Investigating the Effects of Glioma MutationsUsing Genome-Scale Metabolic Models and StructuralBioinformatics

Introduction: Cancer is a complex and challenging disease,and early diagnosis and effective treatment remain elusive. Onepromising approach is personalized medicine with genomescalemetabolic models, which can predict metabolic flux basedon patient-specific data. Structural bioinformatics has also enabledthe investigation of the effects of mutations and noveldrugs on cancer. In this study, we aim to explore the effects ofglioma mutations using this approach.Materials and Methods:We extracted glioma mutation datafrom the cancer genome atlas database and performed aminoacid sequence alterations to predict three-dimensional structuresusing ColabFold. We utilized Autodock Vina to calculatethe affinity between proteins and ligands based on genomescalemetabolic models for both mutated and wild types. Wecompared the differences between the affinities to determinethe impact of mutations.Results: Our docking results showed significant differencesbetween mutated and wild-type proteins in their affinity forligands. Some mutant proteins in different reactions exhibitedvarying affinities for their ligands, making it challenging to determinethe harmfulness of mutations. Overall, our study highlightsthe potential of structural bioinformatics to investigatethe effects of glioma cancer mutations.Conclusion: Our study highlights the potential of using structuralbioinformatics to investigate the effects of glioma cancermutations in genome-scale metabolic models. While our resultsare promising, further research is needed to determine the fullextent of these mutations' effects on cancer and to integratethem into the model as a new layer of information. This approachmay lead to the development of more personalized andeffective treatments for cancer.

کلیدواژه ها:

Glioma Mutations ، Genome-Scale Metabolic Models ، Structural Bioinformatics ، Docking