The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2.

The objectives of this study were to evaluate tetrahydropyridine derivatives as efflux inhibitors and to understand the mechanism of action of the compounds by in silico studies. Minimum inhibitory concentration (MIC) determination, fluorometric methods and docking simulations were performed. The compounds NUNL02, NUNL09 and NUNL10 inhibited efflux, and NUNL02 is very likely a substrate of the transporter protein AcrB. Docking studies suggested that the mechanism of action could be by competition with substrate for binding sites and protein residues. We showed for the first time the potential of tetrahydropyridines as efflux inhibitors and highlighted compound NUNL02 as an AcrB-specific inhibitor. Docking studies suggested that competition is the putative mechanism of action of these compounds.

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Recent advances in Bioinformatics and in Computer simulation and modelling have positively impacted the drug discovery process by turning viable the rational drug design (RDD). One of the major challenges in RDD is the understanding about protein-ligand interaction simulated at the atomic level by molecular docking algorithms. Virtual screening (VS) is defined as a computational approach applied to the analyses of large libraries of chemical structures in order to identify possible drug candidates to a target. The major challenge of VS based on molecular docking is the time required to run each experiment and the countless parameters and characteristics that should be defined by the researcher such as: the target(s) receptor, one or a set of ligands, the receptor binding site and so on. In order to perform more realistic docking simulations it is also necessary to account for the receptor and ligand flexibility. Therefore, this paper presents a framework for VS, where the user configure an experiment in a Web based platform informing the path of input and output files as well as the size, center and variation of the binding site(s). Then, the proposed framework generates a Python script that performs the VS experiment on the users personal computer. We expect that researchers from diverse backgrounds as Biology, Physics, Pharmacy, etc. can easily prepare VS experiments without the necessity of learning how to write scripts. To * Corresponding Author validate our proposed framework we performed five different case studies considering the AcrB protein as target receptor. All the case studies were easily realized using the proposed framework. The results show that the framework is effective to configure the VS experiments with different characteristics. Besides, the experiments can help on the search for new drug candidates for this important target.

Protein Data Bank (PDB) is a public web database with more than 100,000 biological macromolecular structures. With this large amount of protein structures available on PDB the use of tools for acquisition and analysis of specific sets of biological macromolecules is a necessity. Hence, in this work we propose the development of a tool for acquiring, storing and analyzing specific sets of proteins from the PDB database. The proposed tool runs on desktop environment allowing the user to acquire the structures from the RESTful web-service provided by PDB server. After the acquisition of a set of interesting PDBs the user can manipulate these data in an off-line environment through a local database that stores the information about the characteristics of the structures, for example, ligands, mutations, residues, sequences and docking results. The protein files are locally stored in the users' computer and can be used, for instance, for molecular docking simulations and alignment of sequences and structures. Having a set of proteins of interest available locally and using our proposed tool the user can perform analysis related to alignments and visualize important proteins characteristics improving the knowledge about specific target. Besides, the user can select PDB files to be visualized on a graphical environment that is integrated in our tool. Other features are related to the exporting of sequence alignments results in csv (comma separated value) format or exporting sequences that have a similar identity in a format that can be easily loaded on graph tools. These alignments allow the user to visualize which proteins are similar and discard those that are not.