In the early stages of a CPS project, it is helpful to work through many different routes of synthesis and compare their difficulty, a practice we refer to as "bracketology". The process involves two challenges that can be greatly aided by data science and visualization tools: dividing the protein into segments that can be produced by SPPS in good quality (usually up to ~50 aa), and finding the most efficient order of joining segments together to minimize accumulating yield losses. Our Aligator (Automated Ligator) program assists in choosing ligation junctions by ranking possible ligation strategies (i.e., sets of segments) by factors such as segment length, predicted segment solubility, and the amino acids at ligation junctions (applying penalties for desulfurizations and poor-yielding thioesters). Judging the true difficulty of a strategy, however, requires full consideration of the path from every starting segment to the final product in addition to the properties of the segments themselves. Segments are joined sequentially two at a time into increasingly larger intermediates. With large proteins consisting of four or more segments, many ligation orders are possible, and choosing the optimal order can be a complex task of its own. Furthermore, it is vital to have backup plans in case an impassible obstacle, such as an insoluble intermediate segment, is encountered. Thus, we sought to provide tools within BracketMaker to help users assemble segments into efficient brackets and judge the feasibility of different plans.