In silico scanning of structural and functional deleterious nsSNPs in Arabidopsis thaliana’s SOG1 protein, using molecular dynamic simulation approaches

Abstract

Suppressor of gamma response 1 (SOG1) is a member of the NAC domain family transcription factors of the DNA damage response (DDR) signaling in the plant’s genome. SOG1 is directly involved in transcriptional response to DNA damage, cell cycle checkpoints and ATR or ATM-mediated activation of the DNA damage responses and repair functioning in programmed cell death and regulation of end reduplication. Different mutations in the SOG1 protein lead to severe diseases and, ultimately, cell death. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that cause different diseases or programmed cell death. The current study applied different computational approaches to Arabidopsis thaliana L. SOG1 protein to identify the potential deleterious nsSNPs and monitor their impact on the structure, function and protein stability. Various bioinformatics tools were applied to analyze the retrieved 34 nsSNPs and interestingly extracted four deleterious nsSNPs, that is, ensvath13968004 (Q166L), tmp18998388 (P159L), ensvath01103049 (K199N) and tmp18998295 (Y190F). For example, homology modeling, conservation and conformational analysis of the mutant’s models were considered to scrutinize the deviations of these variants from the native SOG1 structure. All atoms molecular dynamic simulation confirmed the significance of these mutations on the protein stability, residual and structural conformation, compactness, surface conformation, dominant motion, Gibbs free energy distribution and dynamic effects. Similarly, protein–protein interaction revealed that SOG1 operates as a hub-linking cluster of various proteins, and any changes in the SOG1 might result in the disassociation of several signal transduction cascades.

Communicated by Ramaswamy H. Sarma

Keywords:

• Molecular dynamic simulation
• SOG1
• Arabidopsis thaliana
• single nucleotide polymorphism
• NAC domain

Acknowledgments

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331128DSR68). The authors would like to thank the University of Nizwa for the generous support of this project. We thank the technical staff for their assistance.

Author’s contribution

Asif Khan, Waheed Murad, Ajmal Khan and Syed Umair Ahmad designed the project and prepared the first draft of the manuscript. Muhammad Waqas, Muhammad Faheem and Asaad Khalid performed the simulation. Jalal Uddin and Ashraf N. Abdalla analyzed data. Muhammad Tufail, Ahmad Al-Harassi and Sobia Ahsan Halim revised the manuscript and approved for publication.

Disclosure Statement

The authors declare that they have no competing interests.

Availability of data and materials

All datasets on which the conclusions of the manuscript rely are presented in the paper.