An in silico approach towards identification of novel drug targets in pathogenic species of Leptospira

Gupta, Reena; Verma, Rashi; Pradhan, Dibyabhaba; Jain, Arun K.; Umamaheswari, Amineni; S., C.

doi:10.1371/journal.pone.0221446

Cited by 14 publications

(5 citation statements)

References 56 publications

(65 reference statements)

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“…The paralog proteins were essentially eliminated from further analysis, and the non-paralogous proteins that had greater than 100 amino acid sequences were taken up. A protein having less than 100 amino acids is generally assumed to be non-essential, and therefore omitted [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches

et al. 2023

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Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium. If the plague is not properly treated it can cause rapid death of the host. Bubonic, pneumonic, and septicemic are the three types of plague described. Bubonic plague can progress to septicemic plague, if not diagnosed and treated on time. The mortality rate of pneumonic and septicemic plague is quite high. The symptom-defining disease is the bubo, which is a painful lymph node swelling. Almost 50% of bubonic plague leads to sepsis and death if not treated immediately with antibiotics. The host immune response is slow as compared to other bacterial infections. Clinical isolates of Yersinia pestis revealed resistance to many antibiotics such as tetracycline, spectinomycin, kanamycin, streptomycin, minocycline, chloramphenicol, and sulfonamides. Drug discovery is a time-consuming process. It always takes ten to fifteen years to bring a single drug to the market. In this regard, in silico subtractive proteomics is an accurate, rapid, and cost-effective approach for the discovery of drug targets. An ideal drug target must be essential to the pathogen’s survival and must be absent in the host. Machine learning approaches are more accurate as compared to traditional virtual screening. In this study, k-nearest neighbor (kNN) and support vector machine (SVM) were used to predict the active hits against the beta-ketoacyl-ACP synthase III drug target predicted by the subtractive genomics approach. Among the 1012 compounds of the South African Natural Products database, 11 hits were predicted as active. Further, the active hits were docked against the active site of beta-ketoacyl-ACP synthase III. Out of the total 11 active hits, the 3 lowest docking score hits that showed strong interaction with the drug target were shortlisted along with the standard drug and were simulated for 100 ns. The MD simulation revealed that all the shortlisted compounds display stable behavior and the compounds formed stable complexes with the drug target. These compounds may have the potential to inhibit the beta-ketoacyl-ACP synthase III drug target and can help to combat Yersinia pestis-related infections. The dataset and the source codes are freely available on GitHub.

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Section: Methodsmentioning

confidence: 99%

Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches

et al. 2023

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“…Combining flux balance analysis of the reactome and structural assessment of targetability, secY (Rv0732), katG (Rv1908c), gltB (Rv3859c), and sirA (Rv2391) were identified as potential therapeutic targets against Mtb H37RV [104]. Gupta et al [148] performed subtractive genomic and comparative genomics of 16 pathogenic Leptospira strains retrieved from NCBI against DEG and the Cluster of Essential Genes (CEG) [149] using the Cluster Database at High Identity…”

Section: Comprehensive Applicationsmentioning

confidence: 99%

“…NCBI, DEG, CEG, UniProt KAAS, BLASTp, Cytoscape Leptospira lpxB, lpxK, kdtA, fliN, cobA, metX, thiL, ubiA [148] with Tolerance (CD-Hit) and BLASTp to identify 34 common genes. After analyzing and comparing two extended PPI networks of two strains and multiple sequence alignment, eight proteins (lpxB, lpxK, kdtA, fliN, cobA, metX, thiL, and ubiA) were identified as putative therapeutic targets for drug design or vaccine development [148].…”

Section: Comprehensive Applicationsmentioning

confidence: 99%

In silico Methods for Identification of Potential Therapeutic Targets

Zhang

Yang

et al. 2021

Interdiscip Sci Comput Life Sci

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At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery. Graphical abstract

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“…Identification and evaluation of novel drug targets drive the advancement of new inhibitors against pathogenic microbial strains resistant to common antibiotics. Several in silico approaches were engaged to investigate genomic data to identify potential drug targets that are critical for the survival and virulence of bacterial pathogens and with no counterpart in humans or other animal proteins ( Gupta et al, 2019 ). Several approaches including a whole-genome transposon random approach to ascertain the critical gene set found in various branches of life and were applied to the genomes of Haemophilus influenza , Mycobacterium tuberculosis , Pseudomonas aeruginosa , E. coli and Mycoplasma genitalium ( Akerley et al, 2002 ; Gerdes et al, 2002 ; Lamichhane et al, 2003 ; Sassetti et al, 2003 ; Glass et al, 2006 ; Liberati et al, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Inhibitors of riboflavin biosynthetic pathway enzymes as potential antibacterial drugs

Islam

Kumar

2023

Front. Mol. Biosci.

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Multiple drug resistance is the main obstacle in the treatment of bacterial diseases. Resistance against antibiotics demands the exploration of new antimicrobial drug targets. A variety of in silico and genetic approaches show that the enzymes of the riboflavin biosynthetic pathway are crucial for the survival of bacteria. This pathway is absent in humans thus enzymes of the riboflavin biosynthetic pathway are emerging drug targets for resistant pathogenic bacterial strains. Exploring the structural details, their mechanism of action, intermediate elucidation, and interaction analysis would help in designing suitable inhibitors of these enzymes. The riboflavin biosynthetic pathway consists of seven distinct enzymes, namely, 3,4-dihydroxy-2-butanone 4-phosphate synthase, GTP cyclohydrolase II, pyrimidine deaminase/reductase, phosphatase, lumazine synthase, and riboflavin synthase. The present review summarizes the research work that has been carried out on these enzymes in terms of their structures, active site architectures, and molecular mechanism of catalysis. This review also walks through small molecule inhibitors that have been developed against several of these enzymes.

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An in silico approach towards identification of novel drug targets in pathogenic species of Leptospira

Cited by 14 publications

References 56 publications

Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches

Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches

In silico Methods for Identification of Potential Therapeutic Targets

Inhibitors of riboflavin biosynthetic pathway enzymes as potential antibacterial drugs

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