Ultra-high-diversity factorizable libraries for efficient therapeutic discovery

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

Automated summary

This study proposes a method of creating large libraries of candidate sequences that meet an objective function at low cost, called computationally designed factorizable libraries. By representing objective functions as an inner product of feature vectors, the authors introduce an optimization method called stochastically annealed product spaces (SAPS). Using this approach, they successfully design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.