Effects of changes in glycan composition on glycoprotein dynamics: example of N-glycans on insulin receptor.

Glycosylation is among the most common post-translational modifications in proteins, although it is observed in only about 10% of all the protein structures in protein data bank (PDB). Modifications of sugar composition in glycoproteins profoundly impact the overall physiology of the organism. One such example is the development of insulin resistance, which has been attributed to the removal of sialic acid residues from N-glycans of insulin receptor (IR) from various experimental studies. How such modifications affect the glycan-glycoprotein dynamics, and ultimately their function is not clearly understood to date. In this study, we performed molecular dynamics simulations of glycans in different environments. We studied the effects of removal of sialic acid on the glycan, as well as on the dynamics of leucine-rich repeat L1 domain of the IR ectodomain. We observed perturbations in L1 domain dynamics as a result of the removal of sialic acid. The perturbations include an increase in the flexibility of insulin-binding residues, which may affect insulin binding with IR. These changes are accompanied by perturbations in glycan-protein interactions and perturbation of long-range allosteric dynamics. Our observations will further aid in understanding the role of sugars in maintaining homeostasis and how changes in glycan composition may lead to perturbations in homeostasis, ultimately leading to conditions such as insulin resistance.

The transcriptome enables the identification of candidate genes behind medicinal value of Drumstick tree (Moringa oleifera).

Moringa oleifera is a plant well-known for its nutrition value, drought resistance and medicinal properties. cDNA libraries from five different tissues (leaf, root, stem, seed and flower) of M. oleifera cultivar Bhagya were generated and sequenced. We developed a bioinformatics pipeline to assemble transcriptome, along with the previously published M. oleifera genome, to predict 17,148 gene models. Few candidate genes related to biosynthesis of secondary metabolites, vitamins and ion transporters were identified. Expressions were further confirmed by real-time quantitative PCR experiments for few promising leads. Quantitative estimation of metabolites, as well as elemental analysis, was also carried out to support our observations. Enzymes in the biosynthesis of vitamins and metabolites like quercetin and kaempferol are highly expressed in leaves, flowers and seeds. The expression of iron transporters and calcium storage proteins were observed in root and leaves. In general, leaves retain the highest amount of small molecules of interest.

Genome-wide survey and phylogeny of S-Ribosylhomocysteinase (LuxS) enzyme in bacterial genomes.

BACKGROUND: The study of survival and communication of pathogenic bacteria is important to combat diseases caused by such micro-organisms. Bacterial cells communicate with each other using a density-dependent cell-cell communication process called Quorum Sensing (QS). LuxS protein is an important member of interspecies quorum-sensing system, involved in the biosynthesis of Autoinducer-2 (AI-2), and has been identified as a drug target. Despite the above mentioned significance, their evolution has not been fully studied, particularly from a structural perspective. RESULTS: Search for LuxS in the non-redundant database of protein sequences yielded 3106 sequences. Phylogenetic analysis of these sequences revealed grouping of sequences into five distinct clusters belonging to different phyla and according to their habitat. A majority of the neighbouring genes of LuxS have been found to be hypothetical proteins. However, gene synteny analyses in different bacterial genomes reveal the presence of few interesting gene neighbours. Moreover, LuxS gene was found to be a component of an operon in only six out of 36 genomes. Analysis of conserved motifs in representative LuxS sequences of different clusters revealed the presence of conserved motifs common to sequences of all the clusters as well as motifs unique to each cluster. Homology modelling of LuxS protein sequences of each cluster revealed few structural features unique to protein of each cluster. Analyses of surface electrostatic potentials of the homology models of each cluster showed the interactions that are common to all the clusters, as well as cluster-specific potentials and therefore interacting partners, which may be unique to each cluster. CONCLUSIONS: LuxS protein evolved early during the course of bacterial evolution, but has diverged into five subtypes. Analysis of sequence motifs and homology models of representative members reveal cluster-specific structural properties of LuxS. Further, it is also shown that LuxS protein may be involved in various protein-protein or protein-RNA interactions, which may regulate the activity of LuxS proteins in bacteria.

The mutual and dynamic role of TSPO and ligands in their binding process: An example with PK-11195.

Translocator protein (TSPO) is an 18 kDa transmembrane protein, localized primarily on the outer mitochondrial membrane. It has been found to be involved in various physiological processes and pathophysiological conditions. Though studies on its structure have been performed only recently, there is little information on the nature of dynamics and doubts about some structures referenced in the literature, especially the NMR structure of mouse TSPO. In the present work, we thoroughly study the dynamics of mouse TSPO protein by means of atomistic molecular dynamics simulations, in presence as well as in absence of the diagnostic ligand PKA. We considered two starting structures: the NMR structure and a homology model (HM) generated on the basis of X-ray structures from bacterial TSPO. We examine the conformational landscape in both the modes for both starting points, in presence and absence of the ligand, in order to measure its impact for both structures. The analysis highlights high flexibility of the protein globally, but NMR simulations show a surprisingly flexibility even in the presence of the ligand. Interestingly, this is not the case for HM calculations, to the point that the ligand seems not so stable as in the NMR system and an unbinding event process is partially sampled. All those results tend to show that the NMR structure of mTSPO seems not deficient but is just in another portion of the global conformation space of TSPO.

Evaluation of the inhibitory potential of bioactive compounds against SARS-CoV-2 by in silico approach.

CONTEXT: The COVID-19 (coronavirus disease 19) pandemic brought on by the SARS-CoV-2 outbreak (severe acute respiratory syndrome coronavirus 2) has stimulated the exploration of various available chemical compounds that could be used to treat the infection. This has driven numerous researchers to investigate the antiviral potential of several bioactive compounds from medicinal plants due to their reduced adverse effects compared to chemicals. Some of the bioactive compounds used in folklore treatment strategies are reported as effective inhibitors against the proliferative and infective cycles of SARS-CoV-2. The secondary metabolites from plants are generally used to treat various diseases due to their intact medicinal properties. The present study analyzes the inhibitory potential of phytochemicals from medicinal plants like Sphaeranthus indicus, Lantana camara, and Nelumbo nucifera against SARS-CoV-2 by molecular docking. METHODS: Ten druggable protein targets from SARS-CoV-2 are docked against the phytochemicals from the selected medicinal plants. The phytocompounds astragalin, isoquercetin, and 5-hydroxy-7-methoxy-6-c-glycosy flavone were found to have lower binding energy depicting their inhibitive potential compared with the reported inhibitors that are used in the treatment of SARS-CoV-2 infection. The phytocompounds found to have the least binding energy were selected for further analyses. To assess the compounds' potential as drugs, their ADMET characteristics were also examined. Sphaeranthus indicus, Lantana camara, and Nelumbo nucifera six possible compounds were separately screened for ADME and toxicity characteristics; then, the results were analyzed. To assess the impact of the phytocompound binding on the dynamics of SARS-CoV-2 ribonuclease protein NSP15, microsecond-level all atomistic molecular dynamics simulations were performed, and their dynamics were analyzed. Microsecond-level molecular dynamics simulations of both the ligands complexed with NSP15 revealed that the ligand induces allosteric effects on NSP15, which could lead to destabilization of NSP15 hexameric interface and loss of RNA binding. The low binding energy exhibited by the phytochemicals from Lantana camera, Sphaeranthus indicus, and Nelumbo nucifera against the protein targets of SARS-CoV-2 showed inhibitory potential by the selected molecules. Their predicted interference of the enzymes involved in the molecular mechanisms aiding the proliferation of SARS-CoV-2 indicated the inhibitive ability of the phytochemicals.

Modelling and Simulations of Extracellular Glycoproteins.

While the knowledge of protein structure and function has seen vast advances in previous decades, the understanding of how their posttranslational modifications, such as glycosylations, influence their structure and function remains poor. However, advances in in silico methodologies to study glycosylations in recent past have enabled us to study this and understand the role of glycosylations in protein structure and function in ways that would not be possible by conventional experimental methods. In this chapter, we will demonstrate how to leverage these methodologies to study glycoproteins and their structural and dynamic properties using molecular modelling techniques.

How molecular modelling can better broaden the understanding of glycosylations.

Glycosylations are among the most ubiquitous post-translational modifications (PTMs) in proteins, and the effects of their perturbations are seen in various diseases such as cancers, diabetes and arthritis to name a few. Yet they remain one of the most enigmatic aspects of protein structure and function. On the other hand, molecular modelling techniques have been rapidly bridging this knowledge gap since the last decade. In this review, we discuss how these techniques have proven to be indispensable for a better understanding of the role of glycosylations in glycoprotein structure and function.

A knowledge-driven protocol for prediction of proteins of interest with an emphasis on biosynthetic pathways.

This protocol describes a stepwise process to identify proteins of interest from a query proteome derived from NGS data. We implemented this protocol on Moringa oleifera transcriptome to identify proteins involved in secondary metabolite and vitamin biosynthesis and ion transport. This knowledge-driven protocol identifies proteins using an integrated approach involving sensitive sequence search and evolutionary relationships. We make use of functionally important residues (FIR) specific for the query protein family identified through its homologous sequences and literature. We screen protein hits based on the clustering with true homologues through phylogenetic tree reconstruction complemented with the FIR mapping. The protocol was validated for the protein hits through qRT-PCR and transcriptome quantification. Our protocol demonstrated a higher specificity as compared to other methods, particularly in distinguishing cross-family hits. This protocol was effective in transcriptome data analysis of M. oleifera as described in Pasha et al.•Knowledge-driven protocol to identify secondary metabolite synthesizing protein in a highly specific manner.•Use of functionally important residues for screening of true hits.•Beneficial for metabolite pathway reconstruction in any (species, metagenomics) NGS data.

Dataset for the combined transcriptome assembly of M. oleifera and functional annotation.

In this paper, we present the data acquired during transcriptome analysis of the plant Moringa oleifera [1] from five different tissues (root, stem, leaf, flower and seed) by RNA sequencing. A total of 271 million reads were assembled with an N50 of 2094 bp. The combined transcriptome was assessed for transcript abundance across five tissues. The protein coding genes identified from the transcripts were annotated and used for orthology analysis. Further, enzymes involved in the biosynthesis of select medicinally important secondary metabolites, vitamins and ion transporters were identified and their expression levels across tissues were examined. The data generated by RNA sequencing has been deposited to NCBI public repository under the accession number PRJNA394193 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA394193).