From the beginning of our project, we were fascinated by nanobodies and lipid nanoparticles as powerful tools for creating versatile therapeutics. The idea for an application came during a brainstorming meeting, where suddenly secondary plant metabolites or toxins were mentioned. Mushrooms were thrown into the mix, and suddenly we had a project idea: DeathCapTrap, a nanobody-based antidote against the mushroom’s toxin α-amanitin. As the death cap is a common mushroom in Germany, it is often mistaken for edible mushroom species. Since literature research revealed that there is no specific antidote yet, and existing treatments are highly dependent on fast administration, we wanted to develop a highly-specific antidote that could still be applied in later phases of the poisoning. Through our approach using custom-designed nanobodies and a delivery vehicle with modifiable cell targeting we hope to help future scientists develop specialized treatments against intracellular toxins.
As part of the iGEM process, it was important to us to contribute to the progress of the community on a global as well as on a local level. This way, we considered helping the iGEM teams as a whole but still kept local networking and foundational problem solving as well as involvement with the scientific and non-scientific community on site in mind.
We wanted to provide advancements and novel support for future iGEM teams not directly connected to the Team Hamburg through distributive means, such as the iGEM registry and Gitlab. This way, we wanted to contribute to both Wet- and Dry-Lab.
Parts can be the most direct help for future teams as they can build upon, use and order them in a centralized manner circling around the iGEM registry. We divide our contribution to the registry in parts originating from our Wetlab and a part originating from our Drylab. For further information see our dedicated Parts site here.
To establish an experimental pipeline for recombinant nanobody expression, we designed various nanobody constructs containing signal sequences for extracellular secretion [6], protease cleavage sites and a nuclear localization sequence [5] derived from literature. Additionally, we optimized existing iGEM parts used in our design for codon-usage in BL21 E. coli. We decided to contribute to iGEM’s parts collection by adding four of our parts to the registry (BBa_2503U531, BBa_257TMLA6, BBa_25L5B0SQ and BBa_25BR1T4B here). Although we did not manage to validate these particular constructs experimentally, we hope that other iGEM teams can build on our designs and create constructs for extracellularly secreted and nuclear-targeted proteins themselves.
We tried finding a nanobody specific for the peptide-toxin alpha-amanitin but our in silico approach was cut short by the available AI models inability to use it as an input epitope. Alpha-amanitin is highly modified posttranslationally and conjoined twice to form its cytotoxic structure which makes it quite different from other peptides or small proteins.
So instead of a nanobody we designed binders with all atom capable protein design models. These AI models are trained on data not only including proteins from database entries of the PDB containing standard amino acid residues but also bound small molecules, posttranslational modifications and even DNA/RNA. With this training and a specialized architecture these neural networks can iteratively find a structure for a binding protein if faced with a ligand of choice.
With Boltzdesign1, one of these models, we created 50 designs, evaluated them computationally and used available models to predict characteristics. After vigorous selection we decided on protein design #15 that outperformed all other designs in relevant metrics to be registered as a part with the name “In silico designed Alpha-Amanitin binding protein” ( https://registry.igem.org/parts/bba-25owwae9).
We hope this contribution can help future iGEM researchers in their projects beyond detoxification as a starting structure for further design, as a scaffold for enzyme engineering or as a delivery modality for cancer treatment.
We wanted to ease the use of the AI models for protein design for future teams by providing freely available notebooks annotated for easy comprehensibility.
We went into the field of neural networks in life sciences with minimal pre-existing knowledge but a lot of hope to guide someone on a similar path. To help future users, we have collected a list of resources to introduce anyone interested in the field of AI-based protein structure prediction and generation and help them build a foundation to build upon and venture from into the direction their project demands.
The resources we collected are not just recent reviews, but rather easy-to-digest videos including playlists with scientific talks of researchers at the forefront of the field.
This way, we tried to cover the basics but also peak interest in different directions and methods.
We also wanted to help future teams directly by providing two Jupyter notebooks for the AI models we used, Boltzdesign1 [3] and LigandMPNN [2].
These notebooks are adapted from the primary source’s Github repositories with minimal change to the code itself but with annotations and comments to help guide the user. This includes notes on default settings, parameter adjustments and usage tips.
On our journey with the two models, we came across many specifics to improve our outcomes that we included in the notebooks.
Both of them can be run on Google Colab in its cost-free setting. It was important to us that the need for expensive server access or stand-alone hardware was avoided, so the models might be used decentrally and by anyone without specialized resources.
Our hope is to reduce the barrier of entry to protein design, to structural biology and maybe to bioinformatics. In our opinion, starting is the hardest part. Our goal was to make it a little easier. Lastly, we wrote a simple ChimeraX script for quick and easy protein design depiction in a visually uniform way, also annotated for easier understanding. The design output from Boltzdesign1 can quickly be visualized and evaluated in a repeatable way with running the script. Visualizing might help clarify design differences especially for learning structural biologists.
All resources are available in our Gitlab repository at https://gitlab.igem.org/2025/software-tools/hamburg.
As we had many creative heads in our iGEM team, we wanted to show this side of science as well. That’s why we wrote an educational booklet about nanobodies. With pictures drawn by our own team members, we wanted to break down central concepts of molecular biology and immunology and transport them to young minds. We printed 200 copies of the booklet so far and asked academic institutions, shops and schools if they were willing to distribute them.
To sustain the printing we also reached out to different companies.
Our hope is to be one tiny part of the reason why a future scientist went into the field of biology, and bring useful and exciting information to the non-scientific community in a fun and colorful way
We will also bring the booklet to Paris, so ask us if one is left for you.
As part of our educational and outreach program, we hosted international students at our affiliated lab and introduced synthetic biology, lab techniques and general scientific work to them. (see here") Organized by our PIs Prof. Dr. Michael Kolbe and Prof. Mirko Himmel, we had the opportunity to spend two weeks with them in the roles of teacher, student and teammate.
In the context of the SummerSchool, we also introduced the students to iGEM and asked whether their universities did support their own teams. In the conversation, it became clear that they had little knowledge about the competition and its community.
We brought them up to speed and found out that some of the universities did already have teams, they just never heard of.
We tried to convey the iGEM spirit to the students, hoping that they might join a future team and tackle a problem like we did.
In those cities without iGEM teams yet, we encouraged the founding of teams and offered organisational advice if needed. Our hope is to see them contribute on their own in the future.
To increase opportunities for future teams in Hamburg, we launched multiple long-term cooperations with regional science institutions, educational programs and technological facilities.
The goal of our project was to develop therapeutically useful biologics and one step on the journey to the application in humans is to assess the in vitro performance. To achieve this, we established a collaboration with the Liverlab [4] at the University Clinic of Eppendorf (UKE) in Hamburg.
As the UKE is specialized in human research, they can provide facilities, cell lines and expertise, in our case for hepatocytes, the cell type mainly affected by alpha-amanitin.
Beyond our own project we wanted to establish contact and collaboration with the UKE for future teams to supplement the research facilities at the DESY with specialized knowledge, technically skilled personnel and methods not present there.
The importance of bioinformatics in synthetic biology is apparent to the researchers in the field. As a team, we gradually got to know more about it and are now of the impression that its use by future teams is a certainty. Computational modelling as we did necessitates access to computing power beyond laptops or gaming PCs. To accommodate the use of AI and cutting edge software use for future teams, we connected with the Maxwell HPC cluster [5] team, with the IT at DESY and established a partnership with the help of our PIs. DESY agreed to the iGEM team using the GPU power of Maxwell for free and introduced us to the shared linux environment.
Our hope in using the high end machines at Maxwell and providing guidance to the next team Hamburg via infrastructure, contacts and instructions, is to infect our successors with enthusiasm for the possibilities of neural networks in the life sciences and pave the way to repeated use of the technology.
Part of iGEM always is to think about the next generation of researchers and of people benefitting from the synthetic biology we do.
We thought about ways to reach out to pupils that may already have a sense of the natural sciences but lack contact to biology and its related fields. The SFZ, an established institution for extracurricular activities in science, engineering, tech and maths, which is conveniently located close to team Hamburgs University [6], did prove to be the perfect partner.
The team was quite curious about iGEM and enthusiastic about a collaboration. They expressed their wish to venture more into the life sciences and welcomed us as input giver, as supervisor or as external experts.
Because of the school year just starting, the collaboration did not yet bear many fruits, but we already got to know some pupils with interest in biological sciences and recommended ourselves to the teachers initiating projects with them.
We hope a long lasting collaboration of our team and of future teams will commence with the SFZ and are happy to shape young scientists, not unlike researchers that came before us.
For more information see our education page at see our Education page