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Towards a Unified Pipeline: Comparing tools for Thermal Proteome Profiling experiments

AI and Bioinformatics

Abstract #271 Posted on28 March 202512 May 2025 12 May 2025 12h21

Abstract

Epigenetic modifications modulate gene expression without altering the DNA sequence and are potentially reversible, making epigenetic modifying enzymes promising drug targets against antimicrobial resistance.
Identifying the protein targets of epigenetic drugs is challenging. Thermal Proteome Profiling (TPP) is powerful approach for detecting drug-affected proteins across the proteome. Although multiple analytical methods have been developed to interpret TPP data, their comparative performance remains underexplored.
We applied three analytical pipelines to a benchmark dataset to evaluate the impact of data processing tools on protein identification. Using the TPP experiment by the Savitski lab (Franken et al., 2015), in which K562 cells treated by with Panobinostat, we re-analysed both our replicated dataset and the original raw files (Childs et al., 2004).
Our results show that the choice of search engine affects the proteins identified. This underscores the importance of careful selecting and standardization of data analysis strategies in TPP workflows to ensure robust and reliable identification of drug targets. These optimized pipelines are now being applied to high-resolution datasets acquired on the Orbitrap Astral to extend target identification to more complex samples.

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Epigenetic modifications modulate gene expression without altering the DNA sequence and are potentially reversible, making epigenetic modifying enzymes promising drug targets against antimicrobial resistance.  
Identifying the protein targets of epigenetic drugs is challenging. Thermal Proteome Profiling (TPP) is powerful approach for detecting drug-affected proteins across the proteome. Although multiple analytical methods have been developed to interpret TPP data, their comparative performance remains underexplored. 
We applied three analytical pipelines to a benchmark dataset to evaluate the impact of data processing tools on protein identification. Using the TPP experiment  by the Savitski lab (Franken et al., 2015), in which  K562 cells treated by with Panobinostat, we re-analysed both our replicated dataset and the original  raw files (Childs et al., 2004).
Our results show that the choice of search engine affects the proteins identified. This underscores the importance of careful selecting and standardization of data analysis strategies in TPP workflows to  ensure robust and reliable identification of drug targets. These optimized pipelines are now being applied to high-resolution datasets acquired on the Orbitrap Astral to extend target identification to more complex samples.

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