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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PrimerDesigner: Designing Efficient Primers for Synthesizing Large Protein Libraries Without Cross-Hybridization

Jonathan Mandl, Yaron Orenstein, Polly Fordyce, Scott Longwell et al.
Journal of Computational Biology
Genomics and Phylogenetic Studies
article

PrimerDesigner: Designing Efficient Primers for Synthesizing Large Protein Libraries Without Cross-Hybridization

Jonathan Mandl, Yaron Orenstein, Polly Fordyce, Scott Longwell, Marcus Bluestone
article en

Abstract

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.

Journal of Computational Biology
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)
Openalex Percentile: Top 22%
Genomics and Phylogenetic Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.