A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Thang V Pham, Vinh V Nguyen, Duong Vu, Alex A Henneman, Robin A Richardson, Sander R Piersma, Connie R Jimenez

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgaveArtikelWetenschappelijkpeer review

2 Citaten (Scopus)

Samenvatting

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.

Originele taal-2Engels
Pagina's (van-tot)e2200041
TijdschriftProteomics
Volume23
Nummer van het tijdschrift7-8
DOI's
StatusGepubliceerd - apr. 2023

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