PrimerDesigner: Designing Efficient Primers for Synthesizing Large Protein Libraries Without Cross-Hybridization
Efficient synthesis of large protein libraries is key to making high-throughput biochemistry scalable. Advances in DNA microarray technology now allow more than 170,000 designed oligonucleotides to be synthesized in a single array, dramatically reducing cost and enabling pooled amplification for downstream mutagenesis reactions. Effective amplification of short oligonucleotides into long coding sequences requires efficient primer design (PD) without cross-hybridization. However, no PD method currently guarantees complete coverage of the protein-coding sequences with maximum efficiency and no cross-hybridization over multiple proteins or multiple variants of the same protein. Here, we present PrimerDesigner, a suite of methods to design the most efficient primers for synthesizing large protein libraries with complete protein-coding sequence coverage and without cross-hybridization. We first prove that PD with complete coverage and without cross-hybridization is NP-hard, even for a single protein. As a solution, we formulate the PD problem using integer linear programming (ILP). When no cross-hybridization is possible, we solve the PD problem for a single protein in linear time. Moreover, we extend our ILP formulation to handle multiple variants of the same protein. We demonstrate that PrimerDesigner produces optimal solutions in only a few minutes for a single protein and within a couple of hours for multiple proteins while achieving higher efficiency than a greedy baseline approach. We expect PrimerDesigner to enable the synthesis of large protein libraries at an unprecedented scale and efficiency.
Authors
- Jonathan Mandl (ORCID: https://orcid.org/0009-0007-9257-2450)
- Yaron Orenstein (ORCID: https://orcid.org/0000-0002-3583-3112)
- Polly Fordyce
- Scott Longwell
- Marcus Bluestone
Institutions
- Bar-Ilan University (IL)
- Chan Zuckerberg Biohub San Francisco (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Massachusetts Institute of Technology (US)
- Stanford University (US)
Publication Details
- Journal
- Journal of Computational Biology
- Published
- 2026-10-07
- DOI
- https://doi.org/10.1177/15578666261491603
- Primary Topic
- Genomics and Phylogenetic Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00