Insights into the application of polyhydroxyalkanoates derivatives from the combination of experimental and simulation approaches

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

Modelling across Multiple Scales to Design Biopolymer Membranes for Sustainable Gas Separations: 1—Atomistic Approach

In this work, we assessed the CO2 and CH4 sorption and transport in copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV), which showed good CO2 capture potential in our previous papers, thanks to their good solubility–selectivity, and are potential biodegradable alternatives to standard membrane-separation materials. Experimental tests were carried out on a commercial material containing 8% of 3-hydroxyvalerate (HV), while molecular modelling was used to screen the performance of the copolymers across the entire composition range by simulating structures with 0%, 8%, 60%, and 100% HV, with the aim to provide a guide for the selection of the membrane material. The polymers were simulated using molecular dynamics (MD) models and validated against experimental density, solubility parameters, and X-ray diffraction. The CO2/CH4 solubility–selectivity predicted by the Widom insertion method is in good agreement with experimental data, while the diffusivity–selectivity obtained via mean square displacement is somewhat overestimated. Overall, simulations indicate promising behaviour for the homopolymer containing 100% of HV. In part 2 of this series of papers, we will investigate the same biomaterials using a macroscopic model for polymers and compare the accuracy and performance of the two approaches.

In-silico Computational Investigations of AntiViral Lignan Derivatives as Potent Inhibitors of SARS CoV-2

Due to alarming outbreak of pandemic COVID-19 in recent times, there is a strong need to discover and identify new antiviral agents acting against SARS CoV-2. Among natural products, lignan derivatives have been found effective against several viral strains including SARS CoV-2. Total of twenty-seven reported antiviral lignan derivatives of plant origin have been selected for computational studies to identify the potent inhibitors of SARS CoV-2. Molecular docking study has been carried out in order to predict and describe molecular interaction between active site of enzyme and lignan derivatives. Out of identified hits, clemastatin B and erythro-strebluslignanol G demonstrated stronger binding and high affinity with all selected proteins. Molecular dynamics simulation studies of clemastin B and savinin against promising targets of SARS CoV-2 have revealed their inhibitory potential against SARS CoV-2. In fine, in-silico computational studies have provided initial breakthrough in design and discovery of potential SARS CoV-2 inhibitors.

In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis

Severe acute respiratory syndrome has relapsed recently as novel coronavirus causing a life threat to the entire world in the absence of an effective therapy. To hamper the replication of the deadly SARS CoV-2 inside the host cells, systematic in silico virtual screening of total 267,324 ligands from Asinex EliteSynergy and BioDesign libraries has been performed using AutoDock Vina against RdRp. The molecular modeling studies revealed the identification of twenty-one macrocyclic hits (2-22) with better binding energy than remdesivir (1), marketed SARS CoV-2 inhibitor. Further, the analysis using rules for drug-likeness and their ADMET profile revealed the candidature of these hits due to superior oral bioavailability and druggability. Further, the MD simulation studies of top two hits (2 and 3) performed using GROMACS 2020.1 for 10 ns revealed their stability into the docked complexes. These results provide an important breakthrough in the design of macrocyclic hits as SARS CoV-2 RNA replicase inhibitor.

22-Hydroxyhopane, a novel multitargeted phytocompound against SARS-CoV-2 from Adiantum latifolium Lam

The present pandemic disease COVID-19 demands an urgent need for more efficient antiviral drugs against SARS-CoV-2. 22-Hydroxyhopane is a bioactive triterpenoid compound with antibacterial activity, present in the leaves of Adiantum latifolium. In this study, molecular docking method revealed strong binding affinity of the compound for ten proteins essential for SARS-CoV-2 multiplication in host cells, including seven nonstructural proteins, two structural proteins and one receptor protein, with a binding energy of -7.61 to -9.82 kcal/mol and inhibition constant <1 μM. MDS and MM-PBSA analysis of the best ranked complex further confirmed the results. The targets selected include six enzymes, RNA binding protein, spike protein, membrane protein and ACE2 receptor of SARS-CoV-2. It is the first report of a natural compound from A. latifolium having multitargeted activity against SARS-CoV-2. We conclude that 22-hydroxyhopane may be used as a best source for the development of novel therapeutic drugs for COVID-19, but requires further evaluations.

In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis

Corona Virus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome coronavirus (SARS CoV-2) has been declared a worldwide pandemic by WHO recently. The complete understanding of the complex genomic structure of SARS CoV-2 has enabled the use of computational tools in search of SARS CoV-2 inhibitors against the multiple proteins responsible for its entry and multiplication in human cells. With this endeavor, 177 natural, anti-viral chemical entities and their derivatives, selected through the critical analysis of the literatures, were studied using pharmacophore screening followed by molecular docking against RNA dependent RNA polymerase and main protease. The identified hits have been subjected to molecular dynamic simulations to study the stability of ligand-protein complexes followed by ADMET analysis and Lipinski filters to confirm their drug likeliness. It has led to an important start point in the drug discovery and development of therapeutic agents against SARS CoV-2.