The interest in the self-assembly of peptide-based systems has grown significantly over the past 10–15 years, as more and more applications are shown to benefit from the useful properties of the amino acid based monomers. With the desire to apply the principals of self-assembly to systems within new application areas, there has been an increasing emphasis in understanding the governing forces involved in the self-assembly process, and using this understanding to predict the behaviour of, and design, new materials. To this end, computational approaches have played an increasingly important role over the past decade in helping to decode how small changes in the primary structure can lead to significantly different nanostructures with new function. In this review, a brief survey of the different computational approaches employed in this quest for understanding are provided, along with representative examples of the types of questions that can be answered with each of the different approaches.