Category: 2019

Understanding the structure-function relationships of RNA has become increasingly important given the realization of its functional role in various cellular processes. Chemical denaturation of RNA by urea has been shown to be beneficial in investigating RNA stability and folding. Elucidation of the mechanism of unfolding of RNA by urea is important for understanding the folding pathways. In addition to studying denaturation of RNA in aqueous urea, it is important to understand the nature and strength of interactions of the building blocks of RNA. In this study, a systematic examination of the structural features and energetic factors involving interactions between nucleobases and urea is presented. 

Inappropriate and uncontrolled use of antibiotics results in the emergence of antibiotic resistance, thereby threatening the present clinical regimens to treat infectious diseases. Therefore, new antimicrobial agents that can prevent bacteria from developing drug resistance are urgently needed. Selective disruption of bacterial membranes is the most effective strategy for combating microbial infections as accumulation of genetic mutations will not allow for the emergence of drug resistance against these antimicrobials. In this work, we tested cholic acid (CA) derived amphiphiles tethered with different alkyl chains for their ability to combat Gram-positive bacterial infections. In-depth biophysical and biomolecular simulation studies suggested that the amphiphile with a hexyl chain (6) executes more effective interactions with Gram-positive bacterial membranes as compared to other hydrophobic counterparts.

The gold-palladium (Au−Pd) bimetallic nanocluster (NC) catalyst in colloidal phase performs the homocoupling reaction of various aryl chlorides (Ar−Cl) under ambient conditions. We have systematically investigated various aspects of the Au−Pd NC catalysts with respect to this homocoupling reaction by using density functional theory (DFT) calculations, genetic algorithm (GA) approaches, and molecular dynamics (MD) simulations. 

Comparative study of the efficiency of Au, Ag, Pd and Pt based mono and bimetallic trimer clusters for the CO oxidation reaction Saumya Gurtua, Sandhya Raib and U. Deva Priyakumara* aCenter for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad-500 032, India bTheoretical Science Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bengaluru-560 001, India 

Hydroxymethylbilane synthase (HMBS), the third enzyme in the heme biosynthesis pathway, catalyzes the formation of 1-hydroxymethylbilane (HMB) by a stepwise polymerization of four molecules of porphobilinogen (PBG) using the dipyrromethane (DPM) cofactor. The mechanism by which HMBS polymerizes four units of PBG has not been elucidated to date. In vitro and in silico studies on HMBS have suggested certain residues with catalytic importance, but their specific role in the catalysis is unclear. To understand the catalytic mechanism of HMBS, quantum mechanical (QM) calculations were performed on model systems obtained from the active site of the human HMBS enzyme. The addition of one molecule of PBG to the DPM cofactor is carried out in four steps: (1) protonation of the substrate, PBG; (2) deamination of PBG; (3) electrophilic addition of the deaminated substrate to the terminal pyrrole ring of the enzyme-bound DPM cofactor and (4) deprotonation of the carbon atom at the α-position of the second ring of DPM. Based on the energy profiles from the QM calculations on cluster models, R26 is proposed to be the best suitable proton donor to the PBG moiety, which aids in the deamination of the substrate. During the electrophilic addition step, the intermediate formed is stabilized by the carboxylate side chain of the D99 residue. 

In recent times, our healthcare system is being challenged by many drug-resistant microorganisms and ageing-associated diseases for which we do not have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related biomedical fields, there have been dramatic increase in the number of drugs approved in general, but still way behind in drug discovery for certain class of diseases. Now, we have access to bigger genomics database, better biophysical methods,  and knowledge about chemical space with which we should be able to easily explore and predict synthetically feasible compounds for the lead optimization process. In this chapter, we discuss the limitations and highlights of currently available computational methods used for protein–ligand binding affinities estimation and this includes force-field, ab initio electronic structure theory and machine learning approaches.