Week 3 HW: Cell-Free Reaction Design

Literature-guided design and optimization of cell-free reaction formulations

Jennifer Zayetsy Almeyda Tejada

1. Objective

The goal of this project is to design 20-µL cell-free protein synthesis (CFPS) reactions for sfGFP, PETase/TfCut2, and Reteplase that reduce reagent cost while maintaining or improving production of functional protein. The three targets provide an increasing challenge in folding: sfGFP is a robust reporter with no disulfide bond, TfCut2 contains one disulfide bond, and Reteplase contains nine disulfide bonds and therefore requires substantially stronger control of the oxidative folding environment.

The design strategy used here is intentionally different from simply copying one published formulation. We first benchmark the formulations already represented in the course literature table, then identify the biochemical and economic rationale behind their differences. Finally, we use those observations to construct target-specific candidate formulations. The numerical concentrations are therefore literature-constrained hypotheses, not experimentally validated optima.

Inventory note. The formulations below are the proposed designs we derived from the literature benchmark. Components that are available in the current inventory are used directly. If a proposed component is not represented in the inventory or is not recognized by the Reagent Supplement interface, it is retained as a documented proposal and should be listed in the submission notes rather than silently replaced by another reagent.

2. Experimental constraints

Each reaction has a total volume of 20,000 nL (20 µL).

Fixed components:

The remaining 11,000 nL must be composed of available reagents and nuclease-free water. Reagent volumes are constrained to multiples of 25 nL.

The 10X base buffer contributes approximately 30 mM HEPES, 150 mM potassium glutamate, 2.6 mM magnesium glutamate, 1 mM 17-amino-acid mix, 1 mM tyrosine, and 1 mM cysteine to the final reaction. Therefore, concentrations reported below are final concentrations after accounting for the fixed base buffer.

3. Literature benchmark

3.1 PANOx-SP versus RFopt: two fundamentally different design philosophies

The literature table assembled by Olsen et al. provides a useful progression [1] from conventional phosphorylated-energy formulations to low-cost formulations. PANOx-SP uses PEP as the principal energy substrate and supplies ATP-generating capacity through a conventional phosphorylated-energy system. RFopt instead removes PEP and uses a combination of non-phosphorylated substrates, NMPs, ribose, and other low-cost components.

In Olsen et al.’s direct benchmark, PANOx-SP produced the highest sfGFP yield among the previously published formulations tested in their BL21 Star (DE3) lysate, but non-phosphorylated systems had substantially lower reagent cost per gram of protein. Their subsequent optimization produced RFopt at 2.4 ± 0.3 g/L sfGFP and approximately $60/g protein in reagent cost, compared with $4,083/g for PANOx-SP. This is a 99% reduction in reagent cost while increasing yield relative to PANOx-SP.

The important lesson is that yield and cost are not optimized by the same reagent choices. PEP can support high initial energy regeneration, but it is expensive and generates phosphate. Non-phosphorylated systems can lower cost and sustain ATP availability by relying on the metabolic capacity remaining in the lysate.

The values above should be interpreted as formulation comparisons rather than universal optima because lysate composition, reaction geometry, and oxygen transfer also affect performance.

3.2 Component-by-component benchmark

Salts and ionic environment

Question A1: Why do potassium glutamate and magnesium glutamate differ between formulations, and which is more sensitive?

They perform different biochemical roles and are not interchangeable. Potassium glutamate mainly establishes ionic strength and affects the intracellular-like environment of the lysate, whereas magnesium is directly involved in ribosome function, nucleotide binding, and many enzymatic reactions. Consequently, increasing K-glutamate can be tolerated over a much broader range than magnesium. Independent active-learning work has also identified magnesium as the more sensitive variable, with a sharp expression optimum around 10 mM in one IVTT system. Thus, a reasonable design principle is to treat Mg²⁺ as a narrow-range variable and K⁺ as a broader-range variable.

Energy systems

Question A2: PEP+NTP versus ribose+NMP

A PEP+NTP formulation supplies both an energy substrate and the final triphosphate forms required for RNA synthesis. PEP rapidly supports ATP regeneration but generates inorganic phosphate as it is consumed. In contrast, a ribose+NMP formulation supplies less phosphorylated, less expensive nucleotide precursors and relies on the metabolic machinery remaining in the lysate to regenerate NTPs. Olsen et al. showed that RFopt began without exogenous ATP yet maintained approximately 250 µM ATP throughout the reaction, while PANOx-SP generated an early ATP surge followed by depletion and substantially more phosphate accumulation.

This is not simply a substitution of one reagent for another. It changes the metabolic architecture of the reaction.

Nucleotides, ribose and nucleosides

NTPs are among the expensive components of classical CFPS. Olsen et al. found that NMPs could support a much cheaper formulation. Importantly, the replacement strategy is not uniform across nucleotide classes.

• GMP could be replaced by guanine + ribose without loss of protein yield.
• UMP replacement by its corresponding base + ribose reduced yield.
• Complete replacement of NMPs by nucleosides or bases was generally detrimental.
• Smith et al. independently found that partial replacement is beneficial and that AMP can be partially replaced by adenosine.
• Smith et al. also found that attempts to replace CMP and UMP with their nucleosides did not work.

Therefore, our designs retain NMPs rather than eliminating them completely. We use ribose as a metabolic precursor while preserving sufficient direct NMP input.

Buffers

HEPES varies substantially between formulations. Classical PANOx-SP uses approximately 57 mM, whereas RFopt uses 75 mM. Smith et al. observed that higher HEPES could substantially improve titer by preventing pH-driven yield collapse, although the optimum depended on the rest of the formulation.

This is important for our designs because buffer is not treated as a passive background component. We deliberately retain moderate HEPES rather than minimizing it solely for cost.

Polyamines

Spermidine and putrescine are present in many classical formulations. They can affect nucleic-acid interactions and ribosomal function. Smith et al. found a strong association between spermidine addition and increased titer in their experimental search [6].

Our designs therefore retain a modest spermidine concentration rather than eliminating it completely. We deliberately use a lower level than the 1.5 mM commonly seen in PANOx-SP because the objective is to balance performance with reagent cost and reaction volume.

Cofactors

NAD and CoA occur in classical formulations, but Olsen et al. excluded expensive tRNA, CoA and NAD+ from their optimization because previous work showed that these components can be unnecessary in non-dialyzed lysates. Smith et al. later found nicotinamide in all of their top-performing formulations.

We therefore distinguish NAD+ from nicotinamide. Our designs do not add NAD+, while sfGFP and PETase retain a moderate nicotinamide concentration because it repeatedly appeared in the later AI-guided formulations.

Oxygen and redox

Oxygen is not merely a nuisance variable. Olsen et al. showed that RFopt became more sensitive to oxygen transfer than PANOx-SP [1]. Increasing the oxygen feed in a bioreactor from 20% to 100% increased sfGFP production by approximately 45%, reaching 3.7 ± 0.2 g/L.

Because the present reaction platform does not provide an adjustable oxygen-feed parameter, we use available oxygen-related chemistry conservatively. For sfGFP we include catalase as a candidate component because it appeared in the experimentally successful Smith formulation. This should be interpreted as a hypothesis about oxidative stress management, not as equivalent to increasing dissolved oxygen.

Disulfide components

The three proteins deliberately span increasing folding difficulty:

• sfGFP: no structural disulfide bond.
• TfCut2/PETase: one disulfide bond.
• Reteplase: nine disulfide bonds.

Therefore, applying one oxidative formulation to all three targets would not be biologically justified.

Olsen et al. demonstrated that oxidative RFopt containing glutathione and DsbC can support disulfide-containing proteins, but also showed that indiscriminate addition of IAM/GSSG/GSH can dramatically reduce sfGFP production. Importantly, increasing HEPES pH to 7.5 recovered yield by preventing the pH decrease caused by glutathione.

Our design therefore uses a gradient of oxidative pressure rather than a binary reducing/oxidizing switch.

4. Additional literature contrasts

4.1 Magnesium is a sensitive variable

Jewett and Swartz reported very different Mg-glutamate concentrations across PANOx-SP and Cytomim, while later active-learning experiments showed that magnesium can have a sharp optimum. This explains why transferring a magnesium concentration directly from one formulation to another is unsafe.

Our designs therefore keep magnesium close to the experimentally recurrent ~8 mM region instead of making a large protein-specific change.

4.2 PEP versus pyruvate/glucose

Jewett and Swartz showed that PEP-based systems accumulate substantial phosphate, whereas Cytomim using pyruvate accumulated much less phosphate. Calhoun and Swartz further demonstrated that glucose can support high protein production when pH and phosphate limitation are controlled.

These studies provide the mechanistic basis for replacing expensive phosphorylated energy substrates with inexpensive metabolic substrates.

4.3 Modern optimization confirms that interactions matter

Caschera et al. used evolutionary design of experiments to optimize CFPS and obtained conditions substantially better than the starting standard. Borkowski et al. later used active learning to explore hundreds of reaction conditions. Olsen et al. combined definitive screening designs, statistical models, active learning and domain expertise across 1,231 formulations.

This supports our methodological choice: we do not claim that a mathematical formula can calculate the true optimum concentration without experimental data. Instead, we use literature-supported concentration windows and formulate a testable candidate.

5. Target-specific candidate formulations

5.1 sfGFP — oxygen-aware low-cost NMP/ribose formulation

Hypothesis

sfGFP is a relatively robust protein and therefore provides the cleanest target for maximizing the benefit of the low-cost NMP/ribose strategy. We start from the experimentally successful region defined by RFopt and Smith et al., retain moderate HEPES, K-glutamate, Mg-glutamate, phosphate, ribose and NMPs, and add catalase as an oxygen/redox-related variable.

The formulation is intentionally not a copy of RFopt or 1793122_35. We reduce the total NMP burden to reduce cost and reaction volume, retain partial AMP substitution with adenosine, and keep catalase at the concentration used in the Smith top-performing formulation.

Candidate final concentrations

Why these changes?

• Ribose + NMPs: based directly on Olsen and Smith.
• AMP + adenosine: Smith found evidence for partial AMP replacement by adenosine.
• CMP/UMP retained: Smith found that replacing these pyrimidines was detrimental.
• Phosphate ~7.5 mM: within the strong-performing 5–10 mM region identified in the autonomous-lab experiments.
• Moderate HEPES: supports pH stability.
• Catalase: experimentally present in the Smith top-performing composition and therefore preferable to inventing an arbitrary oxygen-related reagent concentration.
• No PEP: avoids the major cost and phosphate burden of the classical system.

5.2 PETase/TfCut2 — mild oxidative folding formulation

Hypothesis

TfCut2 contains one disulfide bond. It therefore occupies an intermediate position between sfGFP and Reteplase. We propose keeping the low-cost NMP/ribose backbone while introducing a mild oxidative folding module consisting of GSSG, GSH and DsbC.

The concentrations are deliberately lower than the Reteplase oxidative system. This is a mechanistic hypothesis: one disulfide bond should not require the same oxidative burden as nine disulfide bonds.

Candidate final concentrations

Rationale

The GSSG/GSH pair provides a controlled redox environment, while DsbC provides disulfide-bond isomerization capacity. The important point is not simply that PETase receives “more reagents”: we are adding a folding module that is biologically relevant to its one disulfide bond [1].

We deliberately keep this module milder than the Reteplase condition. Olsen et al. showed that oxidative supplementation can be strongly protein-dependent and that excessive oxidative conditions can reduce sfGFP expression, even though oxidative RFopt can support disulfide-containing proteins [1]. Thus, the difference between PETase and Reteplase is a hypothesis based on folding complexity, not an experimentally established optimum.

This is a protein-specific adaptation, not a claim that 1 mM GSSG/0.5 mM GSH is an experimentally established TfCut2 optimum.

5.3 Reteplase — controlled oxidative folding formulation

Hypothesis

Reteplase contains nine disulfide bonds and therefore requires a fundamentally different folding environment. We retain the cost-saving NMP/ribose energy architecture but increase oxidative support and DsbC relative to PETase.

Candidate final concentrations

Rationale

The main change relative to PETase is the strength of the oxidative-folding module. Reteplase has nine disulfide bonds, so we hypothesize that it needs substantially more redox support than a one-disulfide protein. We therefore increase GSSG/GSH and DsbC rather than simply copying the PETase formulation.

The GSSG/GSH concentrations are anchored to the oxidative RFopt literature, and the 5 µM DsbC level follows the oxidizing disulfide-protein condition described by Olsen et al. [1]. This is the strongest protein-specific change in our design.

We also keep the ribose/NMP energy architecture rather than returning to a PEP/NTP system. The reason is economic: the benchmark shows that non-phosphorylated energy systems can maintain production at much lower reagent cost [1]. In other words, we are changing the folding environment because Reteplase needs it, but we are not giving up the cost-saving energy strategy.

A separate literature on recombinant Reteplase refolding supports the importance of a controlled GSH/GSSG environment. Importantly, that work concerns post-expression refolding, not CFPS itself, so we use it as mechanistic support rather than as a direct recipe [8].

5.4 What changes across the three formulations and why?

The central idea is therefore not to optimize every component independently. We preserve the parts of the formulation that are supported by the cost/yield benchmark and change only the components for which the protein creates a specific biochemical reason to do so.

6. How the numerical concentrations were selected

We did not calculate a theoretical optimum from first principles. Instead, each concentration follows this hierarchy:

1. Published experimentally successful concentration when available.
2. Range observed across multiple published formulations when no single optimum exists.
3. Protein-specific mechanistic adjustment when the target has a distinctive folding requirement.
4. Volume and inventory constraints imposed by the 20-µL reaction.
5. 25-nL dispensing resolution.

For example:

Mg-glutamate

Literature repeatedly places Mg-glutamate in the low-millimolar range, and independent active-learning experiments demonstrate strong sensitivity to magnesium. We therefore select approximately 8.2 mM rather than treating magnesium as a freely tunable high-range variable.

Ribose

RFopt uses 50 mM, while the Smith top-performing formulations span approximately 40–77 mM. We select 40 mM for sfGFP and PETase and 30 mM for Reteplase to leave volume for the larger oxidative-folding module.

GSSG/GSH

PETase receives a mild redox condition as a hypothesis, whereas Reteplase receives the stronger oxidized RFopt-like condition. This follows the number of structural disulfides rather than copying a single formulation across all proteins.

7. Volume calculations

For a 20-µL reaction:

[ V_i = \frac{(C_{target}-C_{base})V_{reaction}}{C_{stock}} ]

where:

• (C_{target}) = desired final concentration
• (C_{base}) = contribution from the fixed 2-µL 10X base buffer
• (V_{reaction}) = 20,000 nL
• (C_{stock}) = reagent stock concentration

Volumes are then rounded to the nearest 25 nL.

Final reaction volumes

8. R script used to calculate reaction volumes

# ============================================================
# HTBAA Week 3 — Literature-guided CFPS formulation design
# All reaction volumes are in nL.
# Final reaction volume = 20,000 nL.
# ============================================================

reaction_volume <- 20000
fixed <- list(
  lysate = 5000,
  template = 2000,
  base_buffer = 2000
)

variable_capacity <- reaction_volume - sum(unlist(fixed))

# Stock concentrations
# mM unless otherwise noted
stock <- c(
  hepes_koh = 1000,
  potassium_glutamate = 1550,
  magnesium_glutamate = 500,
  amino_acid_mix_17 = 50,
  tyrosine = 50,
  cysteine = 200,
  glucose = 1000,
  potassium_phosphate = 500,
  ribose = 666.6667,
  amp = 20,
  cmp = 20,
  gmp = 20,
  ump = 20,
  adenosine = 20,
  nicotinamide = 100,
  spermidine = 20,
  gssg = 100,
  gsh = 100,
  dsbc_ecoli = 100
)

# Contribution from the fixed 2-uL 10X base buffer
base_contribution <- c(
  hepes_koh = 30,
  potassium_glutamate = 150,
  magnesium_glutamate = 2.6,
  amino_acid_mix_17 = 1,
  tyrosine = 1,
  cysteine = 1
)

# Desired final concentrations
targets <- list(

  sfGFP = c(
    hepes_koh = 45,
    potassium_glutamate = 280,
    magnesium_glutamate = 8.2,
    amino_acid_mix_17 = 4.8,
    tyrosine = 1.2,
    cysteine = 5,
    glucose = 8.3,
    potassium_phosphate = 7.5,
    ribose = 40,
    amp = 0.5,
    cmp = 0.75,
    gmp = 1.0,
    ump = 0.75,
    adenosine = 0.4,
    nicotinamide = 4,
    spermidine = 0.6,
    catalase = 187.5
  ),

  PETase = c(
    hepes_koh = 45,
    potassium_glutamate = 280,
    magnesium_glutamate = 8.2,
    amino_acid_mix_17 = 4.8,
    tyrosine = 1.2,
    cysteine = 5,
    glucose = 8.3,
    potassium_phosphate = 7.5,
    ribose = 40,
    amp = 0.5,
    cmp = 0.75,
    gmp = 1.0,
    ump = 0.75,
    nicotinamide = 3,
    spermidine = 0.6,
    gssg = 1,
    gsh = 0.5,
    dsbc_ecoli = 2.5
  ),

  Reteplase = c(
    hepes_koh = 57.5,
    potassium_glutamate = 280,
    magnesium_glutamate = 8.2,
    amino_acid_mix_17 = 4.0,
    tyrosine = 1.0,
    cysteine = 4.0,
    glucose = 8.3,
    potassium_phosphate = 7.5,
    ribose = 30,
    amp = 0.5,
    cmp = 0.5,
    gmp = 0.75,
    ump = 0.5,
    spermidine = 0.6,
    gssg = 4,
    gsh = 1,
    dsbc_ecoli = 5
  )
)

# Catalase stock is U/mL rather than mM
stock["catalase"] <- 50000

calc_volume <- function(reagent, target) {

  base <- ifelse(
    reagent %in% names(base_contribution),
    base_contribution[reagent],
    0
  )

  # Catalase is expressed in U/mL.
  if (reagent == "catalase") {
    volume <- target / stock[reagent] * reaction_volume
  } else {
    volume <- (target - base) / stock[reagent] * reaction_volume
  }

  # All dispensing volumes must be multiples of 25 nL.
  round(volume / 25) * 25
}

make_recipe <- function(targets_for_protein) {

  volumes <- sapply(
    names(targets_for_protein),
    function(x) calc_volume(x, targets_for_protein[[x]])
  )

  total_variable <- sum(volumes)
  water <- variable_capacity - total_variable

  if (water < 0) {
    stop("Variable reagent volume exceeds the 11,000-nL capacity.")
  }

  recipe <- c(
    fixed,
    as.list(volumes),
    nuclease_free_water = water
  )

  # Check total volume
  stopifnot(sum(unlist(recipe)) == reaction_volume)

  recipe
}

recipes <- lapply(targets, make_recipe)

recipes

9. Interpretation and limitations

These formulations are candidate designs, not experimentally established optima. The literature demonstrates that CFPS is strongly nonlinear and that reagent interactions matter. Olsen et al. required 1,231 experimental formulations and combined Design of Experiments, statistical modeling, active learning and domain expertise to obtain RFopt. Caschera et al. and Borkowski et al. similarly demonstrated that experimental feedback is essential for navigating the high-dimensional formulation space.

Therefore, the appropriate claim is not:

“These are the theoretically optimal concentrations.”

Instead:

“These are literature-constrained, protein-specific candidate formulations designed to occupy experimentally supported regions of the CFPS design space while minimizing reagent cost and respecting the reaction-volume constraints.”

A true optimization stage would experimentally measure titer and functional activity across candidate formulations and then use DOE/response-surface or active-learning methods to refine the concentrations and interactions.

For Reteplase in particular, functional activity rather than total protein yield should be the relevant endpoint because the main challenge is correct disulfide pairing and folding.

10. Overall design logic

Published formulations
        ↓
Component-by-component benchmark
        ↓
Cost + biochemical function
        ↓
Identify robust concentration ranges
        ↓
Literature-supported design rules
        ↓
Protein-specific modifications
        ↓
Volume-constrained candidate formulation
        ↓
Experimental validation / future DOE

The central design principle is therefore:

We are not replacing the literature benchmark with our own ideas. We are using the benchmark as the foundation and adding experimentally motivated, protein-specific modifications on top of it.

References

1. Jewett, M. C. & Swartz, J. R. Mimicking the Escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnology and Bioengineering (2004). DOI: 10.1002/bit.20025.

2. Calhoun, K. A. & Swartz, J. R. Energizing cell-free protein synthesis with glucose metabolism. Biotechnology and Bioengineering (2005). DOI: 10.1002/bit.20449.

3. Caschera, F. et al. Coping with complexity: machine learning optimization of cell-free protein synthesis. Biotechnology and Bioengineering (2011). DOI: 10.1002/bit.23178.

4. Borkowski, O. et al. Large scale active-learning-guided exploration for in vitro protein production optimization. Nature Communications 11, 1872 (2020). DOI: 10.1038/s41467-020-15798-5.

5. Olsen, M. L. et al. Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression. Nature Communications 17, 3478 (2026). DOI: 10.1038/s41467-026-69605-8.

6. Smith, A. A. et al. Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis. bioRxiv (2026). DOI: 10.64898/2026.02.05.703998.

7. Silverman, A. D. et al. Cell-free gene expression: an expanded toolkit for synthetic biology. Nature Reviews Genetics (2020).

8. Fathi, M. et al. Efficient refolding of recombinant reteplase expressed in Escherichia coli strains using response surface methodology. (2020). PMID: 32698065.

9. Fu, C. et al. A cell-free platform for the high-efficiency synthesis and screening of PET hydrolase. Journal of Biotechnology 417, 101–107 (2026). DOI: 10.1016/j.jbiotec.2026.05.012.