Abstract
Noncanonical amino acids (NCAAs) occupy an important place, both in natural biology and in synthetic applications. However, modeling these amino acids still lies outside the capabilities of most deep learning methods due to sparse training data sets for this task. Instead, biophysical methods such as Rosetta can excel in modeling NCAAs. We discuss the various aspects of parametrizing an NCAA for use in Rosetta, identifying rotamer distribution modeling as one of the most impactful factors of NCAA parametrization on Rosetta performance. To this end, we also present FakeRotLib, a method that uses statistical fitting of small-molecule conformers to create rotamer distributions. We find that FakeRotLib outperforms existing methods in a fraction of the time and is able to parametrize NCAA types previously unmodeled by Rosetta.