About
Protein phase separation has emerged as an important mechanism for
cellular compartmentalization, notably through liquid-liquid phase
separation (LLPS).
LLPS is a key mechanism for cellular compartmentalization, forming
membrane-less organelles that support critical physiological processes
like nuclear transcription and synaptic transmission. Dysregulation of
LLPS is linked to diseases such as amyotrophic lateral sclerosis (ALS),
underscoring the need to accurately identify phase-separating proteins
(PSPs).
To support this, over 20 computational tools have been developed to
predict LLPS behavior, using approaches ranging from simpler heuristic
rules to machine learning models.
However, these tools vary widely in their design and intended use,
and there is currently no unified resource to help researchers compare
and select the most appropriate one. This creates a barrier for
experimental scientists seeking efficient and accurate predictions.
Our work addresses this gap by providing a comprehensive review of
available LLPS predictors. We aim to offer a practical, centralized
guide to help researchers choose the right tool for their specific
scientific goals, ultimately reducing the cost and time of
trial-and-error experimentation.
Authors
Carlos Pintado-Grima
Carlos Pintado-Grima is a PhD student in Bioinformatics at the
Institute of Biotechnology and Biomedicine at the Autonomous University
of Barcelona (UAB). He obtained his degree in Biology and the Bachelor
of Science at UAB and Thompson Rivers University (Kamloops, BC, Canada).
He recieved his M.Sc. in Bioinformatics in 2020 at UAB. His current
research is focused on the development and analysis of bioinformatics
tools to better understand protein aggregation, folding and
misfolding.
Contact: Carlos.Pintado@uab.cat
Twitter: https://twitter.com/cpintadogrima
Oriol Bárcenas
Oriol Bárcenas is a PhD student in Bioinformatics, affiliated with
the Autonomous University of Barcelona (UAB) and the Spanish National
Research Council (CSIC). He completed his B.Sc degree in Biotechnology
at UAB in 2022, followed by an M.Sc. in Modelling for Science and
Engineering in 2023, also from UAB. His current research is focused on
the analysis of protein folding and aggregation data, alongside in
silico protein design and molecular dynamics (MD).
Contact: Oriol.Barcenas@uab.cat
Twitter: https://twitter.com/oriolbarcenas
Valentín Iglesias
Valentín Iglesias is a PhD in Biochemistry and Molecular Biology
working as a post-doc in the Centre for Clinical Research at the Medical
University of Białystok. His research is based on protein conformational
conversion on structured and mainly intrinsically disordered proteins
and the link between protein adaptations and taxonomic evolution.
Contact: Valentin.Iglesias@uab.cat
Twitter: https://twitter.com/ValentnIglesias.
Eva Arribas-Ruiz
Eva Arribas holds a B.Sc in Biotechnology from the University of
Barcelona and an M.Sc in Bioinformatics from UAB. She conducted a
research stay at the Medical University of Bialystok, focusing on the
analysis and prediction of liquid-liquid phase separation (LLPS).
Contact: Eva.Arribas@uab.cat
Michał Burdukiewicz
Michał Burdukiewicz is currently working as a post-doc at the
Institute of Biotechnology and Biomedicine at the Autonomous University
of Barcelona and a research assistant in the Centre for Clinical
Research at the Medical University of Białystok. His research interests
cover machine learning applications in the functional analysis of
peptides and proteins, focusing on amyloids. Moreover, he is
co-developing tools for proteomics, mainly hydrogen-deuterium exchange
monitored by mass spectrometry.
Contact: michaljan.burdukiewicz@uab.cat
Twitter: https://twitter.com/burdukiewicz
Website: https://github.com/michbur
