# Post-Transcriptional Modification Integration for Ligand–Receptor Cellular Network Inference

## TL;DR

This paper introduces a new method to improve the accuracy of predicting cell communication by incorporating protein modifications.

## Contribution

The novel contribution is integrating post-translational modifications into ligand–receptor interaction inference using an extended BulkSignalR framework.

## Key findings

- PTM integration reduces false positives in pathway activation predictions.
- The method reveals context-specific signaling in cancer datasets.
- The framework supports all types of PTMs and works with bulk and single-cell data.

## Abstract

Cell–cell communications are widely explored to understand tissue homeostasis and diseases. Numerous computational tools have been developed to infer cellular interactions from transcriptomic or proteomic expression data. However, proteins often carry post-translational modifications (PTMs) that can induce conformational switches and alter their functional properties. A key challenge remains to incorporate PTM data in the inference and analysis of cellular interactions. Here, we propose an extension of our previously published tool BulkSignalR to integrate PTM information in ligand–receptor interactions and downstream pathway predictions. This new functionality is compatible with bulk and single-cell data, and it supports all types of PTMs. Based on two illustrative datasets, we show that this new feature provides deeper insights into biological pathway regulation and that PTM integration helps reduce false-positive results occasionally produced by standard approaches.

•Extending BulkSignalR to integrate post-translational modifications (PTMs) into ligand–receptor interaction inference.•PTM integration improves accuracy and confidence in pathway activation prediction.•Application to two cancer datasets reveals context-specific signaling.•Framework supports all types of PTMs.

Extending BulkSignalR to integrate post-translational modifications (PTMs) into ligand–receptor interaction inference.

PTM integration improves accuracy and confidence in pathway activation prediction.

Application to two cancer datasets reveals context-specific signaling.

Framework supports all types of PTMs.

We developed an extension of BulkSignalR to integrate post-translational modification data in the inference of ligand–receptor interactions and downstream signaling pathways. Applied to laryngeal squamous cell carcinoma and renal carcinoma datasets, this approach refines pathway activation analysis, reduces false positives, and reveals context-dependent signaling mechanisms. By combining proteomic and post-translational modification data, it provides a more mechanistic understanding of cellular communication and regulatory processes across biological contexts.