RT Journal Article
SR Electronic
T1 Ultra high diversity factorizable libraries for efficient therapeutic discovery
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.01.17.476670
DO 10.1101/2022.01.17.476670
A1 Dai, Zheng
A1 Saksena, Sachit D.
A1 Horny, Geraldine
A1 Banholzer, Christine
A1 Ewert, Stefan
A1 Gifford, David K.
YR 2022
UL http://biorxiv.org/content/early/2022/01/18/2022.01.17.476670.abstract
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.