A Computational and Modeling Study of the Reaction Mechanism of Staphylococcus aureus Monoglycosyltransferase Reveals New Insights on the GT51 Family of Enzymes

Metagenomic Analysis of Microbial Community Associated with Food Waste Composting

Food waste is an increasing cause of concern in India. Its management through composting plays a vital role in managing the biodegradable fraction of municipal solid waste. However, the existing composting process has many challenges, such as the lack of optimum microenvironment and microbiome knowledge, which limits efficient outcomes. Therefore, the present study aims to bridge the gap by applying metagenomics to study microbial community dynamicity during different stages of composting. The bacterial community analysis showed that genus Marionobacter (9.4%) and Halomonas (7.4%) were prevalent during the mesophilic stage, whereas the Bacillus (12.2%) and Cellulomonas (0.1%) were prevalent during the thermophilic and maturation stage of composting. The functional profiling of metagenome indicated the abundance of genes involved in degradation of polymeric compounds such as carbohydrates, lipids, and proteins. The relative abundance of arginine and proline metabolisms increased during the thermophilic stage. Whereas the relative abundance of genes involved in fatty acid, tryptophan, galactose, and propanoate metabolisms declined. Similarly, the CAZyme tool predicted that the genes encoding for glycoside hydrolase (GH) families were higher during the mesophilic and thermophilic stages of composting. These enzymes play an important role in degradation of complex polysaccharides such as cellulose and hemicellulose. The data obtained from the present study could be utilized for the optimization and improving the composting process.

A Comprehensive Study of Factors Affecting the Prediction of the pKa Shift of Asp26 in Thioredoxin Protein

The stable protonation state of ionizable amino acids in a protein can be predicted by computing the pKa shift of that residue within the protein environment. Thermodynamic Integration (TI) is an ideal molecular dynamics-based approach for predicting the pKa shift of ionizable protein residues. Here, we probe TI-based simulation protocols for their ability to accurately predict the pKa shift of Asp26 in thioredoxin. While implicit solvent models can predict the pKa shift accurately, explicit solvent models result in substantial errors. To understand the underlying reason for this surprising discrepancy, we investigate the role of various factors such as solvent models, conformational sampling, background charges, and polarization.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.