PLiCat: decoding protein-lipid interactions by large language model.

Area of Science:

• Biochemistry
• Computational Biology
• Bioinformatics

Background:

• Protein-lipid interactions are crucial for cellular functions but distinguishing lipid-binding specificity remains challenging.
• Current methods lack the ability to accurately categorize interacting lipids, hindering a deeper understanding of these essential molecular interactions.

Purpose of the Study:

• To introduce PLiCat (Protein-Lipid interaction Categorization tool), a novel sequence-based framework for predicting lipid categories that interact with proteins.
• To provide a computational tool that addresses the limitations in discriminating among lipid categories in protein-lipid interactions.

Main Methods:

• PLiCat utilizes a hybrid deep learning architecture, integrating ESM-2 and BERT (Bidirectional Encoder Representations from Transformers).
• The framework performs accurate and interpretable classification across eight major lipid categories based on protein sequence data.
• Attribution analysis is employed to uncover sequence-encoded lipid-binding signatures and identify key residues involved in binding specificity.

Main Results:

• PLiCat accurately predicts lipid categories from protein sequences, offering insights into lipid recognition mechanisms.
• The tool successfully identifies sequence-encoded lipid-binding signatures and highlights residues critical for binding specificity.
• PLiCat demonstrates potential in discovering cryptic lipid-binding sites within protein sequences and assessing mutation impacts on lipid binding.

Conclusions:

• PLiCat is the first computational tool capable of predicting lipid categories solely from protein sequences.
• This framework offers valuable insights into protein-lipid recognition and has potential applications in rational protein design.
• PLiCat provides a powerful new resource for researchers studying protein-lipid interactions and their functional implications.