A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

UIDB/50006/2020 UIDB/04378/2020 Norma transitória DL 57/2016The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.publishersversionpublishe

Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach

UIDB/50006/2020 Norma transit?ria DL 57/2016 UIDP/04378/2020 LA/P/0140/2020Biofouling is the undesirable growth of micro-and macro-organisms on artificial waterimmersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand-and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.publishersversionpublishe

Marine actinomycetes from Madeira Archipelago preliminary taxonomic studies

Financial support from Fundação para a Ciência e a Tecnologia (FCT) and FEDER (through grant n° PTDC/QUI-QUI/119116/2010, and projects PEst-C/EQB/LA0006/2011 and Pest-OE/BIA/UI0457/2011-CREM), and the EU 7th Framework Programme (FP7/2007-2013) under grant agreement n° PCOFUND-GA-2009-246542 and nº 269138-NanoGuard. We thank W. Fenical, P. R. Jensen and C. A. Kauffman, from SIO, CA, USA and P. Castilho from Universidade da Madeira, Portugal for the logistic support during sampling collection.publishersversionpublishe

The Madeira Archipelago As a Significant Source of Marine-Derived Actinomycete Diversity with Anticancer and Antimicrobial Potential

FCT/MEC (PTDC/QUIQUI/119116/2010; IF/00700/2014; UID/QUI/50006/2013; UID/Multi/04378/2013) ERDF (POCI-01-0145-FEDER - 007265; POCI-01-0145-FEDER-007728) EU 7th Framework Programme (FP7) (PCOFUND-GA-2009-246542; 269138-NanoGuard) CONACYTMarine-derived actinomycetes have demonstrated an ability to produce novel compounds with medically relevant biological activity. Studying the diversity and biogeographical patterns of marine actinomycetes offers an opportunity to identify genera that are under environmental pressures, which may drive adaptations that yield specific biosynthetic capabilities. The present study describes research efforts to explore regions of the Atlantic Ocean, specifically around the Madeira Archipelago, where knowledge of the indigenous actinomycete diversity is scarce. A total of 400 actinomycetes were isolated, sequenced, and screened for antimicrobial and anticancer activities. The three most abundant genera identified were Streptomyces, Actinomadura, and Micromonospora. Phylogenetic analyses of the marine OTUs isolated indicated that the Madeira Archipelago is a new source of actinomycetes adapted to life in the ocean. Phylogenetic differences between offshore (>100 m from shore) and nearshore (< 100 m from shore) populations illustrates the importance of sampling offshore in order to isolate new and diverse bacterial strains. Novel phylotypes from chemically rich marine actinomycete groups like MAR4 and the genus Salinispora were isolated. Anticancer and antimicrobial assays identified Streptomyces, Micromonospora, and Salinispora as the most biologically active genera. This study illustrates the importance of bioprospecting efforts at unexplored regions of the ocean to recover bacterial strains with the potential to produce novel and interesting chemistry.publishersversionpublishe

Marine Drug Discovery through Computer-Aided Approaches

Besides the importance of our oceans as oxygen factories, food providers, shipping pathways, and tourism enablers, oceans hide an unprecedented wealth of opportunities [...

Phylogenetic and chemical diversity of MAR4 streptomycete lineage

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

A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †

Financial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was supported by the LAQV, which is financed by national funds from FCT/MEC (UID/QUI/50006/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265). This work was also supported by the UCIBIO, which is financed by national funds from FCT/MEC (UID/Multi/04378/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-010145-FEDER-007728). The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R 2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data ( 1 H and 13 C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.publishersversionpublishe

QSAR-Assisted Virtual Screening of Lead-Like Molecules from Marine and Microbial Natural Sources for Antitumor and Antibiotic Drug Discovery

A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database—25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature

A Chemoinformatics Approach to the Discovery of Lead-Like Molecules from Marine and Microbial Sources En Route to Antitumor and Antibiotic Drugs

The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure–Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened—57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs

To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure&ndash;activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets