Week 1 HW: Final Project Ideas
HTBAA Homework #1
(1) What I would like to biomanufacture and why
My long-term vision is an allosteric “Bioswitch” version of an immune checkpoint inhibitor: pembrolizumab (Keytruda) that can be turned off on demand using light, to address chronic immune-related adverse effects associated with checkpoint blockade therapy. As Wolf, Mathony, and Niopek (Heidelberg University) describe in their 2026 Clinical and Translational Medicine invited letter, “Bioswitches: Towards programmable, on-demand control of therapeutic proteins,” most protein therapeutics act continuously for as long as they remain in the body, and “adverse effects such as off-target activity and toxicity may arise in consequence of prolonged drug activity and are difficult to mitigate once a patient has received the therapeutic” [1]. A light-switchable checkpoint inhibitor could shut off the inhibitor if severe adverse effects are observed without waiting for the drug to clear naturally.
Cancer is a large global health burden: GLOBOCAN 2024 (IARC / American Cancer Society) estimates approximately 21 million new cancer cases and roughly 10 million deaths worldwide in 2024 [2]. Checkpoint inhibitors are among the most effective immunotherapies available, but their continuous immune activation can produce adverse effects.
This is a difficult, several year goal and I am not proposing to build the full therapeutic switch this semester. Instead, my project is to replicate and quantitatively extend a validated building block of this technology. A light-controlled protein switch in E. coli as a step toward eventually being able to design and calibrate controllable protein therapeutics.
(2) What living system would I use?
I would use E. coli as the chassis, replicating the CAT-LOV2 system described by Wolf et al. (Nature Methods, 2025) in their ProDomino paper [3]. CAT (chloramphenicol acetyltransferase) is an enzyme that inactivates the antibiotic chloramphenicol, conferring antibiotic resistance. In this construct, the AsLOV2 photoreceptor domain is inserted into a permissive surface loop of CAT — a site identified computationally by ProDomino, a published machine-learning tool for predicting domain-insertion sites — creating a fusion protein whose chloramphenicol-resistance activity is light-dependent.
E. coli transformed with a plasmid encoding the CAT-LOV2 fusion gene, grown in standard LB medium with chloramphenicol selection.
AsLOV2 adopts a conformation that allows CAT to fold and function, conferring resistance; under blue light, AsLOV2 undergoes a conformational change that disrupts CAT’s activity, acting as a light-triggered “off switch.” The original paper reports roughly a 20-fold difference in bacterial growth (by optical density) between dark and light conditions [3].
Results: quantitative, light-dose-dependent growth/survival signal (OD600 and colony counts) I can use to characterize the switch’s dynamic range and light sensitivity.
Assumption: the CAT-LOV2 construct (or one reconstructed from the published insertion site and linker sequences) is available or reproducible for this project.
(3) How would I use automation?
I would automate two parts of the workflow:
Design: ProDomino is open-source and publicly available. I would use it computationally, before any wet-lab work, to confirm the previously identified CAT insertion site and, time permitting, screen additional candidate sites not tested in the original paper [3].
Execution — the core of this semester’s contribution: I would use a desktop liquid-handling robot (e.g., an Opentrons) together with a programmable LED light array to run a parallel light-intensity dose-response experiment. The robot would dispense identical E. coli/CAT-LOV2 cultures across a 96-well plate; the LED array would expose different wells to a defined gradient of blue-light intensities, from darkness to a saturating high-intensity control; a plate reader would then automate OD600 measurement across all conditions simultaneously. This automates the parts of the workflow — precise, repeatable liquid handling and precise, repeatable light-dose delivery across many parallel conditions — that would be slow and error-prone to reproduce by hand.
(4) How much would I need to make, and how long would it take?
(a) How much
For this semester’s experiment, the relevant scale is the number of conditions needed for a statistically defensible dose-response curve. I would test 8–10 light intensities in triplicate, giving 24–30 cultures comfortably within one 96-well plate alongside blank and control wells.
For context on the longer-term therapeutic motivation (not this semester’s actual target): pembrolizumab is dosed at approximately 200 mg per session; assuming roughly 13 sessions per patient [4], that is about 2.6 g per patient. Scaled to a 500-person cohort study, that is approximately 1.3 kg of drug. Before that is animal studies to characterize the pharmacokinetics and safety of it, probably half — ~650g.
(b) How long
For the CAT-LOV2 dose-response experiment: E. coli grown from a dilute starting culture typically reaches saturating density in approximately 12–16 hours under standard conditions, so a full cycle — culture setup, overnight growth under the light-intensity gradient, next-day OD600/plate readout — is realistically a 1–2 day turnaround. With automation, this could be repeated across additional parameters (e.g., a linker-length variant) within the same week. To get it right I could see taking several weeks.
For the longer-term technology-development timeline: the original ProDomino model required approximately 6 months of training and validation to produce the results in the Nature Methods paper [3]. Extending and validating this approach on more complex target proteins would likely take 1–2 years of iterative work, and developing a validated switch for a specific oncology target (e.g., a checkpoint inhibitor) is more realistically a 5–6 year, PhD-scale research program. This semester’s project is a small, bounded first step within that timeline, not a replication of the full pipeline.
(5) What don’t I know yet?
Whether ProDomino’s predicted insertion site generalizes as reported, and how CAT-LOV2 behaves under a continuous light-dose gradient rather than the binary light/dark conditions tested in the original paper. I don’t yet know the exact AsLOV2 activation/saturation intensity thresholds for this specific construct (needs to be pulled from the ProDomino paper’s supplementary methods/data before finalizing the experimental light-intensity range — I have deliberately not guessed at precise mW/cm² values without that source).
Whether inserting a photoreceptor domain into a full-length antibody (the long-term pembrolizumab goal) preserves antigen binding and produces a controllable signal at all. This has not been demonstrated in any source I reviewed — every validated domain-insertion switch to date has been built in a smaller, structurally simpler effector (an enzyme, or Cas9/Cas12a), not a full immunoglobulin. This is the largest unresolved technical leap between this semester’s project and the long-term vision.
How light would actually be delivered to a systemically circulating drug in a living patient, if the long-term vision were pursued further. My E. coli dose-response system is entirely external-light-accessible in culture; an in vivo application would need to solve tissue-penetration and light-delivery problems this semester’s work does not address.
AI assistance disclosure: this proposal was developed through an extended conversation with Claude (Anthropic). I used it to check my understanding of the assigned readings and cited papers, stress-test and eliminate several earlier candidate ideas (an extremophile chassis platform, a non-GMO/allergen-free food enzyme, a non-thermal food-enzyme deactivation switch, a therapeutic peptide, and initially a therapeutic switch applied directly to pembrolizumab), correct factual errors in my draft (the institutional affiliation of the Bioswitches letter’s authors, and a conceptual error conflating CRISPR gene-editing with the standard molecular cloning actually used to build domain-insertion switches), and verify current global cancer statistics via web search. Target selection, scoping decisions, and final wording are my own.
Citations
[1] Wolf B, Mathony J, Niopek D. Bioswitches: Towards programmable, on-demand control of therapeutic proteins. Clin Transl Med. 2026 Feb;16(2):e70612. doi:10.1002/ctm2.70612. PMID: 41630504; PMCID: PMC12865224.
[2] American Cancer Society. Global Cancer Facts & Figures, based on GLOBOCAN 2024 (IARC). cancer.org. https://www.cancer.org/research/cancer-facts-statistics/global-cancer-facts-and-figures.html. Accessed September 22, 2026.
[3] Wolf B, Shehu P, Brenker L, von Bachmann AL, Kroell AS, Southern N, Holderbach S, Eigenmann J, Aschenbrenner S, Mathony J, Niopek D. Rational engineering of allosteric protein switches by in silico prediction of domain insertion sites. Nat Methods. 2025;22(8):1698–1706. doi:10.1038/s41592-025-02741-z.
[4] Merck & Co., Inc. KEYTRUDA (pembrolizumab) Dosing Options. keytrudahcp.com. https://www.keytrudahcp.com/dosing/options/. Accessed September 22, 2026.