Protein molecular modeling of genetic markers for thyroid cancer

ABSTRACT Introduction: The advances in thyroid molecular biology studies provide not only insight into thyroid diseases but accurate diagnosis of thyroid cancer. Objective: Design a tutorial on protein molecular modeling of genetic markers for thyroid cancer. Methods: The proteins were selected using the Protein Data Bank sequence and the basic local alignment search tool (BLAST) algorithm. The obtained sequences were aligned with the Clustal W multiple alignment algorithms. For the molecular modeling, three-dimensional structures were generated from this set of constraints with the SWISS-MODEL, which is a fully automated protein structure homology-modeling server, accessible via the ExPASy web server. Results: We demonstrated protein analysis, projection of the molecular structure and protein homology of the following molecular markers of thyroid cancer: receptor tyrosine kinase (RET) proto-oncogene; neurotrophic tyrosine kinase receptor 1 (NTRK1) proto-oncogene; phosphatase and tensin homolog (PTEN); tumor protein p53 (TP53) gene; phosphoinositide 3-kinase/threonine protein kinase (PI3K/AKT); catenin beta 1 (CTNNB1); paired box 8-peroxisome proliferator-activated receptor gamma (PAX8-PPARG); rat sarcoma viral oncogene (RAS); B-raf proto-oncogene, serine/threonine kinase (BRAF); and thyroid-stimulating hormone receptor (TSHR). Conclusion: This study shows the importance of understanding the molecular structure of the markers for thyroid cancer through bioinformatics, and consequently, the development of more effective new molecules as alternative tools for thyroid cancer treatment