Ultra-high-diversity factorizable libraries for efficient therapeutic discovery
- Zheng Dai1,3,
- Sachit D. Saksena1,3,
- Geraldine Horny2,
- Christine Banholzer2,
- Stefan Ewert2 and
- David K. Gifford1
- 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
- 2Novartis Institutes for BioMedical Research (NIBR), CH-4056 Basel, Switzerland
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↵3 These authors contributed equally to this work.
Abstract
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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.276593.122.
- Received January 16, 2022.
- Accepted June 22, 2022.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
