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Computational Analysis of Non-synonymous SNPs in ATM Kinase: Structural Insights, Functional Implications, and Inhibitor Discovery

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Abstract

Ataxia telangiectasia-mutated (ATM) protein kinase, a key player in cellular integrity regulation, is known for its role in DNA damage response. This study investigates the broader impact of ATM on cellular processes and potential clinical manifestations arising from mutations, aiming to expand our understanding of ATM’s diverse functions beyond conventional roles. The research employs a comprehensive set of computational techniques for a thorough analysis of ATM mutations. The mutation data are curated from dbSNP and HuVarBase databases. A meticulous assessment is conducted, considering factors such as deleterious effects, protein stability, oncogenic potential, and biophysical characteristics of the identified mutations. Conservation analysis, utilizing diverse computational tools, provides insights into the evolutionary significance of these mutations. Molecular docking and dynamic simulation analyses are carried out for selected mutations, investigating their interactions with Y2080D, AZD0156, and quercetin inhibitors to gauge potential therapeutic implications. Among the 419 mutations scrutinized, five (V1913C, Y2080D, L2656P, C2770G, and C2930G) are identified as both disease causing and protein destabilizing. The study reveals the oncogenic potential of these mutations, supported by findings from the COSMIC database. Notably, Y2080D is associated with haematopoietic and lymphoid cancers, while C2770G shows a correlation with squamous cell carcinomas. Molecular docking and dynamic simulation analyses highlight strong binding affinities of quercetin for Y2080D and AZD0156 for C2770G, suggesting potential therapeutic options. In summary, this computational analysis provides a comprehensive understanding of ATM mutations, revealing their potential implications in cellular integrity and cancer development. The study underscores the significance of Y2080D and C2770G mutations, offering valuable insights for future precision medicine targeting-specific ATM. Despite informative computational analyses, a significant research gap exists, necessitating essential in vitro and in vivo studies to validate the predicted effects of ATM mutations on protein structure and function.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ATR:

ATM- and Rad3-related

ATM:

Ataxia telangiectasia mutated

BRCA1:

Breast cancer type 1 susceptibility protein

c-Abl:

Abelson tyrosine-protein kinase 1

Chk1:

Checkpoint kinase 1

Chk2:

Checkpoint kinase 2

ConSurf:

Conservation of amino acid residues in proteins

DNA:

Deoxyribonucleic acid

DNA-PKc:

DNA-dependent protein kinase catalytic subunit

DSB:

Double-stranded break

FANCD2:

Fanconi anemia group D2 protein

FATHMM-cancer:

Functional analysis through hidden Markov models

IR:

Ionizing radiation

MetaSNP:

Metastability-based SNP predictor

Mdm2:

Murine double minute 2

MDS:

Molecular dynamics simulation

NbsS1:

Nijmegen breakage syndrome 1

p53:

Tumour protein 53

PIKK:

Phosphatidylinositol 3-kinase-related kinase

Provean:

Protein variation effect analyzer

Pmut:

Pathogenicity prediction software for missense variants

Rad51:

DNA repair protein RAD51 homolog 1

SDM2:

Site-directed mutator 2

SNPs:

Single-nucleotide polymorphisms

mCSM:

Mutations of computational saturation mutagenesis

PyMOL:

Python molecular graphics system

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Acknowledgements

The authors are thankful to the Vellore Institute of Technology, Vellore, India for providing the necessary facilities to carry out this work.

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Authors and Affiliations

  1. Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632 014, India

    Nagesh Kishan Panchal & Sabina Evan Prince

  2. Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India

    Poorva Samdani & Tiasa Sengupta

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Contributions

Nagesh Kishan Panchal: idea for the article, literature survey, formal analysis, investigation, manuscript writing. Poorva Samdani: formal analysis, manuscript writing. Tiasa Sengupta: formal analysis, manuscript writing. Sabina Evan Prince: project administration, idea for the article, formal analysis, investigation, manuscript writing.

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Correspondence to Sabina Evan Prince.

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Panchal, N.K., Samdani, P., Sengupta, T. et al. Computational Analysis of Non-synonymous SNPs in ATM Kinase: Structural Insights, Functional Implications, and Inhibitor Discovery. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-024-01120-x

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