LLPS datasets & benchmarking: bringing order to condensate chaos 🧬📊

Publications
We present curated datasets of proteins involved in liquid–liquid phase separation (LLPS), enabling robust benchmarking and improved machine learning predictions in condensate biology.
Author

BioGenies Lab

Published

July 9, 2025

Keywords

LLPS, liquid-liquid phase separation, biomolecular condensates, protein datasets, machine learning, bioinformatics, benchmarking

📌 Project highlights

🎉 New paper out! And this one is a big one, not just another tool, but a foundation for the whole field 😄

👉 Comprehensive protein datasets and benchmarking for liquid–liquid phase separation studies

🔗 Data & resources

This is not just a paper, it’s a community resource for building and benchmarking LLPS models.

🎧 Audio summary

Not everyone wants to read about κ parameters and sticker–spacer distributions over coffee ☕😄

👉 We’ve attached a short audio explanation 🎧 to make things easier.

🔬 What is this about?

Liquid–liquid phase separation (LLPS) is a process where proteins form biomolecular condensates, membrane-less compartments that organize cellular processes.

These condensates are crucial for:

  • gene regulation
  • stress response
  • disease mechanisms (e.g. neurodegeneration)

But studying LLPS computationally has a major problem:

👉 data is messy, inconsistent, and fragmented across databases

🚨 The problem we tackled

Existing LLPS databases:

  • use different definitions
  • contain inconsistent annotations
  • lack reliable negative examples

👉 This makes:

  • machine learning unreliable
  • benchmarking unfair
  • results hard to compare

🧠 What we built

We created high-confidence datasets of proteins involved in LLPS, based on:

🧬 Protein roles

  • Drivers → form condensates independently
  • Clients → join existing condensates
  • Negatives → proteins not associated with LLPS

👉 Importantly, we introduced two types of negative datasets:

  • structured proteins (PDB-like)
  • disordered proteins (DisProt-like)

This is critical because: 👉 disorder alone ≠ LLPS (big source of bias!)

⚙️ How it works

We used:

  • 📚 integration of multiple LLPS databases
  • 🧹 strict filtering criteria for data quality
  • 🧬 annotation of:
    • disorder
    • prion-like domains
    • physicochemical features

Then we:

👉 analyzed protein properties
👉 and benchmarked prediction tools

📊 Benchmarking LLPS predictors

We evaluated 16 bioinformatics tools on independent datasets.

Key findings:

  • 🤖 ML-based tools perform best
  • ⚠️ Many tools still have high false positive rates
  • 🧠 Models often confuse:
    • disorder
    • with actual phase separation

👉 Translation: we are still not great at predicting LLPS reliably 😄

🧬 Key biological insights

From the analysis:

  • 🧩 LLPS is multi-factorial, not driven by a single feature
  • ⚡ properties like:
    • charge distribution
    • aggregation propensity
    • interaction regions
      matter more than simple sequence features
  • 🧠 drivers and clients are different, but subtle

👉 And importantly: you cannot model LLPS properly without good negative data

🚀 Why this matters

This work:

  • 📊 sets a new standard for benchmarking
  • 🧠 improves machine learning reliability
  • 🔬 enables better biological interpretation of condensates

👉 In short: we finally have a clean dataset to stop guessing and start learning properly

💚 BioGenies context

This project connects directly to our interests in:

  • amyloids 🧬
  • protein aggregation 🧊
  • phase separation ⚡

👉 and gives us a solid foundation for future tools and models

💚 BioGenies perspective

This project connects directly to our interests in:

  • amyloids 🧬
  • protein aggregation 🧊
  • phase separation ⚡

👉 and gives us a solid foundation for future tools and models