Role and Perspective of Molecular Simulation-Based Investigation of RNA–Ligand Interaction: From Small Molecules and Peptides to Photoswitchable RNA Binding

Aberrant RNA–protein complexes are formed in a variety of diseases. Identifying the ligands that interfere with their formation is a valuable therapeutic strategy. Molecular simulation, validated against experimental data, has recently emerged as a powerful tool to predict both the pose and energetics of such ligands. Thus, the use of molecular simulation may provide insight into aberrant molecular interactions in diseases and, from a drug design perspective, may allow for the employment of less wet lab resources than traditional in vitro compound screening approaches. With regard to basic research questions, molecular simulation can support the understanding of the exact molecular interaction and binding mode. Here, we focus on examples targeting RNA–protein complexes in neurodegenerative diseases and viral infections. These examples illustrate that the strategy is rather general and could be applied to different pharmacologically relevant approaches. We close this study by outlining one of these approaches, namely the light-controllable association of small molecules with RNA, as an emerging approach in RNA-targeting therapy.     

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.     

Volatiles and Antifungal-Antibacterial-Antiviral Activity of South African Salvia spp. Essential Oils Cultivated in Uniform Conditions

Spontaneous emissions of S. dentata Aiton and S. scabra Thunb., as well as the essential oil (EO) composition of the cited species, together with S. aurea L., were investigated. The chemical profile of the first two species is reported here for the first time. Moreover, in vitro tests were performed to evaluate the antifungal activity of these EOs on Trichophyton mentagrophytes, Microsporum canis, Aspergillus flavus, Aspergillus niger, and Fusarium solani. Secondly, the EO antibacterial activity against Escherichia coli, Staphylococcus aureus, and Staphylococcus pseudointermedius was examined, and their antiviral efficacy against the H1N1 influenza virus was assessed. Leaf volatile organic compounds (VOCs), as well as the EOs obtained from the arial part of Salvia scabra, were characterized by a high percentage of sesquiterpene hydrocarbons (97.8% and 76.6%, respectively), mostly represented by an equal amount of germacrene D (32.8% and 32.7%, respectively). Both leaf and flower spontaneous emissions of S. dentata, as well as the EO composition, showed a prevalence of monoterpenes divided into a more or less equal amount of hydrocarbon and oxygenated compounds. Interestingly, its EO had a non-negligible percentage of oxygenated sesquiterpenes (29.5%). S. aurea EO, on the contrary, was rich in sesquiterpenes, both hydrocarbons and oxygenated compounds (41.5% and 33.5%, respectively). S. dentata EO showed good efficacy (Minimal Inhibitory Concentration (MIC): 0.5%) against M. canis. The tested EOs were not active against E. coli and S. aureus, whereas a low inhibition of S. dentata EO was observed on S. pseudointermedius (MIC = 10%). Once again, S. dentata EO showed a very good H1N1 inhibition; contrariwise, S. aurea EO was completely inactive against this virus. The low quantity of S. scabra EO made it impossible to test its biological activity. S. dentata EO exhibited interesting new perspectives for medicinal and industrial uses.     

Hydrogen Peroxide Detection Using Prussian Blue-modified 3D Pyrolytic Carbon Microelectrodes

A highly sensitive amperometric Prussian blue-based hydrogen peroxide sensor was developed using 3D pyrolytic carbon microelectrodes. A 3D printed multielectrode electrochemical cell enabled simultaneous highly reproducible Prussian blue modification on multiple carbon electrodes. The effect of oxygen plasma pre-treatment and deposition time on Prussian blue electrodeposition was studied. The amperometric response of 2D and 3D sensors to the addition of hydrogen peroxide in μM and sub-μM concentrations in phosphate buffer was investigated. A high sensitivity comparable to flow injection systems and a detection limit of 0.16 μM was demonstrated with 3D pyrolytic carbon microelectrodes at stirred batch condition     

Thermal conductivity of isolated and interacting carbon nanotubes: comparing results from molecular dynamics and the boltzmann transport equation

We investigate the thermal conductivity of single-wall carbon nanotubes (SWCNT) either isolated or in contact with external media by using equilibrium molecular dynamics and the Boltzmann transport equation. We show that, contrary to existing controversies, both methods yield a finite value of the thermal conductivity for infinitely long tubes, as opposed to the case of 1D, momentum-conserving systems. Acoustic and flexure modes with mean free paths of the order of a few microns are identified as major contributors to the high value of SWCNT conductivity. We also find that the interaction with an external medium may substantially decrease the lifetime of the low-frequency vibrations, reducing the thermal conductivity by up to 2 orders of magnitude.     

Carbon Dioxide as a Protecting Group: Highly Efficient and Selective Catalytic Access to Cyclic cis‐Diol Scaffolds

The efficient and highly selective formation of a wide range of (hetero)cyclic cis‐diol scaffolds using aminotriphenolate‐based metal catalysts is reported. The key intermediates are cyclic carbonates, which are obtained in high yield and with high levels of diastereo‐ and chemoselectivity from the parent oxirane precursors and carbon dioxide. Deprotection of the carbonate structures affords synthetically useful cis‐diol scaffolds with different ring sizes that incorporate various functional groups. This atom‐efficient method allows the simple construction of diol synthons using inexpensive and accessible precursors and green metal catalysts and showcases the use of CO₂ as a temporary protecting group.     

PyCDFT: A Python package for constrained density functional theory

We present PyCDFT, a Python package to compute diabatic states using constrained density functional theory (CDFT). PyCDFT provides an object-oriented, customizable implementation of CDFT, and allows for both single-point self-consistent-field calculations and geometry optimizations. PyCDFT is designed to interface with existing density functional theory (DFT) codes to perform CDFT calculations where constraint potentials are added to the Kohn–Sham Hamiltonian. Here, we demonstrate the use of PyCDFT by performing calculations with a massively parallel first-principles molecular dynamics code, Qbox, and we benchmark its accuracy by computing the electronic coupling between diabatic states for a set of organic molecules. We show that PyCDFT yields results in agreement with existing implementations and is a robust and flexible package for performing CDFT calculations. The program is available at https://dx.doi.org/10.5281/zenodo.3821097 .