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This chapter envisages the responses of cyanobacteria to different abiotic stresses with special reference to the cyanobacterium Scytonema tolypothrichoides VB-61278. We describe the in silico approach to compare Fe and Mn Super Oxide Dismutase (SOD) for deciphering their role in promoting resilience in Scytonema tolypothrichoides VB-61278. Various physicochemical parameters such as molecular weight, instability index, isoelectric point (pI), aliphatic index, and GRand Average of Hydropathy (GRAVY) were predicted and explained in an illustrative manner. The secondary structure of the protein was predicted followed by the prediction of three-dimensional (3D) structural models using Modeller and trailblazing artificial intelligence (AI)-powered AlphaFold2 tools have been illustrated. Exhaustive explanations of validation methods for the models using Prosa-web, Verify-3D, Ramachandran plot, and LDDT score have been highlighted. Studies emphasizing the conservation and prediction of metal binding positions, domains, and motifs, along with residues of structural and functional importance, as well as the generation of phylogenetic trees, have been well explained. This chapter serves as a state-of-the-art about methodologies, ensuing results, and inferences drawn from tools used in bioinformatics to decipher protein modeling and relevant biotechnological applications in a lucid and illustrative way.

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Proteomics and Bioinformatics Approaches for Exploring Resilience Strategies in Cyanobacteria

  • 章节

  • 发表日期：

    2025年

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  • Minu Kesheri,
  • Swarna Kanchan,
  • Donat-P. Häder,
  • Rajeshwar P. Sinha

摘要

This chapter envisages the responses of cyanobacteria to different abiotic stresses with special reference to the cyanobacterium Scytonema tolypothrichoides VB-61278. We describe the in silico approach to compare Fe and Mn Super Oxide Dismutase (SOD) for deciphering their role in promoting resilience in Scytonema tolypothrichoides VB-61278. Various physicochemical parameters such as molecular weight, instability index, isoelectric point (pI), aliphatic index, and GRand Average of Hydropathy (GRAVY) were predicted and explained in an illustrative manner. The secondary structure of the protein was predicted followed by the prediction of three-dimensional (3D) structural models using Modeller and trailblazing artificial intelligence (AI)-powered AlphaFold2 tools have been illustrated. Exhaustive explanations of validation methods for the models using Prosa-web, Verify-3D, Ramachandran plot, and LDDT score have been highlighted. Studies emphasizing the conservation and prediction of metal binding positions, domains, and motifs, along with residues of structural and functional importance, as well as the generation of phylogenetic trees, have been well explained. This chapter serves as a state-of-the-art about methodologies, ensuing results, and inferences drawn from tools used in bioinformatics to decipher protein modeling and relevant biotechnological applications in a lucid and illustrative way.