In the past decade, computational predictions of protein structure and disorder have become widely accessible, achieving accuracy that, in some cases, rivals experimental results. These advances have been instrumental in identifying structural homologies, guiding protein design, and enhancing functional annotation. However, most prediction models are trained on “classical proteins,” favoring specific sequence lengths and patterns, leading to biases. Microproteins—small proteins with emerging roles in development and disease—function similarly across the tree of life and stand to benefit significantly from structure and disorder predictions. Yet, their short length and molecular interactions present unique challenges, making homology detection more difficult and requiring careful methodological considerations. Here, I outline workflows for predicting, analyzing, and refining microprotein structures and disorders, emphasizing key precautions to ensure reliable insights.