CP is a nonprogressive, unchanging disorder of posture or movement caused by abnormalities in the brain, which appears during the rapid development of the brain, affecting one in five children worldwide. The type of movement disorder determines the clinical subclassification of the condition. According to the current clinical literature, there are around seven groups exhibiting a pleuthera of traits and prognosis. Moreover, the condition’s high prevalence of comorbidities with other neurological disorders presents difficulties for both diagnostics and care. Therefore, even though there are a significant number of disease-related genes reported by several clinical genetics studies, the gene-phenotype relationships are not fully elucidated, which further complicates the current landscape. Moreover, there are limited computational studies focusing on the mutation-structure-function paradigm regarding CP at the protein level. In line with these, utilizing AI-based structure and variant effect prediction methods to understand the CP-related protein landscape would provide a valuable step towards elucidating CP pathogenesis in more detail. In this study, a curated dataset of CP-related protein sequences is prepared. 3D structures were obtained from PDB and AlphaFold2. The structural properties of the dataset were determined by the ProtParam tool. STRING and GO enrichment analysis was performed to understand if any functional enrichment is present. Our comparative analysis revealed an inherent heterogeneity by means of protein function and structure with a better conservation of hydrophobic properties in the CP dataset.