RT Journal
A1 Dai, Zheng
A1 Saksena, Sachit D.
A1 Horny, Geraldine
A1 Banholzer, Christine
A1 Ewert, Stefan
A1 Gifford, David K.
T1 Ultra-high-diversity factorizable libraries for efficient therapeutic discovery
JF Genome Research 
JO Genome Research 
YR 2022 
FD September 01 
VO 32 
IS 9 
SP 1787 
OP 1794 
DO 10.1101/gr.276593.122 
UL http://genome.cshlp.org/content/32/9/1787.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.