Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design.

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking.

The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

Prioritization of potential drug targets and antigenic vaccine candidates against Klebsiella aerogenes using the computational subtractive proteome-driven approach.

Klebsiella aerogenes is a multidrug-resistant Gram-negative bacterium that causes nosocomial infections. The organism showed resistance to most of the conventional antibiotics available. Because of the high resistance of the species, the treatment of K. aerogenes is difficult. These species are resistant to third-generation cephalosporins due to the production of chromosomal beta-lactams with cephalosporin activity. The lack of better treatment and the development of therapeutic resistance in hospitals hinders better/new broad-spectrum-based treatment against this pathogen. This study identifies potential drug targets/vaccine candidates through a computational subtractive proteome-driven approach. This method is used to predict proteins that are not homologous to humans and human symbiotic intestinal flora. The resultant proteome of K. aerogenes was further searched for proteins, which are essential, virulent, and determinants of antibiotic/drug resistance. Subsequently, their druggability properties were also studied. The data set was reduced based on its presence in the pathogen-specific metabolic pathways. The subtractive proteome analysis predicted 13 proteins as potential drug targets for K. aerogenes. Furthermore, these target proteins were annotated based on their spectrum of activity, cellular localization, and antigenicity properties, which ensured that they are potent candidates for broad-spectrum antibiotic and vaccine design. The results open up new opportunities for designing and manufacturing powerful antigenic vaccines against K. aerogenes and the detection and release of new and active drugs against K. aerogenes without altering the gut microbiome. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-021-00068-9.

Therapeutic Targets and Computational Approaches on Drug Development for COVID-19.

World Health Organization declared coronavirus disease (COVID-19) caused by SARS coronavirus-2 (SARS-CoV-2) as pandemic. Its outbreak started in China in Dec 2019 and rapidly spread all over the world. SARS-CoV-2 has infected more than 800,000 people and caused about 35,000 deaths so far, moreover, no approved drugs are available to treat COVID-19. Several investigations have been carried out to identify potent drugs for COVID-19 based on drug repurposing, potential novel compounds from ligand libraries, natural products, short peptides, and RNAseq analysis. This review is focused on three different aspects; (i) targets for drug design (ii) computational methods to identify lead compounds and (iii) drugs for COVID-19. It also covers the latest literature on various hit molecules proposed by computational methods and experimental techniques.

Identification of Potential Inhibitors for Targets Involved in Dengue Fever.

Lethality due to dengue infection is a global threat. Nearly 400 million people are affected every year, which approximately costs 500 million dollars for surveillance and vector control itself. Many investigations on the structure-function relationship of proteins expressed by the dengue virus are being made for more than a decade and had come up with many reports on small molecule drug discovery. In this review, we present a detailed note on viral proteins and their functions as well as the inhibitors discovered/designed so far using experimental and computational methods. Further, the phytoconstituents from medicinal plants, specifically the extract of the papaya leaves, neem and bael, which combat dengue infection via dengue protease, helicase, methyl transferase and polymerase are summarized.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery.

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

Combination of site directed mutagenesis and secondary structure analysis predicts the amino acids essential for stability of M. leprae MurE.

The life-threatening infections caused by Mycobacterium leprae (Mle) remain a major challenge in developing countries as well as globe and there is a need to design potent anti-leprosy drugs. In our previous studies, ATP-dependent Mle-MurE ligase involved in biosynthesis of peptidoglycan was identified as one of the common drug targets, homology modeled and reported. In this work in silico site directed mutagenesis study was carried out on the homology modeled Mle-MurE ligase. This predicted the amino acids essential for stability. In addition, the distribution of these residues in different secondary structures and in active sites was analyzed. Finally, the role of the conserved residues in stability and function was analyzed. The availability of Mle-MurE ligase built model together with insights gained from stability studies and docking studies will promote the rational design of potent and selective Mle-MurE ligase inhibitors as anti-leprosy therapeutics.

Homology modeling and docking analyses of M. leprae Mur ligases reveals the common binding residues for structure based drug designing to eradicate leprosy.

Multi drug resistance capacity for Mycobacterium leprae (MDR-Mle) demands the profound need for developing new anti-leprosy drugs. Since most of the drugs target a single enzyme, mutation in the active site renders the antibiotic ineffective. However, structural and mechanistic information on essential bacterial enzymes in a pathway could lead to the development of antibiotics that targets multiple enzymes. Peptidoglycan is an important component of the cell wall of M. leprae. The biosynthesis of bacterial peptidoglycan represents important targets for the development of new antibacterial drugs. Biosynthesis of peptidoglycan is a multi-step process that involves four key Mur ligase enzymes: MurC (EC:6.3.2.8), MurD (EC:6.3.2.9), MurE (EC:6.3.2.13) and MurF (EC:6.3.2.10). Hence in our work, we modeled the three-dimensional structure of the above Mur ligases using homology modeling method and analyzed its common binding features. The residues playing an important role in the catalytic activity of each of the Mur enzymes were predicted by docking these Mur ligases with their substrates and ATP. The conserved sequence motifs significant for ATP binding were predicted as the probable residues for structure based drug designing. Overall, the study was successful in listing significant and common binding residues of Mur enzymes in peptidoglycan pathway for multi targeted therapy.

Comparative modeling of UDP-N-acetylmuramoyl-glycyl-D-glutamate-2, 6-diaminopimelate ligase from Mycobacterium leprae and analysis of its binding features through molecular docking studies.

Leprosy is an infectious disease caused by Mycobacterium leprae. The increasing drug and multi-drug resistance of M. leprae enforce the importance of finding new drug targets. Mycobacterium has unusually impermeable cell wall that contributes to considerable resistance to many drugs. Peptidoglycan is an important component of the cell wall of M. leprae. UDP-N-acetylmuramoyl-glycyl-D-glutamate-2, 6-diaminopimelate ligase (MurE) plays a crucial role in the peptidoglycan biosynthesis and hence it could be considered as a potential drug target for leprosy. Structure of this enzyme for M. leprae has not yet been elucidated. We modeled the three-dimensional structure of MurE from M. leprae using comparative modeling methods based on the X-ray crystal structure of MurE from E. coli and validated. The 3D-structure of M. leprae MurE enzyme was docked with its substrates meso-diaminopimelic acid (A2pm) and UDP-N-acetyl muramoyl-glycyl-D- glutamate (UMGG) and its product UDP-N-acetyl muramoyl-glycyl-D-glu-meso-A(2)pm (UTP) and also with ATP. The docked complexes reveal the amino acids responsible for binding the substrates. Superposition of these complex structures suggests that carboxylic acid group of UMGG is positioned in proximity to γ-phosphate of the ATP to facilitate the formation of acylphosphate intermediate. The orientation of an amino group of A(2)pm facilitates the nucleophilic attack to form the product. Overall, the proposed model together with its binding features gained from docking studies could help to design a truly selective ligand inhibitor specific to MurE for the treatment of leprosy.

Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification.

Leprosy is an infectious disease caused by Mycobacterium leprae. M. leprae has undergone a major reductive evolution leaving a minimal set of functional genes for survival. It remains non-cultivable. As M. leprae develops resistance against most of the drugs, novel drug targets are required in order to design new drugs. As most of the essential genes mediate several biosynthetic and metabolic pathways, the pathway predictions can predict essential genes. We used comparative genome analysis of metabolic enzymes in M. leprae and H. sapiens using KEGG pathway database and identified 179 non-homologues enzymes. On further comparison of these 179 non-homologous enzymes to the list of minimal set of 48 essential genes required for cell-wall biosynthesis of M. leprae reveals eight common enzymes. Interestingly, six of these eight common enzymes map to that of peptidoglycan biosynthesis and they all belong to Mur enzymes. The machinery for peptidoglycan biosynthesis is a rich source of crucial targets for antibacterial chemotherapy and thus targeting these enzymes is a step towards facilitating the search for new antibiotics.