fibrea.qmd

---
title: "Functional Interfaces and Biomolecular Rules for Engineered Amyloids"
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::: {.hero-strip}
<span class="hero-pill">
  <i class="bi bi-grid-3x3-gap-fill"></i> M-ERA.NET Call 2025 – Functional materials
</span>
<span class="hero-pill secondary">
  <i class="bi bi-hourglass-split"></i> Duration: 36 months · TRL 1 → 3
</span>
<br><br>
<span class="hero-text">
  <strong>Coordinator:</strong> Medical University of Białystok, Poland
</span>
:::

<div class="tagline">
Designing programmable, curli-based amyloid materials through molecular grammar, simulations and biofabrication.
</div>

---


![](subsite/fig/graphical_abstracts/FIBREA.png)

## Project overview

FIBREA tackles a central challenge in functional materials: **how to design amyloid-based biomaterials rationally**, rather than by trial and error.

We focus on **curli-based functional amyloid materials (CFAMs)**, engineered variants of CsgA that:  

- self-assemble into fibrils with **controllable morphology**  
- remain compatible with **bacterial secretion** and **intracellular inhibition**  
- carry modular **functional domains** such as fluorophores or enzymes  

The core idea is to learn a **molecular grammar** that links CFAM sequence features to fibril properties, co-assembly behavior, and production constraints.

::: {.callout-note.callout-fibrea}
### Project at a glance
- **Acronym:** FIBREA  
- **Call:** M-ERA.NET 2025 – functional materials  
- **Duration:** 36 months  
- **TRL:** 1 → 3  
- **Main outputs:**  
  - Molecular grammar for CFAM design  
  - Coarse-grained simulation framework (CALVADOS-based)  
  - Optimized bacterial expression system  
  - Fluorescent and catalytic CFAM prototypes  
:::

---

## Objectives

:::{.section-intro}
FIBREA builds a full **design–build–test–learn** loop for amyloid materials.
:::

- Develop a **generalizable molecular grammar** for stable CFAMs with tunable self-assembly.
- Encode compatibility with:
  - **curli secretion machinery (CsgEFG)**
  - **intracellular chaperones** (CsgC, Spy)
- Model CFAM assembly and inhibition using **coarse-grained simulations** (CALVADOS).
- Optimize a **bacterial expression system** for high-yield CFAM production.
- Demonstrate:
  - **fluorescent CFAMs** (GFP/YFP, including FRET-based co-assembly)
  - **enzymatic CFAMs** for multi-step catalytic cascades.
- Advance the overall system from **TRL 1 to TRL 3**.

---

## Concept and approach

:::{.section-intro}
FIBREA combines **AI**, **physics-based simulations** and **wet-lab validation** into a single workflow.
:::

- **Molecular grammar (WP1)**  
  - Conditional denoising diffusion model trained on > 43 000 curli operons.  
  - Encodes secretion compatibility, inhibition susceptibility, and fibrillization behavior.

- **Coarse-grained simulations (WP2)**  
  - CALVADOS used to simulate nucleation, growth, and polymorphism of CFAM fibrils.  
  - Outputs structural descriptors (contact maps, stiffness, solvent exposure) feeding back into the grammar.

- **Expression and production (WP3)**  
  - Artificial gene design, codon optimization, fermentation strategy comparison, purification workflows.

- **Structural and biophysical validation (WP4)**  
  - ThT, Congo Red, Amytracker, CD, FTIR, TEM, AFM, cryo-EM.  
  - In vitro testing of CsgC variants as inhibitors.

- **Functional CFAMs (WP5)**  
  - Fluorescent CFAMs for tracking and FRET analyses.  
  - Dual-enzyme CFAMs as **molecular assembly lines**.

Only **independently reproduced fibrillization results** (Lithuanian labs) are used to refine the grammar, enforcing robustness.

---

## Impact

<div class="impact-row">

<div class="impact-card">
<h4><i class="bi bi-microscope"></i> Scientific</h4>
- Establishes **design rules** linking sequence → structure → function in amyloid materials.  
- Bridges machine learning, coarse-grained physics, and experiments.  
- Provides new insight into the **curli system** and **CsgC-mediated inhibition**.
</div>

<div class="impact-card">
<h4><i class="bi bi-building-gear"></i> Economic</h4>
- Lays the foundation for **industrial CFAM applications**: biosensing, catalysis, filtration, remediation.  
- Enables **bio-based, programmable materials** as alternatives to petroleum-derived polymers.
</div>

<div class="impact-card">
<h4><i class="bi bi-globe2"></i> Societal and environmental</h4>
- Promotes **biodegradable, low-toxicity materials** from microbial systems.  
- Implements **FAIR data**, Open Science, and Responsible Research and Innovation (RRI).  
- Engages the public to show amyloids beyond their disease associations.
</div>

</div>

---

## Consortium

:::{.section-intro}
FIBREA unites complementary expertise across **bioinformatics, simulations, amyloid biology and synthetic biology**.
:::

<div class="partner-grid">

<div class="partner-card">
<div class="partner-name">Medical University of Białystok (PL)</div>
<div class="partner-role">Coordinator · WP1 and WP6 Leader</div>
<div class="pill"><i class="bi bi-cpu"></i> Computational design</div>
<div class="pill"><i class="bi bi-diagram-3"></i> Project management</div>
<p>Leads molecular grammar development and overall coordination. Expertise in protein informatics, machine learning for amyloids and peptides and FAIR/DOME-compliant data workflows.</p>
Leader: 
<div>![Michał Burdukiewicz](subsite/fig/ppl/mb.jpeg "Michał Burdukiewicz")</div>
</div>

<div class="partner-card">
<div class="partner-name">Vilnius University (LT)</div>
<div class="partner-role">WP4 Leader</div>
<div class="pill"><i class="bi bi-droplet-half"></i> Aggregation assays</div>
<div class="pill"><i class="bi bi-badge-3d"></i> cryo-EM / TEM / AFM</div>
<p>Experimental amyloid specialists performing biophysical characterization, structural validation and independent reproducibility checks.</p>
Leader:
<div>![Darius Šulskis](subsite/fig/fibrea/DariusS.png "Darius Šulskis")</div>
</div>

<div class="partner-card">
<div class="partner-name">University of Copenhagen (DK)</div>
<div class="partner-role">WP2 Leader</div>
<div class="pill"><i class="bi bi-mesh"></i> Coarse-grained simulations</div>
<p>Developers of CALVADOS and world leaders in protein folding and aggregation modeling. Provide physics-based constraints and structural descriptors for CFAMs.</p>
Leader:
<div>![Kresten Lindorff-Larsen](subsite/fig/fibrea/KDVS-KRESTEN.jpg "Kresten Lindorff-Larsen")</div>
</div>

<div class="partner-card">
<div class="partner-name">Bilkent University – UNAM (TR)</div>
<div class="partner-role">WP3 Leader</div>
<div class="pill"><i class="bi bi-bacteria"></i> Curli expression systems</div>
<p>Experts in curli-based nanomaterials and biofilm engineering. Optimize host strains, codon usage, fermentation and purification for CFAM production.</p>
Leader:
<div>![Urartu Ozgur Safak Seker / Urartu Özgür Şafak Şeker](subsite/fig/fibrea/urartu_seker_wp1.jpg "Urartu Özgür Şafak Şeker")</div>
</div>

<div class="partner-card">
<div class="partner-name">Autonomous University of Barcelona (ES)</div>
<div class="partner-role">WP5 Leader</div>
<div class="pill"><i class="bi bi-lightning-charge"></i> Functional amyloids</div>
<p>Focus on functional CFAMs carrying fluorescent and enzymatic modules. Demonstrate catalytic CFAMs and FRET-based co-assembly.</p>
Leader:
<div>![Javier Garcia Pardo](subsite/fig/fibrea/JaviGP.jpeg "Javier Garcia Pardo")</div>
</div>

</div>

---

## Work packages

:::{.section-intro}
The work plan is organized into six interlinked WPs, forming a closed design–build–test–learn loop.
:::

<div class="wp-grid">

<div class="wp-card">
<div class="wp-label">WP1</div>
<div class="wp-title">Design of the molecular grammar for CFAMs</div>
<div class="wp-meta">Lead: PL · Type: research (TRL 1–2)</div>
<div class="badge-soft"><i class="bi bi-cpu"></i> AI and sequence design</div>
<ul>
  <li>Define biological constraints for secretion-competent constructs.</li>
  <li>Learn CsgA variability and co-evolution with CsgC.</li>
  <li>Develop conditional diffusion models for CFAM design.</li>
  <li>Integrate feedback from simulations (WP2) and experiments (WP3–5).</li>
</ul>
</div>

<div class="wp-card">
<div class="wp-label">WP2</div>
<div class="wp-title">Coarse-grained modeling of CFAMs</div>
<div class="wp-meta">Lead: DK · Type: research (TRL 1–2)</div>
<div class="badge-soft"><i class="bi bi-mesh"></i> CALVADOS</div>
<ul>
  <li>Set up CALVADOS simulations for CFAMs and inhibitors.</li>
  <li>Screen engineered sequences in silico.</li>
  <li>Extract structural descriptors for morphology and stability.</li>
  <li>Calibrate against data from WP3 and WP4.</li>
</ul>
</div>

<div class="wp-card">
<div class="wp-label">WP3</div>
<div class="wp-title">Optimization of the CFAM expression system</div>
<div class="wp-meta">Lead: TR · Type: development (TRL 2–3)</div>
<div class="badge-soft"><i class="bi bi-gear-wide-connected"></i> Bioprocess</div>
<ul>
  <li>Select host systems and design synthetic genes.</li>
  <li>Optimize codon usage and expression constructs.</li>
  <li>Compare batch vs continuous fermentation.</li>
  <li>Improve purification and confirm functional integrity.</li>
</ul>
</div>

<div class="wp-card">
<div class="wp-label">WP4</div>
<div class="wp-title">Assessment of uniform amyloid fibrillation</div>
<div class="wp-meta">Lead: LT · Type: research (TRL 2–3)</div>
<div class="badge-soft"><i class="bi bi-badge-3d"></i> Structure</div>
<ul>
  <li>Biophysical assays (ThT, Congo Red, Amytracker, CD, FTIR).</li>
  <li>TEM and AFM imaging of fibrils.</li>
  <li>Cryo-EM structures of selected CFAM fibrils.</li>
  <li>Inhibition studies with CsgC variants.</li>
</ul>
</div>

<div class="wp-card">
<div class="wp-label">WP5</div>
<div class="wp-title">Harnessing material properties of CFAMs</div>
<div class="wp-meta">Lead: ES · Type: development and demo (TRL 3)</div>
<div class="badge-soft"><i class="bi bi-lightbulb"></i> Function</div>
<ul>
  <li>Assess amyloid aggregation of functional CFAM fusions.</li>
  <li>Validate GFP/YFP-based co-assembly and FRET.</li>
  <li>Demonstrate dual-enzyme CFAM catalytic cascades.</li>
  <li>Test inter-lab reproducibility of fibrillization and activity.</li>
</ul>
</div>

<div class="wp-card">
<div class="wp-label">WP6</div>
<div class="wp-title">Consortium management and dissemination</div>
<div class="wp-meta">Lead: PL</div>
<div class="badge-soft"><i class="bi bi-people"></i> Coordination</div>
<ul>
  <li>Scientific, administrative, and financial coordination.</li>
  <li>Mattermost-based internal communication and data sharing.</li>
  <li>Open Science, FAIR data, and RRI implementation.</li>
  <li>Website, outreach, and training for early-career researchers.</li>
</ul>
</div>

</div>

---

## Sustainability, RRI and data management

- **Sustainability:**  
  - Protein-based, biodegradable materials; microbial expression; potential for circular use.  
  - Focus on low-energy, resource-efficient production strategies.

- **RRI and ethics:**  
  - Ethical review of experimental activities where required.  
  - Biosecurity risk assessment aligned with EU and national regulations.  
  - Public engagement on the constructive uses of amyloids.

- **Data and open science:**  
  - FAIR-compliant data, with deposition to public repositories (e.g. Zenodo, NCBI).  
  - Code release (e.g. GitHub) under permissive licenses.  
  - Documentation following the **DOME** recommendations for machine learning in life sciences.

---

## Publications and outputs

A detailed list will be maintained as the project progresses. Planned outputs include:

- Articles on:
  - CFAM molecular grammar and generative modeling  
  - CALVADOS-based CFAM simulations  
  - Structural and functional characterization of CFAMs
- Conference presentations and workshops.
- Open-source tools and curated datasets.

---

## Contact

**Project Coordinator**  
**Michał Burdukiewicz, PhD**  
Medical University of Białystok  
Jana Kilińskiego 1, 15-089 Białystok, Poland  

<div class="contact-links">
<i class="bi bi-envelope"></i> <a href="mailto:michal.burdukiewicz@umb.edu.pl">michal.burdukiewicz@umb.edu.pl</a>  
</div>