A computational workflow to determine drug candidates alternative to aminoglycosides targeting the decoding center of E. coli ribosome.

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.

Innovative Use of an Injectable, Self-Healing Drug-Loaded Pectin-Based Hydrogel for Micro- and Supermicro-Vascular Anastomoses.

Microvascular surgery plays a crucial role in reconnecting micrometer-scale vessel ends. Suturing remains the gold standard technique for small vessels; however, suturing the collapsed lumen of microvessels is challenging and time-consuming, with the risk of misplaced sutures leading to failure. Although multiple solutions have been reported, the emphasis has predominantly been on resolving challenges related to arteries rather than veins, and none has proven superior. In this study, we introduce an innovative solution to address these challenges through the development of an injectable lidocaine-loaded pectin hydrogel by using computational and experimental methods. To understand the extent of interactions between the drug and the pectin chain, molecular dynamics (MD) simulations and quantum mechanics (QM) calculations were conducted in the first step of the research. Then, a series of experimental studies were designed to prepare lidocaine-loaded injectable pectin-based hydrogels, and their characterization was performed by using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and rheological analysis. After all the results were evaluated, the drug-loaded pectin-based hydrogel exhibiting self-healing properties was selected as a potential candidate for in vivo studies to determine its performance during operation. In this context, the hydrogel was injected into the divided vessel ends and perivascular area, allowing for direct suturing through the gel matrix. While our hydrogel effectively prevented vasospasm and facilitated micro- and supermicro-vascular anastomoses, it was noted that it did not cause significant changes in late-stage imaging and histopathological analysis up to 6 months. We strongly believe that pectin-based hydrogel potentially enhanced microlevel arterial, lymphatic, and particularly venous anastomoses.

Mechanistic insights into the challenges of organocatalytic Beckmann rearrangement reactions.

Organocatalytic Beckmann rearrangement (BKR) reactions are of great interest for synthetic chemists interested in "green chemistry". There are different proposals for the reaction mechanism depending on the experimental conditions. Clarifying the details of the BKR reaction mechanism is important for the selectivity of amides and lactams yet to be synthesized. In this study, the DFT computational method at the M06-2X/6-31+G(d,p) level of theory in conjunction with the implicit PCM solvation method has been used to elucidate alternative pathways for the Beckmann rearrangement reaction at elevated temperatures. The results enabled us to explain details of the Beckmann rearrangement reaction via a Meisenheimer complex where the process was thermodynamically driven. Meisenheimer complexes are found to be highly stable species due to the presence of aromatic ring systems allowing electron delocalization.

Molecular Mechanism of Protein Arginine Deiminase 2: A Study Involving Multiple Microsecond Long Molecular Dynamics Simulations.

Peptidylarginine deiminase 2 (PAD2) is a Ca2+-dependent enzyme that catalyzes the conversion of protein arginine residues to citrulline. This kind of structural modification in histone molecules may affect gene regulation, leading to effects that may trigger several diseases, including breast cancer, which makes PAD2 an attractive target for anticancer drug development. To design new effective inhibitors to control activation of PAD2, improving our understanding of the molecular mechanisms of PAD2 using up-to-date computational techniques is essential. We have designed five different PAD2-substrate complex systems based on varying protonation states of the active site residues. To search the conformational space broadly, multiple independent molecular dynamics simulations of the complexes have been performed. In total, 50 replica simulations have been performed, each of 1 µs, yielding a total simulation time of 50 µs. Our findings identify that the protonation states of Cys647, Asp473, and His471 are critical for the binding and localization of the N-α-benzoyl-l-arginine ethyl ester substrate within the active site. A novel mechanism for enzyme activation is proposed according to near attack conformers. This represents an important step in understanding the mechanism of citrullination and developing PAD2-inhibiting drugs for the treatment of breast cancer.

Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has serious negative effects on health, social life, and economics. Recently, vaccines from various companies have been urgently approved to control SARS-CoV-2 infections. However, any specific antiviral drug has not been confirmed so far for regular treatment. An important target is the main protease (Mpro ), which plays a major role in replication of the virus. In this study, Gaussian and residue network models are employed to reveal two distinct potential allosteric sites on Mpro that can be evaluated as drug targets besides the active site. Then, Food and Drug Administration (FDA)-approved drugs are docked to three distinct sites with flexible docking using AutoDock Vina to identify potential drug candidates. Fourteen best molecule hits for the active site of Mpro are determined. Six of these also exhibit high docking scores for the potential allosteric regions. Full-atom molecular dynamics simulations with MM-GBSA method indicate that compounds docked to active and potential allosteric sites form stable interactions with high binding free energy (∆Gbind ) values. ∆Gbind values reach -52.06 kcal/mol for the active site, -51.08 kcal/mol for the potential allosteric site 1, and - 42.93 kcal/mol for the potential allosteric site 2. Energy decomposition calculations per residue elucidate key binding residues stabilizing the ligands that can further serve to design pharmacophores. This systematic and efficient computational analysis successfully determines ivermectine, diosmin, and selinexor currently subjected to clinical trials, and further proposes bromocriptine, elbasvir as Mpro inhibitor candidates to be evaluated against SARS-CoV-2 infections.