A key aspect of the protocol with multiple initial models relates to the how alternative models are generated. Simple, template-based modeling based on alternative templates, e.g. via MODELLER is not likely to generate alternative models that are competitive initial models subjected to refinement that would likely come from more sophisticated protocols such as Rosetta 59 , I-TASSER , or most recently deeplearning based prediction pipelines. One approach may be to employ competing protocols to generate alternative initial models, e.g. based on different deep-learning protocols. However, this is difficult to implement in practice since not all protocols are fully public and using multiple protocols may incur significant computational cost and complexity. Here, we took a simpler approach of hybridizing between the given initial model and multiple additional template-based models via a modified version of the "Iterative hybridize" protocol of Rosetta. This resulted in sufficiently high-quality alternative models using a compact protocol that is relatively straightforward to apply as a stand-alone refinement procedure, even within an automatic server context. However, other approaches could be taken. For example, our multiplemodel refinement protocol could take advantage of multiple models generated initially by any given prediction method, such as the I-TASSER server that usually gives five models.