In protein modelling and design, an understanding of the relationship between sequence and structure is essential. Using parallel, homotetrameric coiled-coil structures as a model system, we demonstrated that machine learning techniques can be used to predict structural parameters directly from the sequence. Coiled coils are regular protein structures, which are of great interest as building blocks for assembling larger nanostructures. They are composed of two or more alpha-helices wrapped around each other to form a supercoiled bundle. The coiled-coil bundles are defined by four basic structural parameters: topology (parallel or antiparallel), radius, degree of supercoiling, and the rotation of helices around their axes. In parallel coiled coils the latter parameter, describing the hydrophobic core packing geometry, was assumed to show little variation. However, we found that subtle differences between structures of this type were not artifacts of structure determination and could be predicted directly from the sequence. Using this information in modelling narrows the structural parameter space that must be searched and thus significantly reduces the required computational time. Moreover, the sequence-structure rules can be used to explain the effects of point mutations and to shed light on the relationship between hydrophobic core architecture and coiled-coil topology.