Anti-tubercular drug development: computational strategies to identify potential compounds

Rajkhowa, Sanchaita; Jha, Anupam Nath; Deka, Ramesh Chandra

doi:10.1016/j.jmgm.2015.09.007

Cited by 23 publications

(16 citation statements)

References 61 publications

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“…The chain was shown to engage the substrate binding loop, matching the arrangement seen by Rozwarski et al with their C16 substrate mimic [28,29]. In the quest for new inhibitors, other research groups have since explored different modifications of the core TCS structure [23,25,26,28,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Rodriguez et al explored combining two TCS molecules and synthesised a novel macrocyclic InhA inhibitor [48].…”

Section: Sar and Design Of Compoundsmentioning

confidence: 87%

“…TCS has been shown to directly inhibit InhA at low concentrations, with higher concentrations resulting in disruption of bacterial protein synthesis [7,21,22]. The chlorine atoms on the TCS structure, however, make it highly lipophilic thereby lowering its oral bioavailability and thus limiting its potential as an anti-tuberculosis drug [22][23][24]. TCS also has a lower potency in comparison to INH thereby requiring significantly higher concentrations to elicit its effect [22].…”

Section: Introductionmentioning

confidence: 99%

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Endophytic Bacillus spp. from medicinal plants inhibit mycelial growth of Sclerotinia sclerotiorum and promote plant growth

Ansary

Prince

Haque

et al. 2018

Zeitschrift Für Naturforschung C

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Plant growth-promoting bacteria that are also capable of suppressing plant pathogenic fungi play an important role in sustainable agriculture. There is a critical need for conducting research to discover, characterize and evaluate the efficacy of new strains of such bacteria in controlling highly aggressive plant pathogens. In this study, we isolated endophytic bacteria from medicinal plants of Bangladesh and evaluated their antagonistic capacity against an important phytopathogenic fungus Sclerotinia sclerotiorum. Growth-promoting effects of those isolates on cucumber and rice seedlings were also assessed. Among 16 morphologically distinct isolates, BDR-2, BRtL-2 and BCL-1 significantly inhibited the growth of S. sclerotiorum through induction of characteristic morphological alterations in hyphae and reduction of mycelial dry weight. When cucumber and rice seeds were treated with these endophytic bacteria, seven isolates (BCL-1, BDL-1, BRtL-2, BRtL-3, BDR-1, BDR-2 and BBoS-1) enhanced seed germination, seedling vigor, seedling growth and number of roots per plant at a varying level compared to untreated controls. All isolates produced high levels of indole-3-acetic acid (6 to 63 μg/mL) in vitro. Two most potential isolates, BDR-2 and BRtL-2, were identified as Bacillus amyloliquefaciens and B. subtilis, respectively, based on the 16S rRNA gene sequencing. These results suggest that endophytic Bacillus species from native medicinal plants have great potential for being used as natural plant growth promoter and biopesticides in sustainable crop production.

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Section: Sar and Design Of Compoundsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Endophytic Bacillus spp. from medicinal plants inhibit mycelial growth of Sclerotinia sclerotiorum and promote plant growth

Ansary

Prince

Haque

et al. 2018

Zeitschrift Für Naturforschung C

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“…The molecular docking can be seen applied regularly in academic labs and pharmaceutical companies to find effective solutions and thwart deadly diseases [72]. The identification of hit molecules in the preliminary stage of drug discovery is today heavily relied upon high throughput screening.…”

Section: Application Examples Of Molecular Dockingmentioning

confidence: 99%

Role of Force Fields in Protein Function Prediction

Hazarika¹,

Rajkhowa²,

Jha³

2021

Homology Molecular Modeling - Perspectives and Applications

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The world today, although, has developed an elaborate health system to fortify against known and unknown diseases, it continues to be challenged by new as well as emerging, and re-emerging infectious disease threats with severity and probable fluctuations. These threats also have varying costs for morbidity and mortality, as well as for a complex set of socioeconomic outcomes. Some of these diseases are often caused by pathogens which use humans as host. In such cases, it becomes paramount responsibility to dig out the source of pathogen survival to stop their population growth. Sequencing genomes has been finessed so much in the 21st century that complete genomes of any pathogen can be sequenced in a matter of days following which; different potential drug targets are needed to be identified. Structure modeling of the selected sequences is an initial step in structure-based drug design (SBDD). Dynamical study of predicted models provides a stable target structure. Results of these in-silico techniques greatly depend on force field (FF) parameters used. Thus, in this chapter, we intend to discuss the role of FF parameters used in protein structure prediction and molecular dynamics simulation to provide a brief overview on this area.

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“…Virtual screening has proven to be a very useful tool for the rapid discovery of molecules against this pathogen. Rajkhowa et al combined several computational techniques to screen 62 triclosan derivatives searching for compounds with better bioavailability.…”

Section: Qsar In Antibacterial Compound Developmentmentioning

confidence: 99%

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Suay-García

Bueso-Bordils

Falcó

et al. 2020

WIREs Comput Mol Sci

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With the pressing issue of antibiotic resistance, there is a constant need for new antibiotics. However, the fact that traditional methods of drug discovery are expensive and time-consuming has discouraged the pharmaceutical industry, leaving the burden of discovery to research institutions. This is where quantitative structure-activity relationship (QSAR) methods become a key tool in fighting multidrug-resistant bacteria, seeing as they provide useful information for the rational design of new active molecules at a minimal cost. A variety of linear and nonlinear statistical methods are used to develop these models based on the 2D or 3D representations of the molecules. QSAR models have proven to be effective in rapidly providing lead compound candidates against resistant bacteria such as methicillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas spp., Bacillus subtilis, or Mycobacterium tuberculosis. Moreover, QSAR methods allow for a deeper analysis of a library of molecules, selecting those with not only the optimal activity, but also the most favorable pharmacokinetic and toxicological profiles. The information obtained from QSAR studies makes optimizing an existing drug simpler, which is a cost-effective approach to obtain new treatments against increasingly resistant bacteria.

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Anti-tubercular drug development: computational strategies to identify potential compounds

Cited by 23 publications

References 61 publications

Endophytic Bacillus spp. from medicinal plants inhibit mycelial growth of Sclerotinia sclerotiorum and promote plant growth

Endophytic Bacillus spp. from medicinal plants inhibit mycelial growth of Sclerotinia sclerotiorum and promote plant growth

Role of Force Fields in Protein Function Prediction

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

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