with protein motion and solvation during ligand binding and finally identifying the lowest energy binding modes of the most potent compound of the series, 4-decyl-pyrazolo[1,5-a]pyrimidin-7-one

Vibration issues in timber structures: A state-of-the-art review

The increasing use of timber structures worldwide has brought attention to the challenges posed by their lightweight nature, making them more prone to vibrations than more massive structures. Consequently, significant research efforts have been dedicated to understanding and mitigating vibrations in timber structures, while scientific committees strive to establish suitable design regulations. This study aims to classify and identify the main research themes related to timber structure vibrations and highlight future research needs and directions. A bibliometricbased selection process briefly introduces each research topic, presenting the latest findings and proposals for vibration design in timber structures. The paper emphasizes the key outcomes and significant contributions to understanding and addressing vibration issues in timber structures. These findings serve as valuable guidance for researchers, designers, and regulatory bodies involved in designing and assessing timber structures subjected to vibrations

A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD)

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