Computational Approaches for Pathway‐Centric Analysis of Protein Post‐Translational Modifications

TL;DR

This review discusses computational tools for analyzing protein modifications in the context of cellular pathways, emphasizing the need for holistic approaches.

Contribution

The paper provides a systematic overview and critical evaluation of pathway-centric tools for PTM data analysis.

Findings

Pathway enrichment analysis is a common step in PTM data workflows.

Current tools face limitations in integration and visualization capabilities.

Future directions include improving pathway reconstruction and data integration.

Abstract

Protein function is dynamically modulated by post‐translational modifications (PTMs). Many different types of PTMs can nowadays be identified and quantified at a large scale using mass spectrometry. It is well known that many PTMs have an effect on protein function and cellular processes, and they should be studied not in isolation, but in the holistic context of cellular pathways. This is increasingly facilitated by a wide variety of computational efforts. This review aims to give a systematic overview of tools for pathway‐centric analysis of PTM data and critically evaluate the state of play in this research field. Starting from databases that make up the foundational prior knowledge, we follow typical steps that an analytical workflow might contain, including pathway enrichment analysis, algorithms for pathway reconstruction, and the integration and visualization of results. We then reflect on common limitations of all existing tools and give our opinion on future directions that we think are currently most desirable.

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