iGEM 2025 — Team hamburg

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{% block title %}Lab Notebook | iGEM Hamburg 2025{% endblock %}
{% block head %}Our Lab Notebook{% endblock %}
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#### More information

You can download our pdf [here](https://static.igem.wiki/teams/5561/findmore/labnotebook.pdf).

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Engineering Success cycles for iGEM Hamburg 2025 DeathCapTrap: iterative Wet Lab and Dry Lab design-build-test-learn cycles, nanobody construct optimization, computational protein design, plasmid mapping, and children's science communication book development.
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iGEM Hamburg 2025, engineering success, design build test learn, nanobody construct, alpha-amanitin, plasmid design, wet lab cycle, dry lab cycle, computational protein design, LigandMPNN, RFdiffusion, Boltzdesign1, children's book science education
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{% block title %}Engineering Success | iGEM Hamburg 2025{% endblock %}
{% block head %} Engineering  | iGEM Hamburg 2025 {% endblock %}
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Engineering

[Wet Lab](#wetlab)

[Achievements Wet Lab](#achievementsWetLab)
[Cycle 1](#WLcycle1)
[Cycle 2](#WLcycle2)
[Cycle 3](#WLcycle3)
[Dry Lab](#DryLab)

[Cycle 1](#DLcycle1)
[Cycle 2](#DLcycle2)
[Cycle 3](#DLcycle3)
[Children Book](#childrenBook)

[Cycle 1](#CBcycle1)
[Cycle 2](#CBcycle2)
[Sources](#sources)

![Intro for the iGEM engineering page](https://static.igem.wiki/teams/5561/images/wiki/engineering/introimg.webp)

As the WetLab and DryLab parts of our project ran independently and in parallel to each other, we went through
individual cycles of challenges and learnings. Some of these we present here. Additionally, we show the process of
writing a children’s book about nanobodies, as we could also apply the engineering cycle to this project part.

## Wet lab

#### Achievements

We designed different plasmid constructs for an anti-GFP nanobody and GFP, carrying a secretion signal for
*E.
coli* as well as a nuclear localization signal and a 6x His-tag for purification. We expressed two of
those
constructs in BL21(DE3) *E. coli* and validated the expression through a Western Blot. Additionally, we
broadened our methodological skills by learning different protein production, purification and isolation
procedures such as growing and inducing *E. coli* liquid cultures, Ni-NTA purification, protein
precipitation, sonification and periplasmic extraction, and analyses such as SDS-PAGE and Western Blot.

---

## 1. Cycle - Design of nanobody and GFP constructs

**Design:** Our aim in the WetLab was to set up a production pipeline for a recombinant nanobody construct
in
*E. coli*. The construct design was informed by extensive literature research. We chose a strong RBS
(BBa\_B0030) for increased protein expression. The reading frame starts with a pelB sequence (derived from
BBa\_K2114998) for periplasmic secretion, followed by a 5x aspartate linker for subsequent extracellular
secretion [[1]](#source1). We then added a HRV 3C protease cleavage site to be able to remove the 5x aspartate linker. This
is
followed by the CDS for a nanobody against GFP (derived from BBa\_K2114998), which we will use as a placeholder
for
our own nanobody. Additionally, we designed constructs containing the CDS for GFP as a control for the
production
of the nanobody. Right behind the CDS, we added a linker and a 3x NLS for nuclear localization [[2]](#source2). After this,
we
added a second protease cleavage site, this time for the TEV protease for cutting off the 6x Histidine tag or
strep-tag II.

We named our constructs GFP\_his and aGFP\_his. For the expression, we used the widely used *E. coli*
strain
BL21(DE3)
which is a T7 expression strain and optimized the DNA sequence for the strain’s codon-usage.

**Build:** We ordered the constructs for GFP and the anti-GFP nanobody from our sponsor, Twist Bioscience,
who
synthesized the insert and cloned it into a pET blank vector lacking the RBS. Then we introduced the
constructs
into BL21(DE3) *E. coli* through heat shock transformation.

**Test:** We induced the production of our protein constructs with 0.5 mM of IPTG, incubated for 3h at 37
°C
and tested different volumes of liquid cultures. As we designed our construct with a signal sequence and a
linker
for secretion into the periplasm and subsequently into the extracellular space, we expected our proteins to
appear
in the culture’s supernatant. Thus, we used protein precipitation with trichloracetic acid (TCA) and Ni-NTA
purification to concentrate and isolate our protein from the supernatant in various repetitions of the
experiment.
We analyzed the protein production using SDS-PAGE and Coomassie Blue staining (Fig. 1-3). To compare native
and
recombinant protein production, we introduced a negative control in the form of an uninduced liquid culture.
On
top, we used a His-tagged protein from the lab that was expressed intracellularly under the control of the
lac-operon and T7 promoter as a positive control for expression. The positive control is supposed to be
expressed
in the cytosol as it does not carry any secretion signals.
Unfortunately, we were not able to detect any strong or distinguishable bands at the expected sizes of our
protein
constructs (20 kDa for nanobody and 35 kDa for GFP construct) with either of the methods. We were able to
detect
our positive control (a phospholipase with a molecular weight of approximately 79 kDa), which produced a
strong
band at around 70 kDa as expected. We were not able to detect any additional bands in the cell pellet or
precipitated supernatant of our nanobody construct between induced and uninduced cultures (Fig. 1). Some gels
showed a band at 35 kDa in the precipitated supernatant (Fig. 2) before and after induction, which would match
the
expected size for the GFP construct. We were not able to detect any bands in the different steps of the Ni-NTA
purification of the supernatants.

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig1.webp)

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig2.webp)

*Figure 2: SDS-PAGE of GFP\_his.*  A band at 35 kDa appears post
induction in both induced and uninduced supernatant samples.
M = PageRuler™ unstained protein ladder (26614, Thermo Scientific). pre Ind. = pre-induction, post Ind. =
post-induction. - = uninduced. + = induced with 0.5 mM IPTG. CP = cell pellet. SN = TCA-precipitated
supernatant. Polyacrylamide concentration: 12%. Gel was run at 140 V for 1.25 h.

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig3.webp)

*Figure 3: SDS-PAGE of aGFP\_his.*  No band detectable at expected
size of 20 kDa for nanobody construct. Positive control shows a band at expected size of 79 kDa.
M = PageRuler™ Prestained Protein Ladder, 10 to 180 kDa (26616, Thermo Scientific). - = uninduced GFP\_his
(negative control). + = PLCN (positive control). CP = cell pellet. FT = flowthrough. W1 & W2 = wash steps 1
and
2. E1-4 = elution steps 1-4. Polyacrylamide concentration: 15%. Gel was run at 140 V for 1.25 h.

---

## 2. Cycle - Changing RBS, spacing and removing NLS

**Design:** To rule out any issues with the RBS regarding translation initiation, we redesigned our initial
constructs and created two new variations: in one of them, we reduced the spacing between RBS and CDS from
seven
basepairs to one basepair to match an existing iGEM part (BBa\_K5299200) which had been successfully expressed
in
BL21(DE3) *E. coli* before (RBS1\_GFP\_his and RBS1\_aGFP\_his). For the second variation, we switched out
the
previous
RBS (BBa\_B0030) with the RBS commonly used in pET vectors (RBSp\_GFP\_his and RBSp\_aGFP\_his).

Additionally, we decided to use recognition sites for the restriction enzyme EcoRI we introduced up- and
downstream of the nuclear localization signal (3x NLS). By removing the NLS, we wanted to rule out any issues
regarding translation or folding derived from this component. We named the new constructs GFP\_woNLS\_his and
aGFP\_woNLS\_his.

For our experimental design, we decided to try out a higher IPTG concentration of 1 mM for the induction of
the
protein production. We also increased the incubation time to overnight incubation and decreased incubation
temperature to 25 °C to obtain higher amounts of recombinant protein. Additionally, we decided to sonify the
cell
pellets to better access the protein content.

**Build:**
We ordered the different constructs with changed RBS (RBS1- and RBSp-constructs) from Twist Bioscience again
and
introduced them into BL21(DE3) *E. coli* through heat shock transformation. To create the constructs
without
3x NLS,
we used the restriction enzyme EcoRI and performed a restriction digestion. We isolated and purified the
linearized plasmid through gel electrophoresis and gel extraction and ligated it. Sequencing analysis verified
correct restriction and ligation (Fig. 4). These constructs were introduced into BL21(DE3) E. coli as well
through
heat shock transformation.

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig4.webp)

*Figure 4: Sequencing results for aGFP\_woNLS\_his clone 1.*Original sequence with annotations shown at the top, sequencing result with trace data shown at the
bottom.
Sequence in the area around the recognition site of the EcoRI restriction enzyme is correct.

**Test:**
Using our new experimental conditions, we repeated the protein production, precipitation of the supernatant
and
analysis by SDS-PAGE (Fig. 5 and 6). We again detected no distinguishable bands at the expected sizes for both
RBS1-constructs apart from strong bands at the size of 35 kDa, that were found in precipitated supernatants of
both
protein constructs. For the constructs without NLS, we could not detect any strong bands at expected sizes
(Fig.
6).

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig5.webp)

*Figure 5: SDS-PAGE of RBS1\_aGFP\_his.*  No band detectable
at
expected size of 20 kDa for nanobody construct. Negative control shows a band at size of 70 kDa, possibly
switched with positive control. Strong bands appear above 35 kDa in precipitated supernatant samples.
M = PageRuler™ Prestained Protein Ladder, 10 to 180 kDa (26616, Thermo Scientific). - = uninduced GFP\_his
(negative control). + = PLCN (positive control). CP = cell pellet. SN = Supernatant. 1-3 = RBS1\_aGFP\_his
clones
1-3. Polyacrylamide concentration: 15%. Gel was run at 140 V for 1.25 h.

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig6.webp)

*Figure 6: SDS-PAGE of RBS1\_GFP\_his and constructs without
NLS.* No band detectable at expected size of 20 kDa for nanobody construct. Negative control shows a
band
at size of 70 kDa, possibly switched with positive control. RBS1\_GFP\_his supernatant shows a strong band
above
35 kDa, similar to supernatant samples of RBS1\_aGFP\_his supernatant. M = PageRuler™ Prestained Protein
Ladder,
10 to 180 kDa (26616, Thermo Scientific). - = uninduced GFP\_his (negative control). + = PLCN (positive
control).
CP = cell pellet. SN = Supernatant. GFP = RBS1\_GFP\_his. 1 and 2 = GFP\_woNLS\_his clones 1 and 2. 3 and 4 =
aGFP\_woNLS\_his clones 3 and 4. Polyacrylamide concentration: 12%. Gel was run at 140 V for 1.25 h.

In a following repetition, we additionally performed Ni-NTA purification and analyzed the results via SDS-PAGE
and
Coomassie Blue staining. Again, we detected bands at 35 kDa in precipitated supernatant samples and no bands
at
all in the Ni-NTA purified supernatant samples (results not shown). To investigate if the bands at 35 kDa
consisted of our recombinant protein constructs, we repeated the SDS-PAGE and performed a Western Blot to
stain
any His-tagged proteins in the samples. The Western Blot finally showed us bands at the expected sizes of 20
kDa
for the nanobody and 35 kDa for the GFP in the cell pellet samples of the RBSp-constructs. Additionally, we
saw
one band of each of those sizes in the sample from RBS1\_GFP\_his (Fig. 7).

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig7.webp)

*Figure 7: Western Blot of RBS1- and RBSp-constructs.*
Positive control shows a strong band at size of 70 kDa in the cell pellet. RBS1\_GFP\_his cell pellet
shows
bands at 35 and 20 kDa. Cell pellet samples of RBSp\_GFP\_his and RBSp\_aGFP\_his show weak bands at 35 kDa
and
20 kDa, respectively.
M = PageRuler™ Prestained Protein Ladder, 10 to 180 kDa (26616, Thermo Scientific). + = PLCN (positive
control). CP = cell pellet. SN = Supernatant. 1 = RBS1\_GFP\_his. 2 = RBS1\_aGFP\_his. 3 = RBSp\_GFP\_his. 4 =
RBSp\_aGFP\_his. Antibody: Anti-6X His tag® antibody [AD1.1.10] - HRP conjugated (ab178563, abcam).
Antibody
concentration: 1:2000. Polyacrylamide concentration: 12%. Gel was run at 140 V for 1 h.

**Learn:**
We discovered that the Western Blot was a sensitive and specific method to detect our protein constructs.
Apparently, our proteins had not been secreted extracellularly as expected, as the precipitated supernatants
did
not show any signals. However, the secreted amounts might be too little to be detected.
We also did research on native proteins that are secreted in BL21(DE3) *E. coli* and found that outer
membrane
proteins like OmpA or OmpF can be released as well [[3]](#source3). These two proteins, with molecular weights of 37 kDa and
39
kDa, respectively, would match the band at 35 kDa that we saw repeatedly. After reaching out to Dr. Schäfer,
regarding his experience with nanobodies, he explained that the observed band might also represent the lacI
protein with an approximate molecular weight of 38.6 kDa. This protein is part of the lac-Operon present in
BL21(DE3) *E. coli* as well as on our introduced plasmid and might be produced in greater amounts than
other
native
proteins.

---

## 3. Cycle - Redesigning constructs and periplasmic extraction

**Design:** Before we got the results of the Western Blot, we had assumed that we could not detect our
protein
because it was simply not there. We discussed problems occurring on the RNA level, such as secondary RNA
structures inhibiting translation initiation or disrupting translation, as well as issues regarding protein
folding and subsequent degradation. As the iGEM part consisting of a pelB and the nanobody against GFP
(derived
from BBa\_K2114998) had been successfully expressed, we decided to go back to the basics and remove the 5x
aspartate linker from our design, as well as the recognition sites for protease cleavage. This left us with
constructs only consisting of a pelB, the CDS for the respective protein, and a 6x His-tag (part\_bas\_GFP\_his
and
part\_bas\_aGFP\_his). At this point in our project we realized that we had previously included restriction
enzyme
recognition sites in our designs that are not compatible with the iGEM BioBrick RFC[10] and the Type IIS
RFC[1000]
system. To make sure we adhere to these standards, we made sure to only include compatible sites in our new
design
for the reduced constructs, and also redesigned our original construct in a compatible way, so we could use it
as
a basis for future cloning steps (part\_GFP\_his and part\_aGFP\_his).

For the experimental design, we decided to perform a periplasmic extraction using osmotic shock after protein
production, as our protein constructs all contain a pelB-sequence for import into the periplasm. This way, we
wanted to validate if our protein was transported to the periplasm and stuck there, or if it was produced in
the
cytosol of the cells instead. We decided to analyze RBSp\_aGFP\_his again to validate the results of the
previous
Western Blot.

**Build:**
Again, we ordered the new redesigned constructs from Twist Bioscience. This time, both inserts were designed
without a RBS and cloned into a pET blank vector containing a RBS. We introduced the constructs into BL21(DE3)
*E. coli* through heat shock transformation. However, we only obtained very few colonies for the nanobody
constructs, and none for the GFP constructs.

**Test:**
After we had prepared overnight cultures for our new nanobody constructs, RBSp\_aGFP\_his and the positive
control,
we discovered that the cultures prepared from cryostocks, RBSp\_aGFP and the positive control, had not grown.
We
tried to grow those two again in 200 mL over the day, but were unsuccessful. We went on to perform
sonification
of
the cell pellets for 100 mL of the remaining liquid cultures, respectively. The remaining 100 mL were used for
periplasmic extraction through osmotic shock, respectively. We analyzed the sonified cell pellets as well as
the
cytosolic and periplasmic fraction after the periplasmic extraction using SDS-PAGE and Western Blot. However,
we
were not able to detect any bands, even when we increased the exposure time during imaging (Fig. 8).

![SDS-Page of different constructs](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig8.webp)

*Figure 8: Western Blot of part\_aGFP\_his and
part\_bas\_aGFP\_his.*
Sonified cell pellets, cytosolic and periplasmic fractions were analyzed for each construct. No bands
were
detected.
M = PageRuler™ Prestained Protein Ladder, 10 to 180 kDa (26616, Thermo Scientific). CP = sonified cell
pellet. C = cytosolic fraction after periplasmic extraction. PP = periplasmic fraction after periplasmic
extraction. Antibody: Anti-6X His tag® antibody [AD1.1.10] - HRP conjugated (ab178563, abcam). Antibody
concentration: 1:2000. Polyacrylamide concentration: 12%. Gel was run at 140 V for 1 h. Imaging was
performed using the ChemiDoc MP Imaging System (BIO-RAD). Exposure time: 40 s.

**Learn:**
In our last week in the lab, we learned that sometimes not everything goes as planned. As we only obtained
very
few colonies after transformation using our competent BL21(DE3) *E. coli*, we suspect that they might
have
lost
competency over time. As we observed, competent cells should be prepared regularly to ensure usability. When
we
took out our cryostocks from the -80 °C freezer for the previous experiment, we noticed that they had started
to
thaw. Multiple rounds of thawing and freezing might have damaged the cells to a point where they have died.

Since we did not have any positive control for our Western Blot, we have different hypotheses on why we did
not
see any bands in our samples. It might be possible that the antibody used for staining was damaged through
degradation, as it was stored at 4 °C for one week and not at -20 °C. During the periplasmic extraction and
sonification of the cell pellet, buffer amounts ranging between 3 and 10 mL were added. It is possible that
our
protein was present in those supernatants, however the dilution might have been too high for detection.
Lastly, a third possibility might be that the new constructs were simply not expressed due to reasons already
mentioned in the design part of the 3. cycle. This is unlikely though, as the construct part\_aGFP\_his only
differs
from RBSp\_aGFP\_his in terms of restriction enzyme sites and a decreased distance by six base pairs between lac
operon and RBS in the region upstream of the coding sequence. However, we cannot rule out any issues arising
from
these differences. Out of time constraints, we did not analyze the protein production further. We conclude
that
our RBSp-constructs for the anti-GFP nanobody and for GFP are expressed in BL21(DE3) *E. coli*. However,
we
suspect
that the expression is not optimal, as only small amounts of protein could be detected in a Western Blot. More
thorough optimization with detailed analyses of expression conditions such as temperature, duration of
induction
with IPTG and IPTG concentration would be needed to increase protein production. Additionally, different kinds
of
extracellular secretion signals should be tested to facilitate secretion of the constructs into the medium.

---

## Dry Lab

The goal was initially to design an α-amanitin-binding nanobody (NB) for intracellular detoxification. We chose
to design the nanobody in silico. We started out with very little knowledge on AI-assisted protein design.
However, we were aware of the many possibilities because of the Nobel Prize in Chemistry 2024.

#### Achievements Dry Lab

We created 50 binding proteins for α-amanitin with AI models for protein design and evaluated them using
internal quality metrics and structural comparison of the output. We then selected a few high-quality designs
for further *in silico* assessment. We used different structure-prediction models to validate and filter
for the
best designs. As an additional selection process, we predicted solubility, usability, propensity to aggregate
and binding affinity of our designs with multiple models. We found 14 designs to be of good and three to six,
depending on the assessment type, to be of exceptional quality. One of the best designs we registered as a part
for future teams to take as a starting point for design, use our structure as a scaffold or do WetLab
validation.

---

## 1. Cycle: *In silico* nanobody design against α-amanitin with RFantibody

**Design:**
Through a thorough literature research followed by a deep-dive into the Rosetta community's Youtube channel, a
superficial understanding was attained and the Dunning-Kruger effect took off.

We investigated NB *in silico* design and found a promising preprint about RFantibody, an AI-based pipeline
to design antibodies and NBs against epitopes of choice. To implement the software, we approached an AI service
provider called Nanohelix and were granted free use of the established models.

We prepared the design process by conducting a database search for high-resolution structures of our epitope and
were successful as α-amanitin was present in multiple structures on the PDB.

We assessed our epitope’s structure to see if there were different known conformations, but as the number of
experiments was limited and α-amanitin was always in complex with RNAPol II in documented structures, the
conformations did not differ significantly. At this point, we were confident that we could use the obtained PDB
files as inputs for our AI pipeline of choice.

**Build:**
We started to obtain multiple NB structures with different lengths of hypervariable loops and different input
PDB structures to produce varied binding modalities in hope of finding a high affinity binder.

**Learn:** The results of the RFantibody model looked promising on the first glance but we quickly realized
that multiple things were wrong:

* The binding site of α-amanitin is consistently on the lateral side of the NB
* Only the largest of the hypervariable loops is involved in the binding mode
* In the model results, the structure of α-amanitin is never conserved in its original form

See Fig. 9; For a deeper analysis we refer to our Dry Lab page [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27drylab%27%29%20%7D%7D%20).

![α-amanitin with all contact atoms of the binding protein design #15 and hydrogen-bonds displayed](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig9.webp)

*Figure 9:*  α-amanitin with all contact atoms of the binding protein design #15 and hydrogen-bonds displayed

We had the same problems with all of our designs. We analyzed the generated structures with ChimeraX and did
docking on a few selected designs to see if the binding to α-amanitin was given although the output complex did
not have an intact epitope present.

We were discouraged to keep going in this direction by our findings and sought help from experts and literature.

---

## 2. Cycle: Salvage attempts for RFantibody

**Design:** Before we got the opportunity to talk to professional protein engineers and in silico experts, we
tried
to implement a salvage strategy for our model pipeline. First, we tried to run RFantibody with a modified input
file. We deleted most post-translational modifications (PTMs) the peptide has in the PDB input file and
converted the sequence to an unmodified string [IWGIGCNP]. This way, we hoped that a structurally compatible
nanobody would be created by RFantibody that could later be modified to fit the actual α-amanitin. We planned
post-generation processing of the structure with LigandMPNN [[5]](#source5). This neural network is capable of generating
adequate sequences based on an input structure in the context of a non-protein binder (small molecule, DNA/RNA,
modification). Our hope was to generate a nanobody structure binding amantin without PTMs and assign a sequence
to the output optimized to bind the native original α-amanitin.

**Build:** For this approach, we removed all hydroxyl-groups added to the peptide after translation, more
precisely
from hydroxy-proline (HYP), di-hydroxy-isoleucine (ILX) and hydroxy-tryptophane (TLX). We kept the bi-cyclic
nature of the toxin to maintain the overall structure and conformation of the peptide and started generating
binding structures to this input with RFantibody.

Test: The results were sobering. Instead of output designs we got nothing. Our modified input did not
generate
any structures. We assumed the reason for this to be in our file processing.

**Learn:** Simply removing atoms in a PDB file, be it using ChimeraX or manipulating the file in a text
editor,
might have had unforeseen consequences that made the file unusable for the model.

Additionally, we tried running LigandMPNN on our initial outputs but could not set it up correctly at this
point. Adding a ligand instead of the broken α-amanitin turned out to be above our capabilities. Preliminary
docking results from the outputs were not usable by LigandMPNN because the poses created by SwissDock’s
Attracting Cavities 2.0 [[6]](#source6) did not have the adequate PDB coordinates we needed as an input. We ceased further
salvage attempts in favour of a new approach.

---

## 3. Cycle: In silico α-amanitin Binder Design with Boltzdesign1

**Design:**
Our consultations with Clara Schöder and Klara Kropivsek (see [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%20%7D%7D%20)), two computational biologists, confirmed our
concerns. The model pipeline we used was not suited to our needs because it was trained purely on protein data
and not capable of considering such small and modified targets as our α-amanitin. As witnessed in the
structures, the models that are part of the RFantibody pipeline both for structure generation and prediction,
omitted all of the PTMs that our peptide had and represented just the few canonical amino acids as an epitope.
This resulted in improbable binders.

As we identified the problem together with the experts, they gave us a solution to go forward: all-atom-capable
AI models that put out small molecule binding proteins.
Our goal was updated to design a protein binding α-amanitin without regarding the overall NB scaffold with the
following characteristics:

* The chance of binding the intended target is high
* The chance of binding to a similar off-target protein is low
* The binder expresses well in bacteria
* The binder is highly soluble

With research into recent literature and preprints, we decided on a suitable AI model for our purposes:
Boltzdesign1 is an all-atom protein design model, usable on Google Colab through a Jupyter notebook and more
efficient than comparable models in regard to resource usage and output quality. [[4]](#source4)

It has multiple advantages in comparison to RFdiffusionAllAtom for example, a model previously used to design
small molecule binders. (for more detailed information see [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27drylab%27%29%20%7D%7D%20))

**Build:** Our epitope from PDB entries was unfit for our initial pipeline, so we changed our approach for
our second model. α-Amanitin would be treated like a small molecule ligand instead of a highly modified peptide.
The difference is in the model input, in case of a peptide or protein this would mean sequence information and
coordinates obtained from PDB files. For a small molecule, we did not have to provide structural data but only
chemical signifiers like a SMILES string that could be translated to a set of atoms and bonds by the AI model.
With this processing, less initial information was put in that could obscure the inference and lead to
suboptimal outputs.

Boltzdesign1 uses a distogram, a graphical representation of distances between the binding protein and the
ligand, as well as a confidence module to iteratively find a structure. The process is separated into four
phases that each have their unique purpose. [[4]](#source4)

Among many variables, the user can adjust different metrics to influence the extent of the search, the
propensity of forming helices and the amount of interactions between binder and ligand.

At first we used default metrics for small molecule binder design provided in the Boltzdesign repository [source
boltzdesign github] but in time we found adjustments to better fit our goal:

* Increase likelihood of β-sheets to inhibit pure helical bundles
* Drastically increase number of contacts between the binder and the ligand to improve interactions
* Increasing the gradient phase of the program, that finds a “more realistic sequence representation” [[4]](#source4) for
  outputs with more confidence
* Optimize contacts per binder position
* Increase final optimization steps called semi\_greedy\_steps to maximize final confidence

As the paper suggested, we refrained from using recycle steps to not introduce effects that could worsen our
design and to save computing resources. Configurations used for our high-quality designs are deposited in our
Gitlab. [[7]](#source7)

**Test:** We created 50 designs in the progress of optimizing the configuration which we compared to one
another in
different stages. Exemplary design outputs can be seen in Figure 10.

1. We used internal prediction metrics of Boltzdesign1 to select high quality designs
2. We predicted the high-quality designs with Alphafold3 and Boltz2 and assessed the differences between
   Boltzdesign1 output and re-predictions in Backbone RMSD, RMSD specific to the Ligand and Backbone/Ligand RMSD
   of the re-predictions compared to themselves to look for reproducibility and homogeneity.
3. We looked at the protein-ligand interactions on a structural level and compared hydrogen-bonding, clashes, cavities and hydrophobic interactions
4. We predicted characteristics to assess feasibility of our designs and compare them among each other:
   solubility, usability, propensity to aggregate and binding affinity.

![Four design outputs from Boltzdesign1; Top row: Protein surfaces shown with binding site; Bottom row: Fold of the proteins with ligand](https://static.igem.wiki/teams/5561/images/wiki/engineering/fig10.webp)

*Figure 10:* Four design outputs from Boltzdesign1; Top row: Protein surfaces shown with binding site; Bottom row: Fold of the proteins with ligand

Most designs showed good internal values of plDDT above the goal threshold of 0.7, and ptm above 0.8, but only
14 of the designs had above-threshold metrics of complex and interface modelling. Even less designs had a
predicted distance error of the interface (ipde) of below 1, signifying high-confidence ligand binding.

From the quality alone, we could select 14 designs for further evaluation where we found six designs to perform
well in most assessments. Most importantly, the re-predicted ligands of three designs showed very homogeneous
conformations and binding modes.

*Figure 11:*  Twenty Boltz2 re-predictions superimposed to Boltzdesign1 output

We finally settled on design #15 as our best candidate. In re-predictions, the protein structure was shown to be
very homogeneous and the ligand displayed uniform binding conformations (see Fig. 11). Ligand RMSD is under 1 Å
between Boltz2 and Boltzdesign (cross-model) and 1.3 Å between the Boltz2 predictions (intra-model), falling
below the generally accepted thresholds of under 2 Å.

Design #15 also displays high predicted solubility by multiple AI models, high usability and low propensity to
aggregate in comparison to the other designs.
Predicted binding affinity is among the highest of all designs and structural evaluation of the binding modality
reveals multiple hydrogen-bonds, no clashes and potential hydrophobic interactions.

*Figure 12:*  α-amanitin with all contact atoms of the binding protein design #15 and hydrogen-bonds displayed

**Learn:**
What did we achieve when comparing our updated goal with its characteristics with our evaluated design
#15?

* The chance of binding the intended target is high with a predicted affinity of 8.85 kcal/mol (-ΔG)
* The chance of binding to a similar off-target protein is low, the binding pocket is quite closed and
  inaccessible
* The binder will probably express well in bacteria (high predictable “usability”)
* the binder is highly soluble (high predicted solubility)

---

## What do Lab coats and alpakas have in common? - A Childrens Booklet

### 1. Cycle

**Design:**
We started with a brainstorming meeting to develop the concept and story for our children's book. After a few
productive hours, we had our first story draft, accompanied by a moodboard with images we found on the
internet to convey the ideas for pictures that we had.

![Picture of brainstorming](https://static.igem.wiki/teams/5561/images/wiki/education/brainstorming.webp)

**Build:**
After conceptualisation, the painting began. Mariia started sketching all the images digitally. Next, all the
lines were carefully drawn so that we had a clean draft to be filled with colour.

![Picture of the sketch](https://static.igem.wiki/teams/5561/images/wiki/education/skizzeexample.webp)
![Picture of the colorized example](https://static.igem.wiki/teams/5561/images/wiki/education/fastfertigesbildbeispiel.webp)

**Test:**
The pictures were reviewed by the team and non-scientific test readers. Additionally, we conducted an
interview with Julia Offe on science communication (see [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%20%7D%7D%20)), target audiences, and possible ways to
distribute the book.

**Learn:**
We integrated the scientific and general feedback on pictures and story into our book, and reached out to
various initiatives and companies asking for co-operations to share our book with the audience.

---

### 2. Cycle

**Design:**
We decided on a square-shaped format of the book, similar to a popular German children’s book series, the Pixi
book. Additionally, we decided on a total number of 24 pages to keep the story short and precise.

**Build:**
Once all the images were finished, they were combined with the text of the story. We continued to work on the
general layout of the pages, moving pictures and text around to see what looks best.

**Test:**
The digital version of the finished book was sent to Alejandro Rojas-Fernandez and Waldemar Schäfer to gather
scientific feedback from nanobody experts. It was also reviewed again by our team, our PI and external test
readers.

**Learn:**
We made last visual changes, such as improving the distance of pictures and text from the edges. After several
requests, we also created a German version of the text for non-English speakers. Afterwards, our book was
ready for printing.

### Sources

1. Kim, S.-K., Min, W.-K., Park, Y.-C. & Seo, J.-H. Application of repeated aspartate tags to improving extracellular production of *Escherichia coli* L-asparaginase isozyme II. *Enzyme Microb. Technol.* 79–80, 49–54 (2015). [DOI](https://doi.org/10.1016/j.enzmictec.2015.06.017) [↩](#cite1)
2. Chertkova, A. O. *et al.* Robust and bright genetically encoded fluorescent markers for highlighting structures and compartments in mammalian cells. Preprint (2020). [DOI](https://doi.org/10.1101/160374) [↩](#cite2)
3. Xia, X.-X., Han, M.-J., Lee, S. Y. & Yoo, J.-S. Comparison of the extracellular proteomes of *Escherichia coli* B and K-12 strains during high cell density cultivation. *Proteomics* 8, 2089–2103 (2008). [DOI](https://doi.org/10.1002/pmic.200700968) [↩](#cite3)
4. Cho, Y., Pacesa, M., Zhang, Z., Correia, B. E. & Ovchinnikov, S. Boltzdesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design. *bioRxiv* (2025). [DOI](https://doi.org/10.1101/2025.04.06.647261) [↩](#cite4)
5. Dauparas, J. *et al.* Atomic context-conditioned protein sequence design using LigandMPNN. *Nature Methods* 22, 717–723 (2025). [DOI](https://doi.org/10.1038/s41592-024-02535-5) [↩](#cite5)
6. Bugnon, M. *et al.* SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina. *Nucleic Acids Research* 52, W324–W332 (2024). [DOI](https://doi.org/10.1093/nar/gkae266) [↩](#cite6)
7. Team Hamburg GitLab repository. <https://gitlab.igem.org/2025/software-tools/hamburg> [↩](#cite7)

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{% endblock %}

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{% block title %}Experiments | iGEM Hamburg 2025{% endblock %}
{% block head %}Experiments  | iGEM Hamburg 2025{% endblock %}
{% block body %}

## What Should this Page Contain?

---

Provide comprehensive, step-by-step protocols for all experiments conducted. Include a list of materials, reagents, and equipment used. Specify concentrations, volumes, incubation times, temperatures, and other critical parameters. Document any modifications or optimizations made to standard protocols.

Explain the purpose of each experiment and its relevance to your project goals. Describe the experimental design, including controls and replicates. Provide a clear rationale for the chosen methods and approaches.

Document any troubleshooting steps taken and optimizations made during the experimental process. Share any lessons learned that could benefit future teams.

## Inspirations

---

* [2024 Heidelberg](https://2024.igem.wiki/heidelberg/experiments)
* [2024 Marburg](https://2024.igem.wiki/marburg/experiments)
* [2024 EPFL](https://2024.igem.wiki/epfl/experiments)
* [2023 Estonia-TUIT](https://2023.igem.wiki/estonia-tuit/experiments)

{% endblock %}