Amyloids don’t aggregate alone 🤝🧬 Meet AmyloGraph

Publications
AmyloGraph is a curated database of experimentally validated amyloid–amyloid interactions, enabling systematic exploration of cross-seeding and aggregation modulation.
Author

BioGenies Lab

Published

October 16, 2023

Keywords

amyloids, protein aggregation, cross-seeding, AmyloGraph, database, bioinformatics, neurodegeneration

New to this topic? Read the plain-language story →

📌 Project highlights

🎉 New paper out! This one tackles a fundamental gap:

👉 amyloids interacting with each other… but no structured data 😄

👉 AmyloGraph: a comprehensive database of amyloid–amyloid interactions

🔗 Try it yourself

👉 Explore the amyloid interaction network directly

🎧 Audio summary

Amyloids don’t just aggregate…
they talk to each other, accelerate, inhibit, cross-seed 😳

👉 Here’s a short audio overview 🎧 explaining what AmyloGraph brings:

🔬 What is this about?

Amyloids are typically studied as individual aggregating proteins

BUT in reality:

👉 they interact with each other during aggregation

These interactions can:

  • ⚡ accelerate fibril formation
  • 🛑 inhibit aggregation
  • 🔀 create heterogeneous fibrils

👉 and may explain:

  • Alzheimer’s + Parkinson’s overlap
  • prion-like propagation
  • complex disease mechanisms

⚠️ The core problem

Before AmyloGraph:

  • data scattered across hundreds of papers
  • inconsistent terminology
  • difficult to compare experiments

👉 even contradictory conclusions

There was:

❌ no centralized dataset
❌ no standardized vocabulary
❌ no way to model interactions

🧠 What we built

👉 AmyloGraph = curated database of amyloid–amyloid interactions

📊 Data scale

  • 883 interactions
  • 46 amyloid proteins
  • 172 publications

👉 all experimentally validated

⚙️ Standardization (this is key)

AmyloGraph introduces 3 descriptors:

  1. ⚡ Effect on aggregation speed
  2. 🤝 Physical binding evidence
  3. 🔀 Formation of mixed fibrils

👉 turning messy literature into structured, comparable data

🔗 Graph-based representation

Data is presented as:

  • nodes → amyloid proteins
  • edges → interactions

👉 forming an amyloid interaction network

📊 Multiple views

  • 🔗 graph (network exploration)
  • 📋 table (filter + download)
  • 🔍 single interaction (full details)

👉 usable both for:

  • humans
  • ML pipelines

📊 Key insights

🧬 Amyloids form a network

Aggregation is not isolated:

👉 proteins influence each other’s behavior

🔀 Cross-talk is widespread

Interactions include:

  • cross-seeding
  • inhibition
  • co-aggregation

👉 all encoded in a standardized way

⚠️ Context matters

Results depend on:

  • pH
  • concentration
  • experimental setup

👉 biology is messy (and the database reflects that)

🚀 Why this matters

🧠 Disease understanding

Helps explain:

  • overlapping neurodegenerative diseases
  • propagation of aggregation

🤖 Machine learning & modeling

Finally enables:

  • prediction of cross-interactions
  • tools like PACT / AmyloComp built on top

💊 Therapeutic strategies

If we know:

👉 which amyloids interact

we can:

  • block interactions
  • design inhibitors
  • target aggregation pathways

💚 BioGenies perspective

This is a foundational dataset paper 🧱

👉 not flashy, but extremely powerful

Explore the BioGenies publication catalogue and the software and databases behind our research.