Synthetic circuit of inositol phosphorylceramide synthase in Leishmania: a chemical biology approach

Building circuits and studying their behavior in cells is a major goal of systems and synthetic biology. Synthetic biology enables the precise control of cellular states for systems studies, the discovery of novel parts, control strategies, and interactions for the design of robust synthetic systems. To the best of our knowledge, there are no literature reports for the synthetic circuit construction for protozoan parasites. This paper describes the construction of genetic circuit for the targeted enzyme inositol phosphorylceramide synthase belonging to the protozoan parasite Leishmania. To explore the dynamic nature of the circuit designed, simulation was done followed by circuit validation by qualitative and quantitative approaches. The genetic circuit designed for inositol phosphorylceramide synthase (Biomodels Database—MODEL1208030000) shows responsiveness, oscillatory and bistable behavior, together with intrinsic robustness.     

Synthesis of anomeric 1,5-anhydrosugars as conformationally locked selective α-mannosidase inhibitors

Anomeric 1,5-anhydrosugar 2 was synthesized from d-glucose derived N-Cbz protected aminodiol 8. The key step involves, acid catalyzed hydrolysis of 1,2-acetonide group in 8 to get hemiacetal that concomitantly undergoes formation of the pyranose ring by attack of C-3 hydroxyethyl group on anomeric C-1, leading to the formation of dioxabicyclo[3.2.1]octane skeleton which on hydrogenolyis gave 2. The glycosidase inhibitory activities of hydroxy- and amino-substituted anomeric 1,5-anhydrosugars 1 and 2, respectively, showed selective inhibition of α-mannosidase. These results were substantiated by molecular docking studies using WHAT IF software and AUTODOCK 4.0 program.     

Probing the Interactions Responsible for the Structural Stability of Trypanothione Reductase Through Computer Simulation and Biophysical Characterization

The Protein Journal - With the necessity to develop antileishmanial drugs with substrate specificity, trypanothione reductase (TryR) has gained popularity in parasitology. TryR is unique to be...     

Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics

An epidemic caused by COVID-19 in China turned into pandemic within a short duration affecting countries worldwide. Researchers and companies around the world are working on all the possible strategies to develop a curative or preventive strategy for the same, which includes vaccine development, drug repurposing, plasma therapy, and drug discovery based on Artificial intelligence. Therapeutic approaches based on Computational biology and Machine-learning algorithms are specially considered, with a view that these could provide a fast and accurate outcome in the present scenario. As an effort towards developing possible therapeutics for COVID-19, we have used machine-learning algorithms for the generation of alignment kernels from diverse viral sequences of Covid-19 reported from India, China, Italy and USA. Using these diverse sequences we have identified the conserved motifs and subsequently a peptide library was designed against them. Of these, 4 peptides have shown strong binding affinity against the main protease of SARS-CoV-2 (M^pro) and also maintained their stability and specificity under physiological conditions as observed through MD Simulations. Our data suggest that these evolutionary peptides against COVID-19 if found effective may provide cross-protection against diverse Covid-19 variants.     

Signaling networks in Leishmania macrophages deciphered through integrated systems biology: a mathematical modeling approach

Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.     

Molecular drug targets and structure based drug design: A holistic approach

Access to the complete human genome sequence as well as to the complete sequences of pathogenic organisms provides information that can result in an avalanche of therapeutic targets. Structure-based design is one of the first techniques to be used in drug design. Structure based design refers specifically to finding and complementing the 3D structure (binding and/or active site) of a target molecule such as a receptor protein. The aim of this review is to give an outline of studies in the field of structure based drug design that has helped in the discovery process of new drugs. The emphasis will be on comparative/homology modeling.     

Choke point analysis of metabolic pathways in E.histolytica: a computational approach for drug target identification

With the Entamoeba genome essentially complete, the organism can be studied from a whole genome standpoint. The understanding of cellular mechanisms and interactions between cellular components is instrumental to the development of new effective drugs and vaccines. Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in reconstructed biochemical reaction networks. Metabolic pathways illustrate how proteins work in concert to produce cellular compounds or to transmit information at different levels. Identification of drug targets in E. histolytica through metabolic pathway analysis promises to be a novel approach in this direction. This article focuses on the identification of drug targets by subjecting the Entamoeba genome to BLAST with the e-value inclusion threshold set to 0.005 and choke point analysis. A total of 86.9 percent of proposed drug targets with biological evidence are chokepoint reactions in Entamoeba genome database.     

Metabolic pathway analysis of S. pneumoniae: an in silico approach towards drug-design

The emergence of multidrug resistant varieties of Streptococcus pneumoniae (S. pneumoniae) has led to a search for novel drug targets. An in silico comparative analysis of metabolic pathways of the host Homo sapiens (H. sapiens) and the pathogen S. pneumoniae have been performed. Enzymes from the biochemical pathways of S. pneumoniae from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. Five pathways unique to the pathogen S. pneumoniae when compared to the host H. sapiens have been identified. Potential drug targets from these pathways could be useful for the discovery of broad-spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 161 distinct targets identified from these pathways, many are in various stages of progress at the Microbial Genome Database. However, 44 of the targets are new and can be considered for rational drug design. The study was successful in listing out potential drug targets from the S. pneumoniae proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatics approach can be extended to other pathogens of clinical interest.     

Identification of intrinsically disorder regions in non-structural proteins of SARS-CoV-2: New insights into drug and vaccine resistance

Molecular and Cellular Biochemistry - The outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 2019 and caused coronavirus disease 2019 (COVID-19), which...