Leishmaniasis: Omics Approaches to Understand its Biology from Molecule to Cell Level.

Leishmaniasis is the second deadliest vector-borne, neglected tropical zoonotic disease and is found in a variety of clinical forms based on genetic background. Its endemic type is present in tropical, sub-tropical and Mediterranean areas around the world which accounts for a lot of deaths every year. Currently, a variety of techniques are available for detection of leishmaniasis each technique having it's own pros and cons. The advancing next-generation sequencing (NGS) techniques are employed to find out novel diagnostic markers based on single nucleotide variants. A total of 274 NGS studies are available in European Nucleotide Archive (ENA) portal (https://www.ebi.ac.uk/ena/browser/home) that focused on wild-type and mutated Leishmania, differential gene expression, miRNA expression, and detection of aneuploidy mosaicism by omics approaches. These studies have provided insights into the population structure, virulence, and extensive structural variation, including known and suspected drug resistance loci, mosaic aneuploidy and hybrid formation under stressed conditions and inside the midgut of the sandfly. The complex interactions occurring within the parasite-host-vector triangle can be better understood by omics approaches. Further, advanced CRISPR technology allows researchers to delete and modify each gene individually to know the importance of genes in the virulence and survival of the disease-causing protozoa. In vitro generation of Leishmania hybrids are helping to understand the mechanism of disease progression in its different stages of infection. This review will give a comprehensive picture of the available omics data of various Leishmania spp. which helped to reveal the effect of climate change on the spread of its vector, the pathogen survival strategies, emerging antimicrobial resistance and its clinical importance.

Repurposing small molecules of Tephrosia purpurea against SARS-CoV-2 main protease.

Coronavirus infection is a communicable disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which emerged as a global pandemic with deteriorating effect on the world's population. Main protease (Mpro) of SARS-CoV-2 plays a significant role in the viral replication, transcription and disease propagation as well as a potential candidate for drug discovery and development for COVID-19 infection. The current study employed state of art structure-based drug discovery to decipher the role of phytochemicals of Tephrosia purpurea against Mpro. Tephrosia purpurea is being used as a traditional medicinal plant for the treatment of cough, breathlessness and fever as per the Indian Materia Medica. Screening of the phytochemicals of Tephrosia purpurea against Mpro was performed using molecular docking approach to identify the top 5 hits (+)-tephrorin B, deguelin, vitamin p, lanceolarin and 3beta-hydroxy-20(29)-lupene with binding energy of -8.4, -8.1, -8.0, -7.8, and -7.8 kcal/mol, respectively. Furthermore, identified top 5 hits were subjected to drug-likeness and toxicity prediction as well as MM-GBSA calculation. Out of the five molecules four molecules were predicted not to comprise any mutagenic and carcinogenic effects. Top two molecules based on the drug-likeness properties for oral bio-availability were further analysed by molecular dynamics simulation at 100 ns timescale. It was observed from the dynamic behaviour of the two complexes that the addition of these molecules changed the conformation and stability of the apo protein; thus may act as inhibitors for Mpro.Communicated by Ramaswamy H. Sarma.

The interaction analysis between human serum albumin with chlorpyrifos and its derivatives through sub-atomic docking and molecular dynamics simulation techniques.

Chlorpyrifos (CPF) is an extensively used organophosphate pesticide for crop protection. However, there are concerns about it contaminating the environment and human health, with estimated three lakh deaths annually. The molecular modeling protocol was assisted in redesigning thirteen well-known CPF linkers and inserting them at five selectable CPF (R1-R5) positions of CPF to get 258 CPF derivatives. CPF and its derivatives were optimized using LigPrep and docked to a grid centralized on Trp214 using extra precision glide docking. The Binding free energy of complexes was calculated using molecular mechanics/generalized born surface area (MM-GBSA). CPF and CPFD-225 have glide scores of - 3.08 and - 6.152 kcal/mol, respectively, with human serum albumin and ΔG bind for CPF (- 33.041817 kcal/mol) (- 52.825 kcal/mol) for CPF-D225. The top ten CPF derivatives showed at least ninefold better binding free energy than the CPF proposed for polyclonal antibody production. Subsequently, molecular docking studies revealed that CPF and its derivatives could bind to human serum albumin (HSA). Furthermore, using the Desmond package, a 100-ns molecular dynamics (MD) simulation was performed on the potential binding site. The final systems of CPF-HSA and CPF-222D complexes consist of 76,014 and 76,026 atoms, respectively. The physical stability of both the systems (CPF-HSA and CPF-222D) was analyzed by considering the overall potential energy, RMSF, RMSD, Hydrophobic interactions, and water-mediated patterns, which showed total energy of - 141,610 kcal/mol and - 140,150 kcal/mol, respectively. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03344-7.

Screening and Development of Transglutaminase-2 Inhibitors and their Derivative as Anti-lung Cancer Agent by in silico and in vitro Approaches.

AIM: This study aimed at screening and development of TG2 inhibitors as anti lung cancer agent. BACKGROUND: Transglutaminase 2 (TG2) is multifunctional and ubiquitously expressed protein from the transglutaminase family. It takes part in various cellular processes and plays an important role in the pathogenesis of autoimmune, neurodegerative diseases, and also cancer. OBJECTIVE: The proposed study focused on screening potent inhibitors of TG2 by in-silico method and synthesize their derivative as well as analyse its activity by utilizing an in-vitro approach. MATERIALS AND METHODS: Molecular docking studies have been carried out on the different classes of TG2 inhibitors against the target protein. Nearly thirty TG2 inhibitors were selected from literature and docking was performed against transglutaminase 2. The computational ADME property screening was also carried out to check their pharmacokinetic properties. The compounds which exhibited positive ADME properties with good interaction while possessing the least binding energy were further validated for their anti-lung cancer inhibition property against A549 cell lines using cytotoxicity studies. RESULTS: The results of the present study indicate that the docked complex formed by cystamine showed better binding affinity towards target protein, so this derivative of cystamine was formed using 2,5 dihydrobenzoic acid. Invitro results revealed that both molecules proved to be good cytotoxic agents against A549 lung cancer (875.10, 553.22 µg/ml), respectively. Further, their activity needs to be validated on TG2 expressing lung cancer. CONCLUSION: Cystamine and its derivative can act as a potential therapeutic target for lung cancer but its activity should be further validated on TG2 expressing lung cancer.

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis.

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype-phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of Mycobacterium tuberculosis (Mtb) complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance. It is understood that drug resistance and lineage-specific issues require an elaborate prioritization of identified variants to choose the best target for subsequent therapeutic intervention. Mycobacterium variant pipeline (MycoVarP) addresses these specific issues with a flexible battery of user-defined and default filters. It provides an end-to-end solution for WGS analysis of Mtb variants from the raw reads and performs two quality checks, viz, before trimming and after alignments of reads to the reference genome. MycoVarP maps the annotated variants to the drug-susceptible (DS) database and removes the false-positive variants, provides lineage identification, and predicts potential drug resistance. We have re-analyzed the WGS data reported by Advani et al. (2019) using MycoVarP and identified some additional variants not reported so far. We conclude that MycoVarP will help in identifying nonsynonymous, true-positive, drug resistance-associated variants more effectively and comprehensively, including those within the IDP of the PE-PPE/PGRS family, than possible from the currently available pipelines.

E-pharmacophore-based virtual screening to identify GSK-3ß inhibitors.

Glycogen synthase kinase-3ß (GSK-3ß) is a serine/threonine kinase which has attracted significant attention during recent years in drug design studies. The deregulation of GSK-3ß increased the loss of hippocampal neurons by triggering apoptosis-mediating production of neurofibrillary tangles and alleviates memory deficits in Alzheimer's disease (AD). Given its role in the formation of neurofibrillary tangles leading to AD, it has been a major therapeutic target for intervention in AD, hence was targeted in the present study. Twenty crystal structures were refined to generate pharmacophore models based on energy involvement in binding co-crystal ligands. Four common e-pharmacophore models were optimized from the 20 pharmacophore models. Shape-based screening of four e-pharmacophore models against nine established small molecule databases using Phase v3.9 had resulted in 1800 compounds having similar pharmacophore features. Rigid receptor docking (RRD) was performed for 1800 compounds and 20 co-crystal ligands with GSK-3ß to generate dock complexes. Interactions of the best scoring lead obtained through RRD were further studied with quantum polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area. Comparing the obtained leads to 20 co-crystal ligands resulted in 18 leads among them, lead1 had the lowest docking score, lower binding free energy and better binding orientation toward GSK-3ß. The 50 ns MD simulations run confirmed the stable nature of GSK-3ß-lead1 docking complex. The results from RRD, QPLD, IFD and MD simulations confirmed that lead1 might be used as a potent antagonist for GSK-3ß.

Inhibitor design against JNK1 through e-pharmacophore modeling docking and molecular dynamics simulations.

c-Jun-NH2 terminal kinases (JNKs) come under a class of serine/threonine protein kinases and are encoded by three genes, namely JNK1, JNK2 and JNK3. Human JNK1 is a cytosolic kinase belonging to mitogen-activated protein kinase (MAPK) family, which plays a major role in intracrinal signal transduction cascade mechanism. Overexpressed human JNK1, a key kinase interacts with other kinases involved in the etiology of many cancers, such as skin cancer, liver cancer, breast cancer, brain tumors, leukemia, multiple myeloma and lymphoma. Thus, to unveil a novel human JNK1 antagonist, receptor-based pharmacophore modeling was performed with the available eighteen cocrystal structures of JNK1 in the protein data bank. Eighteen e-pharmacophores were generated from the 18 cocrystal structures. Four common e-pharmacophores were developed from the 18 e-pharmacophores, which were used as three-dimensional (3D) query for shape-based similarity screening against more than one million small molecules to generate a JNK1 ligand library. Rigid receptor docking (RRD) performed using GLIDE v6.3 for the 1683 compounds from in-house library and 18 cocrystal ligands with human JNK1 from lower stringency to higher stringency revealed 17 leads. Further to derive the best leads, dock complexes obtained from RRD were studied further with quantum-polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area (MM-GBSA). Four leads have showed lesser binding free energy and better binding affinity towards JNK1 compared to 18 cocrystal ligands. Additionally, JNK1-lead1 complex interaction stability was reasserted using 50 ns MD simulations run and also compared with the best resolute cocrystal structure using Desmond v3.8. Thus, the results obtained from RRD, QPLD, IFD and MD simulations indicated that lead1 might be used as a potent antagonist toward human JNK1 in cancer therapeutics.

Genome-based approaches to develop epitope-driven subunit vaccines against pathogens of infective endocarditis.

Infective endocarditis (IE) has emerged as a public health problem due to changes in the etiologic spectrum and due to involvement of resistant bacterial strains with increased virulence. Developing potent vaccine is an important strategy to tackle IE. Complete genome sequences of eight selected pathogens of IE paved the way to design common T-cell driven subunit vaccines. Comparative genomics and subtractive genomic analysis were applied to identify adinosine tri phosphate (ATP)-binding cassette (ABC) transporter ATP-binding protein from Streptococcus mitis (reference organism) as common vaccine target. Reverse vaccinology technique was implemented using computational tools such as ProPred, SYFPEITHI, and Immune epitope database. Twenty-one T-cell epitopes were predicted from ABC transporter ATP-binding protein. Multiple sequence alignment of ABC transporter ATP-binding protein from eight selected IE pathogens was performed to identify six conserved T-cell epitopes. The six selected T-cell epitopes were further evaluated at structure level for HLA-DRB binding through homology modeling and molecular docking analysis using Maestro v9.2. The proposed six T-cell epitopes showed better binding affinity with the selected HLA-DRB alleles. Subsequently, the docking complexes of T-cell epitope and HLA-DRBs were ranked based on XP Gscore. The T-cell epitope (208-LNYITPDVV-216)-HLA-DRB1(∗)0101 (1T5 W) complex having the best XP Gscore (-13.25 kcal/mol) was assessed for conformational stability and interaction stability through molecular dynamic simulation for 10 ns using Desmond v3.2. The simulation results revealed that the HLA-DRB-epitope complex was stable throughout the simulation time. Thus, the epitope would be ideal candidate for T-cell driven subunit vaccine design against infective endocarditis.

Para-(benzoyl)-phenylalanine as a potential inhibitor against LpxC of Leptospira spp.: homology modeling, docking, and molecular dynamics study.

Leptospira interrogans, a Gram-negative bacterial pathogen is the main cause of human leptospirosis. Lipid A is a highly immunoreactive endotoxic center of lipopolysaccharide (LPS) that anchors LPS into the outer membrane of Leptospira. Discovery of compounds inhibiting lipid-A biosynthetic pathway would be promising for dissolving the structural integrity of membrane leading to cell lysis and death of Leptospira. LpxC, a unique enzyme of lipid-A biosynthetic pathway was identified as common drug target of Leptospira. Herein, homology modeling, docking, and molecular dynamics (MD) simulations were employed to discover potential inhibitors of LpxC. A reliable tertiary structure of LpxC in complex with inhibitor BB-78485 was constructed in Modeller 9v8. A data-set of BB-78485 structural analogs were docked with LpxC in Maestro v9.2 virtual screening workflow, which implements three stage Glide docking protocol. Twelve lead molecules with better XP Gscore compared to BB-78485 were proposed as potential inhibitors of LpxC. Para-(benzoyl)-phenylalanine - that showed lowest XP Gscore (-10.35 kcal/mol) - was predicted to have best binding affinity towards LpxC. MD simulations were performed for LpxC and para-(benzoyl)-phenylalanine docking complex in Desmond v3.0. Trajectory analysis showed the docking complex and inter-molecular interactions was stable throughout the entire production part of MD simulations. The results indicate para-(benzoyl)-phenylalanine as a potent drug molecule against leptospirosis. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:10.