Methods like AlphaFold2, AlphaMissense, and AlphaFold3 are advancing our ability to link protein sequences to functions by providing accurate 3D models of protein structures, surpassing limitations of wet-lab methods. Two questions remain: How accurate are these methods for specific proteins, and what is the optimal workflow, especially considering the importance of structural dynamics, which current AlphaFold methods do not capture? This study focuses on Fibroblast Growth Factor Receptor 2 (FGFR2), a protein critical in cellular processes and associated with various congenital disorders and cancers. We evaluate different workflows for predicting the structural and functional impacts of FGFR2 mutations, finding that a sequence involving AlphaFold2 (structure prediction), followed by SCWRL4 (side-chain optimization) and NAMD2 (structure dynamics), achieves high accuracy and offers insights into both dynamic structural and functional changes. This approach advances our understanding of FGFR2-related diseases and more broadly provides a reliable framework for predicting the structural and functional impact of single-nucleotide variants.