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Computational Approaches for the Structure-Based Identification of Novel Inhibitors Targeting Nucleoid-Associated Proteins in Mycobacterium Tuberculosis

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Abstract

Implementation of computational tools in the identification of novel drug targets for Tuberculosis (TB) has been a promising area of research. TB has been a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb) localized primarily on the lungs and it has been one of the most successful pathogen in the history of mankind. Extensively arising drug resistivity in TB has made it a global challenge and need for new drugs has become utmost important.The involvement of Nucleoid-Associated Proteins (NAPs) in maintaining the structure of the genomic material and regulating various cellular processes like transcription, DNA replication, repair and recombination makes significant, has opened a new arena to find the drugs targeting Mtb. The current study aims to identify potential inhibitors of NAPs through a computational approach. In the present work we worked on the eight NAPs of Mtb, namely, Lsr2, EspR, HupB, HNS, NapA, mIHF and NapM. The structural modelling and analysis of these NAPs were carried out. Moreover, molecular interaction were checked and binding energy was identified for 2500 FDA-approved drugs that were selected for antagonist analysis to choose novel inhibitors targeting NAPs of Mtb. Drugs including Amikacin, streptomycin, kanamycin, and isoniazid along with eight FDA-approved molecules that were found to be potential novel targets for these mycobacterial NAPs and have an impact on their functions. The potentiality of several anti-tubercular drugs as therapeutic agents identified through computational modelling and simulation unlocks a new gateway for accomplishing the goal to treat TB.

Graphical Abstract

Complete framework of the methodology employed in this study to predict inhibitors against mycobacterial NAPs.

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Data Availability

All data generated or analysed during this study are included in this published article. The Data used in the study was provided by DrugBank (on request).

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Acknowledgements

PS acknowledges, the Lab Infrastructure grant by BHU, Varanasi (F(C)/XVIII-Spl.Fund/Misc/Infrastructure/Instt.Sc/2019–2020/10290) and BTiS-NET-Sub-Distributed Information Centre, funded by DBT, Govt. of India at the School of Biotechnology, Banaras Hindu University, Varanasi, India. Authors acknowledge DrugBank for providing the FDA approved molecules used in this study. PS also acknowledges ‘Faculty Incentive Grant’ by Institute of Eminence (IoE) Scheme by BHU, Varanasi (Letter No R/ Dev/D/IoE/Seed & Incentive/2022-23/50024).

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Author notes

  1. Nirjara Singhvi

    Present address: School of Allied Sciences, Dev Bhoomi Uttarakhand University, Dehradun, Uttarakhand, 248001, India

  2. Pratyoosh Shukla

    Present address: Enzyme Technology and Protein Bioinformatics Laboratory, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, 221005, India

  3. Sunita and Nirjara Singhvi are joint first authors.

Authors and Affiliations

  1. Department of Microbiology, Maharshi Dayanand University, Rohtak, 124001, Haryana, India

    Sunita & Pratyoosh Shukla

  2. Bacterial Pathogenesis Laboratory, Department of Zoology, University of Delhi, Delhi, 110007, India

    Sunita & Yogendra Singh

  3. Department of Zoology, Hansraj College, University of Delhi, Delhi, 110007, India

    Nirjara Singhvi

  4. Ministry of Environment, Forest and Climate Change, Government of India, Dehradun, Uttarakhand, 248001, India

    Vipin Gupta

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Contributions

S: methodology, writing- original draft preparation. NS: software validation, Data curation, visualization, writing- original draft preparation. VG: Software validation, Data curation, Visualization. YS: writing- reviewing and editing and supervision. PS: conceptualization, writing- reviewing and editing and supervision.

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Correspondence to Pratyoosh Shukla.

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Sunita, Singhvi, N., Gupta, V. et al. Computational Approaches for the Structure-Based Identification of Novel Inhibitors Targeting Nucleoid-Associated Proteins in Mycobacterium Tuberculosis. Mol Biotechnol 66, 814–823 (2024). https://doi.org/10.1007/s12033-023-00710-5

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