The Biodiveristy2Drugs project, embedded within the BIODIVERSA+ theme, is dedicated to harnessing the biodiversity of nature-derived peptides for drug discovery, aiming to significantly improve human health. This initiative stands at the crossroads of science, human well-being, and sustainability, promising to deliver transformative impacts in drug discovery and environmental protection. By exploring the rich biodiversity of peptides across various organisms, we aim to map their unique chemical space and understand their evolutionary trajectories, thereby identifying novel therapeutic lead compounds, and providing a rationale for preservation of flora, fauna, and their habitats. At its core, the project is structured around six objectives: (1) Peptide Mining and Biodiversity (2) Peptide Evolution and Ecology (3) Computer-Assisted Ligand Mapping and Optimization (4) Green Peptide Chemistry Pipeline (5) Pharmacological Screening and Validation (6) Translation for Societal Impact This interdisciplinary endeavor leverages state-of-the-art nano-analytical, advanced computational, high-throughput pharmacological and green chemistry methodologies to ensure minimal environmental impact while maximizing therapeutic value. Our approach is designed to have a global impact, with the project engaging a consortium of 25 academic partners (from Europe, America, Asia and Australia), which extends by engaging stakeholders across health and environmental sectors for significant societal impact through advanced scientific research merged with sustainability and social responsibility. In summary, our project leverages nature-based solutions to tackle critical global health and environmental challenges. By advancing drug discovery while fostering an eco-conscious future, we highlight the essential link between the natural world and our collective well-being, aiming to transform the landscape of drug discovery and environmental conservation.

In recent years, the number of identified non-proteinogenic amino acid-containing secondary metabolites and their biosynthetic gene clusters has greatly expanded in bacteria. A new family of stand-alone adenylation (A) domains involved in the incorporation of ß-amino acids has been previously described. Based on the protein structural analyses of three members of this new family, new ß-amino acid specificity-conferring codes have been proposed. A common specific feature of these stand-alone A domains is that they are co-encoded with a stand-alone acyl carrier protein domain. Based on these specific features, the French partner (BGPI) has identified two new stand-alone A domains expected to be involved in the incorporation of a ß-amino acid. These stand-alone A domains are present in two loci belonging to important pathogenic bacteria. According to their annotation, these loci encode two different new unknown molecules, respectively. Interestingly, by a genetic approach, these loci have both been shown to be required for the bacterial virulence. However, the chemical structure of the secondary metabolites synthesized by these loci remains unknown and the presence of a ß-amino acid has never been yet suspected and explored. This project aims at elucidating the chemical structure of the molecules encoded by these two loci, deciphering their biosynthesis pathways, and refining the functional assignment of their biosynthesis genes. This project will contribute to a better understanding of the incorporation of ß-amino acids by stand-alone A domains in bacteria. Since the targeted microorganisms are important pathogens, this project will essentially contribute to a deeper understanding of their pathogenicity. Consequently, the fight against these pathogens should therefore be facilitated by new data arising from the project. Since genome mining is an important approach to discover new loci encoding new natural products with potentially novel biological activities, this project proposes to mine bacterial genomic sequences available in Genbank for the presence of new loci encoding a stand-alone A domain specific of a ß amino acid. Depending on the biological activity of the characterized ß-amino acid-containing secondary metabolites, this project potentially could lead to industrial applications as antibiotics or plant protection agents. Patents obtained would potentially also contribute to the visibility of the project. The French and German partners BGPI and TU Berlin have developed a long-standing collaboration since 2005, working together on several projects including the structural characterization of the potent antibiotic albicidin.

Scientific consensus posits that contemporary automotive friction brakes discharge a commensurate quantity of particulate matter when compared to combustion engines. These friction brake particles present pronounced hazards to both human health and the environment, particularly in the context of escalating global urbanization. Presently, the intricate mechanisms governing particle release from the frictional surfaces remain inadequately elucidated. The nuanced roles played by nano to micro scales of the tribological circuit, meso-scale spatial contact temperature fields, and macroscopic structural vibrations are poorly comprehended. Simultaneously, emissions under dynamic loads are a) propelled by enigmatic multi-factorial and multi-physical effects within high-dimensional spaces, b) subject to transient dynamical mechanisms spanning multiple scales, c) significantly influenced by load history effects and memory effects, and d) notably emitted during specific, albeit unknown, complex loading patterns coinciding with unfavorable friction surface states. This collaborative initiative seeks to comprehensively scrutinize brake particulate emissions, at their source, through a series of sophisticated experiments, high-fidelity numerical simulations, and cutting-edge machine learning methodologies. The endeavor aims to unveil the particle emission mechanisms intricately linked to multi-scale load history effects, thermo-mechanical dynamics, and friction surface properties. The project endeavors to seamlessly integrate numerical simulations with data-driven models. An anticipated methodological breakthrough aims to disrupt the scientific impasse surrounding friction under severe conditions, considering the interplay between tribological matter flow and thermomechanical mechanisms and by combining physical models with advanced learning methods. The undertaking aspires to formulate an innovative particle reduction strategy centered on friction surface preconditioning, thereby governing the tribological circuit. The collaborative efforts between Technische Universität Berlin and the University of Lille offer a distinctive opportunity for fundamental research, leveraging complementary laboratory equipment and domain expertise. Prof. P. Dufrénoy (Lille) oversees one of Europe's most advanced tribology testing facilities. This experimental setup, encompassing structural dynamics, particulate emissions, high-resolution interface temperature measurements, and frictional surface topographies simultaneously, currently stands unrivaled in Germany. Prof. M. Stender from (Berlin) is a foremost authority in data-driven modeling of frictional processes. A pioneer in applying big data analysis and deep learning to brake system behavior modeling, he actively explores sensor data fusion techniques, image data processing, and explicating AI models within the context of mechanical systems. The collaboration benefits from the prior research synergy of the applicants, indicating their effective teamwork in previous endeavors.

In many regions of the world, including the Mediterranean and European areas, water needs for irrigation and municipal use are expected to increase. Ensuring these growing needs, in thie context of climate change, constitutes a major challenge, especially in the Mediterranean area. The situation is more complex ,in regions with a dry climate and overexploited water system and sustainable water management is imposed. In this process, the amount of data related to water management is growing. We propose to bring new opportunities offered by artificial intelligence and geospatial technologies by processing data, optimising water supply services, and gathering all stakeholders. This constitutes the overall objective of SAVE water project. Other specific objectives are : (i) Establishing a water system database including gathered Living labs data and already existing data. (ii) Developing an intelligent system based on real time monitoring water systems and predictive models (iii) Modelling the impact of land use and climate change on hydrological systems via models based on remote sensing (iv) Establishing a local adapted vulnerability model for the prevention and protection of highly risked water systems (v) Analysing socio-economic and environmental impacts as well as proposing sustainable pricing and insurance strategic plans against water scarcity and external shocks (vi) Integrating insights from a non- Mediterranean case study to enhance the generalizability and transferability of the project's outcomes. (vii) Developing a collaborative platform including all water system stakeholders (viii) Exploiting and out-scaling SAVE water innovative approach via communication, dissemination and exploitation plans The SAVE water project is directly related to Topic 2.1.1 (RIA*) - Effective water accounting approaches under crisis conditions : climate change and external shocks. This initiative strengthens crisis management with early warning systems and resilience strategies, aiming to address complex water issues collaboratively for long-term sustainability. Compared to previous research studies, the SAVE water project propose an innovative approach for water resources management and tries to address the following challenges : •The risk of using poor-quality data (incomplete datasets) in water resources management. To deal with such risk, we intend to combine AI algorithms and big data analytics to allow stakeholders to make more informed decisions and improve data-driven insights. •The modelling complexity: By incorporating artificial intelligence techniques, the accuracy and functionality of models can be enhanced by leveraging different datasets. •The absence of a common and open database at the service of decision makers and stakeholders. To address this issue, we intend to create a common interactive Dashboard that could serve as a pivotal solution. •The lack user participation and coordination between water system stakeholders. To address this challenge, we propose to create user-friendly platforms that allow the visualisation of data insights and facilitate collaborative planning •The discontinuity between government water policies, problems and needs of stakeholders. To deal with this issue, we intend to create AI-driven analytics and geospatial mapping to assist decision-makers in comprehending the impact of their actions on stakeholders. To achieve these objectives, we will benefit from the rich experience of an interdisciplinary team from several Mediterranean and European countries (2 Tunisia, 2 Morocco, 1 Algeria, 1 Germany, 2 Italy, 1 Egypt, 2 France, 1 Portugal, 1 Spain).