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Subcellular Fractionation01:32

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Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies.

Carlos Pintado-Grima1, Oriol Bárcenas1,2, Eva Arribas-Ruiz1

  • 1Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain.

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Summary

This study generates reliable datasets for proteins involved in liquid-liquid phase separation (LLPS), distinguishing driver and client proteins. These curated resources improve understanding of LLPS mechanisms and benchmark predictive algorithms.

Keywords:
BenchmarkClientDatasetsDisorderDriverIntegrationLiquid–liquid phase separationMachine learningNegativeProteins

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Cell Biology

Background:

  • Proteins form dynamic biomolecular condensates via liquid-liquid phase separation (LLPS) in cells.
  • Proteins in condensates act as drivers (forming condensates) or clients (localizing within).
  • Existing LLPS databases have data inconsistencies, hindering reliable predictive model development.

Purpose of the Study:

  • To create high-confidence datasets of driver and client proteins involved in LLPS.
  • To establish standardized negative datasets for non-LLPS proteins.
  • To provide a comprehensive benchmark for LLPS predictive algorithms.

Main Methods:

  • Integrated biocuration of existing LLPS databases.
  • Generation of standardized positive (client/driver) and negative (non-LLPS) protein datasets.
  • Analysis of physicochemical properties and benchmarking of 16 predictive algorithms.

Main Results:

  • Creation of confident datasets for client and driver proteins.
  • Identification of significant physicochemical differences between LLPS and non-LLPS proteins.
  • Comprehensive benchmarking revealing limitations in predictive algorithms.

Conclusions:

  • The generated datasets provide a reliable resource for studying protein roles in LLPS.
  • Physicochemical properties underlying LLPS are distinct among protein subsets.
  • This work offers the most extensive benchmark to date for LLPS predictive algorithms.