Abstract
Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed 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.
Highlights
Marine biofouling is the undesired accumulation of micro-organisms, e.g., bacteria, cyanobacteria, unicellular algae and protozoa, and macro-organisms, e.g., seaweeds, barnacles, mussels and shells, on artificial water-immersed surfaces in a dynamic process that starts immediately after water submersion and can be a fast or slow process taking only hours or months to develop, respectively [1]
After the exploration of models derived with molecular descriptors and false positives (FP), we investigated the inclusion of 3D descriptors such as radial distribution function (RDF) descriptors and the selection of descriptors using the Random Forests (RF) descriptor importance parameter for the best three sets (Sub FPs, ExtCDK FPs and 1D&2D descriptors)
A computer-aided drug design (CADD) approach relying on ligand- and structure-based methodologies was successfully used to predict new inhibitory marine natural products (MNPs) against antifouling acetylcholinesterase enzyme (AChE)
Summary
Marine biofouling is the undesired accumulation of micro-organisms, e.g., bacteria, cyanobacteria, unicellular algae and protozoa, and macro-organisms, e.g., seaweeds, barnacles, mussels and shells, on artificial water-immersed surfaces in a dynamic process that starts immediately after water submersion and can be a fast or slow process taking only hours or months to develop, respectively [1]. All the MNPs from the virtual screening libraries that were predicted to belong to the active class, i.e., 125 MNPs, were selected to proceed to the CADD structure-based method, where 125 MNPs selected by QSAR approach were screened by molecular docking against the AChE enzyme. In this CADD approach, a virtual screening hit list comprising 19 MNPs was assented based on some established thresholds, such as the probability of being active in the best antifouling model and the prediction of affinity between the AChE arrrr....DDDDrrrruuuuggggssss2222000022222222,,,,22220000,,,,xxxxFFFFOOOORRRRPPPPEEEEEEEERRRRRRRREEEEVVVVIIIEIEEEWWWW.
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