AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics

• DOI: 10.1101/2022.07.14.499992
• Publication Date: 2022-07-16T19:00:10Z
• Authors: Wen-Feng Zeng; Xie-Xuan Zhou; Sander Willems; Constantin Ammar; Maria Wahle; Isabell Bludau; Eugenia Voytik; Maximillian T. St...; Matthias Mann
• Keywords: Python

Abstract Machine learning and in particular deep (DL) are increasingly important mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility fragment intensities of a peptide just from amino acid sequence with good accuracy. However, is very rapidly developing field new neural network architectures frequently appearing, which challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, modular Python framework built on PyTorch library that learns predicts properties peptides ( https://github.com/MannLabs/alphapeptdeep ). It features model shop enables non-specialists create few lines code. AlphaPeptDeep represents post-translational modifications generic manner, even if only chemical composition known. Extensive use transfer obviates need large data sets refine experimental conditions. The predicting collisional cross sections at least par existing tools. Additional sequence-based also be predicted by as demonstrated novel HLA prediction improve identification data-independent acquisition.