TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa.

Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

Selection of Bis-Indolyl Pyridines and Triphenylamines as New Inhibitors of SARS-CoV-2 Cellular Entry by Modulating the Spike Protein/ACE2 Interfaces.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the infectious agent that has caused the current coronavirus disease (COVID) pandemic. Viral infection relies on the viral S (spike) protein/cellular receptor ACE2 interaction. Disrupting this interaction would lead to early blockage of viral replication. To identify chemical tools to further study these functional interfaces, 139,146 compounds from different chemical libraries were screened through an S/ACE2 in silico virtual molecular model. The best compounds were selected for further characterization using both cellular and biochemical approaches, reiterating SARS-CoV-2 entry and the S/ACE2 interaction. We report here two selected hits, bis-indolyl pyridine AB-00011778 and triphenylamine AB-00047476. Both of these compounds can block the infectivity of lentiviral vectors pseudotyped with the SARS-CoV-2 S protein as well as wild-type and circulating variant SARS-CoV-2 strains in various human cell lines, including pulmonary cells naturally susceptible to infection. AlphaLISA and biolayer interferometry confirmed a direct inhibitory effect of these drugs on the S/ACE2 association. A specific study of the AB-00011778 inhibitory properties showed that this drug inhibits viral replication with a 50% effective concentration (EC50) between 0.1 and 0.5 µM depending on the cell lines. Molecular docking calculations of the interaction parameters of the molecules within the S/ACE2 complex from both wild-type and circulating variants of the virus showed that the molecules may target multiple sites within the S/ACE2 interface. Our work indicates that AB-00011778 constitutes a good tool for modulating this interface and a strong lead compound for further therapeutic purposes.

Drug Repurposing Targeting Pseudomonas aeruginosa MvfR Using Docking, Virtual Screening, Molecular Dynamics, and Free-Energy Calculations.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium responsible for acute and chronic infections in planktonic state or in biofilms. The sessile structures are known to confer physical stability, increase virulence, and work as a protective armor against antimicrobial compounds. P. aeruginosa can control the expression of genes, population density, and biofilm formation through a process called quorum sensing (QS), a rather complex and hierarchical system of communication. A recent strategy to try and overcome bacterial resistance is to target QS proteins. In this study, a combined multi-level computational approach was applied to find possible inhibitors against P. aeruginosa QS regulator protein MvfR, also known as PqsR, using a database of approved FDA drugs, as a repurposing strategy. Fifteen compounds were identified as highly promising putative MvfR inhibitors. On those 15 MvfR ligand complexes, molecular dynamic simulations and MM/GBSA free-energy calculations were performed to confirm the docking predictions and elucidate on the mode of interaction. Ultimately, the five compounds that presented better binding free energies of association than the reference molecules (a known antagonist, M64 and a natural inducer, 2-nonyl-4-hydroxyquinoline) were highlighted as very promising MvfR inhibitors.

Perspectives on the Role of Enzymatic Biocatalysis for the Degradation of Plastic PET.

Plastics are highly durable and widely used materials. Current methodologies of plastic degradation, elimination, and recycling are flawed. In recent years, biodegradation (the usage of microorganisms for material recycling) has grown as a valid alternative to previously used methods. The evolution of bioengineering techniques and the discovery of novel microorganisms and enzymes with degradation ability have been key. One of the most produced plastics is PET, a long chain polymer of terephthalic acid (TPA) and ethylene glycol (EG) repeating monomers. Many enzymes with PET degradation activity have been discovered, characterized, and engineered in the last few years. However, classification and integrated knowledge of these enzymes are not trivial. Therefore, in this work we present a summary of currently known PET degrading enzymes, focusing on their structural and activity characteristics, and summarizing engineering efforts to improve activity. Although several high potential enzymes have been discovered, further efforts to improve activity and thermal stability are necessary.

The Biofilms Structural Database.

The Biofilms Structural Database (BSD) is a collection of structural, mutagenesis, kinetics, and inhibition data to understand the processes involved in biofilm formation. Presently, it includes curated information on 425 structures of proteins and enzymes involved in biofilm formation and development for 42 different bacteria. It is available at www.biofilms.biosim.pt.