Deploying biosensors to accelerate pharmaceutical biocatalyst development

Over two thirds of Americans take prescription medications, but manufacturing bottlenecks contribute to rising drug shortages, which restrict patient’s access to life-saving medications. Engineered enzymes can increase the resilience of pharmaceutical manufacturing, but the engineering process is bottlenecked by slow traditional methods for evaluating enzyme performance. This proposal focuses on the systematic development of chemical biosensors that accelerate the engineering of enzymes for robust pharmaceutical manufacturing. Specifically, we will use bacterial transcription factors (TF) as biosensors for chemical measurement, which can transduce enzyme-catalyzed product molecules into programmable gene expression outputs that enables enzyme screening >10,000-fold faster than traditional methods. Although identifying TFs with the specificity needed for enzyme screening is currently a slow ad hoc process, a parallel assay recently implemented at the National Institute of Standards and Technology (NIST) using automation equipment enables the simultaneous measurement of >50,000 TF biosensors that can power a data-driven methodology. This research effort focuses on developing bench-scale parallel reporter assays to facilitate the deployment of TFs for drug manufacturing innovation via three key activities: TF discovery, TF engineering, and TF application. These three pillars directly address two core issues limiting the utility of TFs for drug manufacturing. First, the time needed to develop a TF deployable for enzyme engineering is slow (addressed in Aims 1 & 2). Second, TFs have only been used in qualitative screens for enzyme activity (addressed in Aim 3). Aim 1 focuses on discovering drug-responsive TF scaffolds by developing parallel assays to measure native interactions between poly-specific multidrug regulators and pharmaceuticals. Aim 2 will focus on engineering highly specific TF biosensors by collectively assessing the ability of library design methods, computational methods (in collaboration with Dr. Debora Marks), and TF evolvability to yield highly specific TFs. Aim 3 focuses on developing an assay termed “deep enantioselectivity scanning”, which uses a TF-enabled parallel assay to simultaneously quantify the enantioselectivity of >10,000 enzyme variants. Results from this high-throughput assay will be cross-validated with liquid chromatography. All parallel assays will be developed at Harvard in collaboration with NIST, and under the mentorship of Drs. Pamela Silver and Michael Springer. This work aims to bolster pharmaceutical manufacturing innovation by establishing bench-scale parallel assays to rapidly deploy TF-based biosensors for accelerated enzyme engineering. Beyond supporting NIGMS’s mission to generate novel research tools that benefit public health, completing this research alongside my training plans in writing and communication will greatly support my long-term career goal of leading a research group that leverages quantitative and evolutionary biology to engineer sustainable biotechnologies. Project Number: 1K99GM162998-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Simon d'Oelsnitz | Institution: HARVARD MEDICAL SCHOOL, BOSTON, MA | Award Amount: $125,000 | Activity Code: K99 | Study Section: Special Emphasis Panel[ZRG1 CDB-Z (80)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11282928