Protein Engineering
What it is. A custom design campaign to improve a protein that does not behave the way you need it to — most commonly poor solubility or no functional expression in E. coli, but also low thermostability, aggregation, or weak activity. You supply the target sequence (and the problem, e.g. "insoluble inclusion bodies in E. coli"); we return a ranked, buildable panel of engineered variants with a per-mutation rationale, predicted effects, codon-optimized DNA for the top designs, and a suggested expression/screening plan.
Maturity: Research preview — outputs are computational hypotheses that require wet-lab validation.
The four design strategies we combine
| Strategy | What it does | When we use it |
|---|---|---|
| Rational design | Uses a computed structure (AlphaFold / ESMFold) and biophysical models to predict exactly which point mutations improve the target property (e.g. replace exposed hydrophobic patches, add stabilizing contacts, fix aggregation-prone regions). | First line when a good structure/model exists and the problem localizes (surface hydrophobicity, a flexible loop, a specific liability). |
| Semi-rational design | Focuses randomized/combinatorial libraries on a small set of structure- or conservation-informed positions, cutting the search space by orders of magnitude versus fully random libraries. | When single mutations are not enough but the useful positions can be narrowed down. |
| Directed evolution (design support) | We design smart, low-redundancy variant libraries and screening strategy that mimic iterative natural selection; you run the screen, and results feed the next design round. | When the fitness landscape is unknown and iterative wet-lab selection is available (the approach honored by the 2018 Nobel Prize in Chemistry). |
| De-novo / AI design | Uses deep-learning structure prediction and generative models (AlphaFold-class folding, and — on the GPU tier — RFdiffusion/ProteinMPNN-style tools) to redesign or graft regions, or build new solubilizing scaffolds. | When conservative edits cannot solve the problem and a larger redesign is justified. |
Method (typical flow)
- Diagnose. Predict the structure (AlphaFold/ESMFold), map hydrophobic surface patches and aggregation-prone regions, and identify the likely cause of insolubility / misfolding.
- Design. Generate variants across the strategies above — surface supercharging / hydrophobic-patch removal, stabilizing substitutions, loop/terminus engineering, fusion-tag and truncation options.
- Score & rank. Predict fold stability (ΔΔG), solubility/expression propensity, and retained function/conservation; rank the panel and keep the full provenance.
- Deliver. A ranked variant table with rationale, codon-optimized DNA for the top constructs, and a recommended expression host / screening plan.
Deliverables
- Structure + aggregation/solubility diagnosis (model + hydrophobic-patch and aggregation-prone-region map)
- Ranked variant panel across rational / semi-rational / de-novo strategies, with per-mutation rationale
- Predicted ΔΔG (stability) and solubility/expression scores per variant
- Codon-optimized DNA for the top constructs + a suggested expression and screening plan
- Design provenance and an explicit limitations report
Two reference proteins illustrate the range: an easy target (e.g. Cre recombinase — highly soluble, straightforward E. coli expression) and a hard target (e.g. Flp recombinase — historically very difficult to express solubly in E. coli), where a solubility-engineering campaign is exactly the point.
▶ Order / request a quote on excellgen.com →
List price: $1,499 (typical campaign; scoped per target) · Typical turnaround: 10–20 business days
Pricing & payment
The list price below is a starting point; final pricing depends on input size and scope and is confirmed on your order. Ways to engage:
- In the workbench — sign in to your NextELN workspace, open Services, upload your input, review the quoted price, and sign the service agreement. Operator-run; you are notified when results are ready.
- By invoice / PO — for institutional customers we invoice on delivery (Net-30 by arrangement). Card payments are handled via Stripe.
- Custom scope — email services@excellgen.com to discuss multi-target campaigns, retainers, or collaborative projects.
Excellgen, Inc. is the service provider. Results are computational predictions for research use only and require independent wet-lab validation; no diagnostic, clinical, or regulatory use is authorized.
Prepared with the NextELN AI Lab Assistant; scientifically reviewed by the NextELN science team, Excellgen, Inc. · All services on excellgen.com