Marine Brominated Tyrosine Alkaloids as Promising Inhibitors of SARS-CoV-2

There have been more than 150 million confirmed cases of SARS-CoV-2 since the beginning of the pandemic in 2019. By June 2021, the mortality from such infections approached 3.9 million people. Despite the availability of a number of vaccines which provide protection against this virus, the evolution of new viral variants, inconsistent availability of the vaccine around the world, and vaccine hesitancy, in some countries, makes it unreasonable to rely on mass vaccination alone to combat this pandemic. Consequently, much effort is directed to identifying potential antiviral treatments. Marine brominated tyrosine alkaloids are recognized to have antiviral potential. We test here the antiviral capacity of fourteen marine brominated tyrosine alkaloids against five different target proteins from SARS-CoV-2, including main protease (Mpro) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and non-structural protein 10 (nsp10) (PDB ID: 6W4H). These marine alkaloids, particularly the hexabrominated compound, fistularin-3, shows promising docking interactions with predicted binding affinities (S-score = −7.78, −7.65, −6.39, −6.28, −8.84 Kcal/mol) for the main protease (Mpro) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and non-structural protein 10 (nsp10) (PDB ID: 6W4H), respectively, where it forms better interactions with the protein pockets than the native interaction. It also shows promising molecular dynamics, pharmacokinetics, and toxicity profiles. As such, further exploration of the antiviral properties of fistularin-3 against SARS-CoV-2 is merited.

Hydroxide ions amplified by water entanglement underly the mechanism of general anesthesia

It is believed that inhaled anesthetics occupy hydrophobic pockets within target proteins, but how inhaled anesthetics with diverse shapes and sizes fit into highly structurally selective pockets is unknown. For hydroxide ions are hydrophobic, we determined whether hydroxide ions could bridge inhaled anesthetics and protein pockets. We found that small additional load of cerebral hydroxide ions decreases anesthetic potency. Multiple-water entanglement network, derived from Ising model, has a great ability to amplify ultralow changes in the cerebral hydroxide ion concentration, and consequently, amplified hydroxide ions account for neural excitability. Molecular dynamics simulations showed that inhaled anesthetics produce anesthesia by attenuating the formation of multiple-water entanglement network. This work suggests amplified hydroxide ions underlying a unified mechanism for the anesthetic action of inhaled anesthetics.

DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. In addition, the majority of drug-like ligands contained in a data set of RNA pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets.

Exploiting cyanine dye J-aggregates/monomer equilibrium in hydrophobic protein pockets for efficient multi-step phototherapy: An innovative concept for smart nanotheranostics

After several decades of development in the field of near-infrared (NIR) dyes for photothermal therapy (PTT), indocyanine green (ICG) still remains the only FDA-approved NIR contrast agent. However, upon NIR...

DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery.However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. Further, the majority of drug-like ligands contained in a data set of RNA-containing pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets

DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery.However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. Further, the majority of drug-like ligands contained in a data set of RNA-containing pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets

QSIdb: quorum sensing interference molecules

Quorum sensing interference (QSI), the disruption and manipulation of quorum sensing (QS) in the dynamic control of bacteria populations could be widely applied in synthetic biology to realize dynamic metabolic control and develop potential clinical therapies. Conventionally, limited QSI molecules (QSIMs) were developed based on molecular structures or for specific QS receptors, which are in short supply for various interferences and manipulations of QS systems. In this study, we developed QSIdb (http://qsidb.lbci.net/), a specialized repository of 633 reported QSIMs and 73 073 expanded QSIMs including both QS agonists and antagonists. We have collected all reported QSIMs in literatures focused on the modifications of N-acyl homoserine lactones, natural QSIMs and synthetic QS analogues. Moreover, we developed a pipeline with SMILES-based similarity assessment algorithms and docking-based validations to mine potential QSIMs from existing 138 805 608 compounds in the PubChem database. In addition, we proposed a new measure, pocketedit, for assessing the similarities of active protein pockets or QSIMs crosstalk, and obtained 273 possible potential broad-spectrum QSIMs. We provided user-friendly browsing and searching facilities for easy data retrieval and comparison. QSIdb could assist the scientific community in understanding QS-related therapeutics, manipulating QS-based genetic circuits in metabolic engineering, developing potential broad-spectrum QSIMs and expanding new ligands for other receptors.

Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein–Drug and Protein–Protein Structures: An Analysis of Kinetically Active Residues

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.

A Comprehensive Analysis of Protein Data Bank reveals Low Desolvation Penalty in π-Cation System

Cation-π interactions widely exist between ligand-protein interfaces, attracting much attention in molecular recognition in recent years. Interactions named cation-π and π-cation (cationic vs arene small molecular ligands) shall be separately considered in drug and pesticide design process. The two interactions involved in ligands and protein pockets may differ in energy features and therefore offers significant inspiration for drug and pesticide design. However, an in-depth study on differences between cation-π and π-cation systems from an energy perspective is still lacking. In this study, we calculated and compared cation-π and π-cation systems in terms of physicochemical properties of ligand/protein and solvation effect. It seems that the desolvation penalty of the cation-π systems was relatively higher than the π-cation pairs, even though these interactions both can improve the ligand activity. This is the reason for evolution converged on π-cation interactions in the cation-π-mediated proteins. The π-cation interaction facilitating the inhalation of ligand to the pocket may provide a new sight for the molecular design of pharmaceuticals and pesticides.

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

<div>Motivation: The detection of small molecules binding sites in proteins is central to structure based drug design. Many tools were developed in the last 40 years, but only few of them are available today, open-source, and suitable for the analysis of large databases or for the integration in automatic workflows. In addition, no software can characterize subpockets solely with the information of the protein structure, a pivotal concept in fragment-based drug design.</div><div>Results: CAVIAR is a new open source tool for protein cavity identification and rationalization. Protein pockets are automatically detected based on the protein structure. It comprises a subcavity segmentation algorithm that decomposes binding sites into subpockets without requiring the presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology, medicinal chemistry and ligand identification efforts. Our analysis of the entire PDB and the</div><div>PDBBind confirms that liganded cavities tend to be bigger, more hydrophobic and more complex than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding affinity scales relatively well with the number of subcavities filled by the ligand. Compounds binding to more than three of the subcavities identified by CAVIAR are mostly in the nanomolar or better range of affinities to their target.</div><div>Availability and implementation: Installation notes, user manual and support for CAVIAR are available at https://jr-marchand.github.io/caviar/. The CAVIAR GUI and CAVIAR command line tool are available on GitHub at https://github.com/jr-marchand/caviar and the package is hosted on Anaconda cloud at https://anaconda.org/jr-marchand/caviar under a MIT license. The GitHub</div><div>repository also hosts the validation datasets.</div>