Seq2Phase: language model-based accurate prediction of client proteins in liquid-liquid phase separation.

ABSTRACT:

Motivation

Liquid-liquid phase separation (LLPS) enables compartmentalization in cells without biological membranes. LLPS plays essential roles in membraneless organelles such as nucleoli and p-bodies, helps regulate cellular physiology, and is linked to amyloid formation. Two types of proteins, scaffolds and clients, are involved in LLPS. However, computational methods for predicting LLPS client proteins from amino-acid sequences remain underdeveloped.

Results

Here, we present Seq2Phase, an accurate predictor of LLPS client proteins. Information-rich features are extracted from amino-acid sequences by a deep-learning technique, Transformer, and fed into supervised machine learning. Predicted client proteins contained known LLPS regulators and showed localization enrichment

SUBMITTER: Miyata K 

PROVIDER: S-EPMC10777356 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature