Identifying potential ligand-receptor interactions by integrating LSTM network and the attention mechanism for...

Cell-cell communication (CCC) mediated by ligand-receptor (L-R) interactions is fundamental to deciphering tissue development and disease mechanisms. While single-cell RNA sequencing (scRNA-seq) has advanced this field, existing computational methods for inferring CCC often suffer from limitations such as dependence on static databases and a failure to capture the sequential dependency of amino acids within proteins, which restricts their generalizability and predictive accuracy. Therefore, the primary objective of this study was to develop a robust computational framework capable of identifying potential L-R interactions directly from protein sequence data, thereby overcoming the reliance on static databases and enabling the discovery of novel signaling pairs.

To achieve this objective, we introduce CellAL, a deep learning-based framework for predicting potential interacting L-R pairs and decoding cellular communication. The CellAL pipeline consists of two main stages: (1) L-R Pair Identification, which extracts sequence features using BioTriangle, selects informative features via XGBoost to reduce dimensionality, and classifies interactions using a Long Short-Term Memory (LSTM) network integrated with an attention mechanism specifically designed to capture long-range sequence dependencies that characterize structural binding affinities; and (2) CCC Inference, which filters identified pairs using scRNA-seq data and quantifies crosstalk intensity through a comprehensive scoring strategy that combines expression thresholding, expression product, and specific expression metrics.

Performance evaluations on four standard L-R interaction datasets demonstrated that CellAL significantly surpassed classical protein-protein interaction prediction methods and achieved competitive performance against state-of-the-art ensemble models, achieving the highest AUPR values on three datasets. The identified To achieve L-R pairs showed a high degree of overlap with existing databases such as CellChat and Connectome. Furthermore, when applied to human melanoma scRNA-seq data, CellAL successfully inferred critical signaling networks, revealing strong bidirectional crosstalk between melanoma cells and cancer-associated fibroblasts (CAFs), macrophages, and endothelial cells. These findings were consistent with results from three other representative CCC prediction tools.

CellAL effectively overcomes the limitations of database dependence by leveraging sequence-level biochemical modeling to predict structural L-R interactions. By integrating deep learning predictions with transcriptomic data, CellAL provides a robust and valuable tool for dissecting complex CCC networks at single-cell resolution, particularly within the tumor microenvironment.