Aligator: A computational tool for optimizing total chemical synthesis of large proteins

Abstract

The scope of chemical protein synthesis (CPS) continues to expand, driven primarily by advances in chemical ligation tools (e.g., reversible solubilizing groups and novel ligation chemistries). However, the design of an optimal synthesis route can be an arduous and fickle task due to the large number of theoretically possible, and in many cases problematic, synthetic strategies. In this perspective, we highlight recent CPS tool advances and then introduce a new and easy-to-use program, Aligator (Automated Ligator), for analyzing and designing the most efficient strategies for constructing large targets using CPS. As a model set, we selected the E. coli ribosomal proteins and associated factors for computational analysis. Aligator systematically scores and ranks all feasible synthetic strategies for a particular CPS target. The Aligator script methodically evaluates potential peptide segments for a target using a scoring function that includes solubility, ligation site quality, segment lengths, and number of ligations to provide a ranked list of potential synthetic strategies. We demonstrate the utility of Aligator by analyzing three recent CPS projects from our lab: TNFα (157 aa), GroES (97 aa), and DapA (312 aa). As the limits of CPS are extended, we expect that computational tools will play an increasingly important role in the efficient execution of ambitious CPS projects such as production of a mirror-image ribosome.