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We thank Nathalie Reichmann and Leendert Hamoen (University of Amsterdam) for critical reading of the manuscript, Ana Velic (Proteome Center Tübingen) for help with proteome analysis and Mike VanNieuwenhze (Indiana University) for the generous gift of HADA. This study was funded by the European Research Council through grant ERC‐2017‐CoG‐771709 (to MGP), by national funds through FCT– Fundação para a Ciência e a Tecnologia, PTDC/BIA‐MIC/6982/2020 (to HV); PTDC/BIA‐PLA/3432/2012 (to SRF); FCT through MOSTMICRO‐ITQB R&D Unit (UIDB/04612/2020, UIDP/04612/2020) and LS4FUTURE Associated Laboratory (LA/P/0087/2020) and FCT fellowship SFRH/BD/147052/2019 (to BMS); by the Swiss National National Foundation through P300P3_155346 (to AJ); by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska‐Curie grant agreement No 839596 (to SS) and by the European Molecular Biology Organization through award ALTF 673‐2018 (to SS). Figure 6D and Appendix Fig S7 were created with Biorender.com . A computer-aided drug design (CADD) approach was developed for a focused chemical library comprising a series of sixteen thiazolo[5,4-c]isoquinoline derivatives. Little is known about this group of heteroaromatic compounds, both from the point of view of their synthesis and their biological properties. First, our CADD approach included target prediction by Mondrian conformal prediction with the ChEMBL database. The acetylcholinesterase (AChE) was identified as having a high probability of thiazolo[5,4-c]isoquinolines being active against it. Secondly, the molecular docking predictions revealed four promising thiazoloisoquinolines (2, 7, 13 and 14) according to their prominent ligand-protein energy scores and relevant binding affinities with the AChE pocket residues. The subsequent in vitro evaluation of promising hits and related ones revealed a set of novel AChE inhibitors. Therefore, the findings reported herein may provide a new strategy for discovering novel AChE inhibitors. publishersversion published

Funding Information: This publication is based upon work from COST Action CA18238 (Ocean4Biotech), funded by the European Cooperation in Science and Technology (COST) Program in the period 2019–2023. SPG: This work is financed by national funds from FCT—Fundação para a Ciência e a Tecnologia, I.P., in the scope of the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB. LLB: The publication is part of a project that has received funding from the Erasmus + Project No. ECOBIAS_609967-EPP-1-2019-1-RS-EPPKA2-CBHE-JP; GA.2019-1991/001-001. Development of master curricula in ecological monitoring and aquatic bioassessment for Western Balkans HEIs/ECOBIAS. CJ: This work was supported by grants PID2021-122732OB-C22 from MCIN/AEI/10.13039/501100011033/FEDER “A way to make Europe” (AEI, Spanish State Agency for Research and FEDER Programme from the European Union) and RTI2018-093634-B-C22 from the State Agency for Research (AEI) of Spain, co-funded by the FEDER Programme from the European Union, and BLUEBIOLAB (0474_BLUEBIOLAB_1_E), Programme INTERREG V A of Spain-Portugal (POCTEP). FP: This work is financed by national funds from FCT—Fundação para a Ciência e a Tecnologia, I.P., for an Assistant Research Position (CEECIND/01649/2021). MC: INTERREG-MAC2/1.1b/279 (AHIDAGRO) and the Ministerio de Ciencia e Innovación (Spain) (grant PID2020-115979RR-C32). ARDM is supported with funds from Proyecto Intramural Especial CSIC [Ref. 202280I032]. Publisher Copyright: © 2023 by the authors. Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation. publishersversion published

Grant: SFRH/BPD/108237/2015. POCI-01-0145-FEDER-007265. Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review. publishersversion published

Additional file 1: Figure S1. Graphical representation of the DFT-DM vs. a) DMNBO and b) DMPEOE for the test set. Figure S2. Predicted vs. DFT-calculated DM for the 3368 molecular structures of the test set using the model MACCS_DMNBO_DMPEOE.

This work was supported by the Calouste Gulbenkian Foundation [Programme 'Qualifying New Generations 'grant number 137831], Associated Laboratory for Sustainable Chemistry-Clean Processes and Technologies-LAQV which is financed by Portuguese national funds from Fundacao para a Ciencia e a Tecnologia/MEC [UID/QUI/50006/2013] and co-financed by the ERDF under the PT2020 Partnership Agreement [POCI-01-0145-FEDER-007265]; and NOVA-LINCS [PEest/UID/CEC/04516/2013]. The representation of metabolic reactions strongly relies on visualization, which is a major barrier for blind users. The NavMol software renders the communication and interpretation of molecular structures and reactions accessible by integrating chemoinformatics and assistive technology. NavMol 3.0 provides a molecular editor for metabolic reactions. The user can start with templates of reactions and build from such cores. Atom-to-atom mapping enables changes in the reactants to be reflected in the products (and vice-versa) and the reaction centres to be automatically identified. Blind users can easily interact with the software using the keyboard and textto-speech technology. preprint published

The oceans are a highly complex microbiological environment with typical microbial abundances of 106 and 109 per ml in seawater and ocean-bottom sediments, respectively. Among them, marine actinobacteria, commonly named actinomycetes, are the greatest source of microbial derived natural products, accounting for ca. 75% of all antibiotics discovered until 2002.1 Therefore, in the last decades several efforts have been done regarding marine actinomycete natural product research, which allowed the discovery of significant novel actinomycete biodiversity. A total of 662 sediment samples were collected along Madeira Archipelago and processed for the isolation of actinomycetes. In total, 421 actinomycete strains were isolated, 80 (19%) of these, revealed an obligate requirement of seawater for growth. Among these seawater-obligate marine actinomycetes are specimens of MAR4 streptomycete lineage. The MAR4 lineage of streptomycetes (e.g. Streptomyces aculeolatus) has been reported to be largely of marine origin and recognized as a source of rare of hybrid-isoprenoid class of bacterial secondary metabolites, namely napyradiomycins (1), marinones (2), nitropyrrolins (3), and lavanducyanis (4)2, Figure 1. In addition, from a single strain, in many cases, were reported multiple bioactive derivatives within the same structural class.2 The phylogenetic tree provides clear resolution of the MAR4 streptomycete lineage, with clear separation of Streptomyces aculeolatus MAR4 strains from other Streptomyces aculeolatus strain (accession number EU741176). Our phylogenetic analysis is in accordance with previously published reports, which propose that strain (S. aculeolatus, EU741176) has not being a MAR4 strain, although it was obtained from marine sediment samples collected at the Caribbean Sea. Crude extracts were obtained from these strains; four crude-extracts showed antibacterial activity against both MRSA and VRE with MIC values of ≤ 0.04 μg/ml. Fractions of one of these strains besides antibacterial activity, showed cytotoxic activity against HCT-116 cell line, with IC50 values of 3.5 μg/ml. Additionally, this lineage has been linked to the production of hybrid isoprenoid secondary metabolites, which can display potent biological activities (e.g. the commercially important aminocoumarin antibiotics).3 Currently we are targeting their bioactive compounds for structured elucidation, which appear to be new napyradiomycin derivatives. Figure 1. Main secondary metabolites classes produced by MAR4 streptomycetes lineage. To date, phylogenetic characterization of 6 representative isolates, based on partial sequence of gene encoding 16S rRNA, confirm that these strains belong to the specie Streptomyces aculeolatus. Figure 2. Neighbour-joining phylogenetic tree created from 6 partial 16S rRNA gene sequence from Streptomyces aculeolatus strains cultured from Madeira Archipelago, based on 1000 bootstrap replicates. BLAST matches (deposited in GenBank) are included with species and strain name followed by accession number. Verrucosispora maris and Micromonospora aurantiaca were used as outgroups.