  Score-based generative models (SGMs) have proven to be powerful tools for
designing new proteins. Designing proteins that bind a pre-specified target is
highly relevant to a range of medical and industrial applications. Despite the
flurry of new SGMs in the last year, there has been little systematic
exploration of the impact of design choices in SGMs for protein design. Here we
present LoopGen, a flexible SGM framework for the design of short binding
peptide structures. We apply our framework to design antibody binding loop
structures conditional on a target epitope and evaluate a variety of modelling
choices in SGM-based protein design. We demonstrate that modelling residue
orientations in addition to positions improves not only the quality of the
output structures but also their diversity. Additionally, we identify variance
schedules that result in significant performance improvements and observe
patterns that may motivate the development of better schedules for protein
design. Finally, we develop three novel tests to evaluate whether the model
generates structures that are appropriately conditioned on an epitope,
demonstrating that LoopGen's generated structures are dependent on the
structure, sequence, and position of the epitope. Our findings will help guide
future development and evaluation of generative models for binding proteins.

Biomolecules