@article{10.11648/j.cbb.20261401.11,
  author = {Uma Kumari and Rechel Tirkey and Vipasha Rathi},
  title = {Engineering Probiotic Strains for Gut Health Enhancement Using CRISPR and Molecular Marker-assisted Technologies},
  journal = {Computational Biology and Bioinformatics},
  volume = {14},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.cbb.20261401.11},
  url = {https://doi.org/10.11648/j.cbb.20261401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20261401.11},
  abstract = {Probiotics play a vital role in maintaining gut homeostasis, modulating immune responses, and promoting overall human health. Traditional approaches to probiotic strain development rely primarily on natural isolation and phenotypic screening, which are time-consuming and lack precision. The current study presents an in silico bioinformatics framework for the rational enhancement of probiotic strains through CRISPR-Cas9–guided design, integrated with structural bioinformatics and immunoinformatics analyses. Sequence homology and conservation were evaluated using BLAST and multiple sequence alignment to identify suitable genetic targets while minimizing off-target similarity. Structural insights were obtained from the PDB and MMDB, with PDB ID 2Z7X which was a representative immune-related protein model. Structural stability and conformational variation of hypothetical modifications were assessed using RMSD-based comparisons. Guide RNA candidates for genome editing were computationally nominated and ranked using E-CRISP and CHOPCHOP, emphasizing predicted efficiency and reduced off-target risk. To evaluate immunological safety, reverse vaccinology–based B-cell epitope prediction was performed using BepiPred, with epitope regions mapped onto three-dimensional protein structures. The integrated pipeline enables the identification of modification-tolerant regions while minimizing immunogenic potential. This purely computational strategy reduces experimental dependency, accelerates strain optimization, and provides a reproducible foundation for future probiotic engineering studies under appropriate biosafety and regulatory frameworks. This study provides a computational foundation for designing safer and more effective probiotic strains for gut health, immunomodulation, and disease prevention. The framework can support functional food development, precision microbiome therapies, vaccine-adjuvant research, and regulatory pre-screening of engineered probiotics while minimizing laboratory costs and biosafety risks.},
 year = {2026}
}

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TY  - JOUR
T1  - Engineering Probiotic Strains for Gut Health Enhancement Using CRISPR and Molecular Marker-assisted Technologies
AU  - Uma Kumari
AU  - Rechel Tirkey
AU  - Vipasha Rathi
Y1  - 2026/01/19
PY  - 2026
N1  - https://doi.org/10.11648/j.cbb.20261401.11
DO  - 10.11648/j.cbb.20261401.11
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20261401.11
AB  - Probiotics play a vital role in maintaining gut homeostasis, modulating immune responses, and promoting overall human health. Traditional approaches to probiotic strain development rely primarily on natural isolation and phenotypic screening, which are time-consuming and lack precision. The current study presents an in silico bioinformatics framework for the rational enhancement of probiotic strains through CRISPR-Cas9–guided design, integrated with structural bioinformatics and immunoinformatics analyses. Sequence homology and conservation were evaluated using BLAST and multiple sequence alignment to identify suitable genetic targets while minimizing off-target similarity. Structural insights were obtained from the PDB and MMDB, with PDB ID 2Z7X which was a representative immune-related protein model. Structural stability and conformational variation of hypothetical modifications were assessed using RMSD-based comparisons. Guide RNA candidates for genome editing were computationally nominated and ranked using E-CRISP and CHOPCHOP, emphasizing predicted efficiency and reduced off-target risk. To evaluate immunological safety, reverse vaccinology–based B-cell epitope prediction was performed using BepiPred, with epitope regions mapped onto three-dimensional protein structures. The integrated pipeline enables the identification of modification-tolerant regions while minimizing immunogenic potential. This purely computational strategy reduces experimental dependency, accelerates strain optimization, and provides a reproducible foundation for future probiotic engineering studies under appropriate biosafety and regulatory frameworks. This study provides a computational foundation for designing safer and more effective probiotic strains for gut health, immunomodulation, and disease prevention. The framework can support functional food development, precision microbiome therapies, vaccine-adjuvant research, and regulatory pre-screening of engineered probiotics while minimizing laboratory costs and biosafety risks.
VL  - 14
IS  - 1
ER  - 

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