PT  - JOURNAL ARTICLE
AU  - Zheng Dai
AU  - Sachit D. Saksena
AU  - Geraldine Horny
AU  - Christine Banholzer
AU  - Stefan Ewert
AU  - David K. Gifford
TI  - Ultra high diversity factorizable libraries for efficient therapeutic discovery
AID  - 10.1101/2022.01.17.476670
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.01.17.476670
4099  - http://biorxiv.org/content/early/2022/01/18/2022.01.17.476670.short
4100  - http://biorxiv.org/content/early/2022/01/18/2022.01.17.476670.full
AB  - 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.Competing Interest StatementGeraldine Horny, Christine Banholzer, and Stefan Ewert are employees of Novartis. The remaining authors declare no competing interests.