Besides helping us advance the understanding of the physicochemical principles governing the three-dimensional folding of proteins and their mechanisms of action, the ability to build, evaluate, and optimize reliable 3D protein models has provided valuable tools for the development of different applications in the fields of biotechnology, medicine, and synthetic biology. The development of automated algorithms has made many of the current methodologies for protein modelling and visualization available to researchers from all backgrounds, without the need to be familiarized with the inner workings of their statistical and biophysical principles. However, there is still a lack in some areas where the learning curves are too steep for the methods to be widely used by the average non-programmer molecular biologist, or the implementation of the methods lacks key features to improve the interpretability and impact of their results. Throughout this work, I will focus on two different applications in the field of structural biology where computational methods provide useful tools to aid in synthetic biology or medical research. The first application is the implementation of a pipeline to build models of protein complexes by joining structured domains with disordered linkers, in individual or multiple chains, and with the possibility of building symmetric structures. Its capabilities and performance for the generation of complex constructs are evaluated, and possible areas of improvement described. The second application, but not less important, involves the structural analysis of patient-derived protein mutants using protein modelling techniques and visualization tools, to elucidate the potential molecular basis for the patient’s phenotype. The methodology for these analyses is described, along with the results and observations from 22 such cases in 13 different proteins. Finally, the need for a dedicated pipeline for the structure-based prediction of the effect of different types of mutations on the stability and function of proteins, complementary to available sequence-based approaches, is highlighted.