Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry

Peptide identification in mass spectrometry-based proteomics is crucial for understanding protein function and dynamics. Traditional database search methods, though widely used, rely on heuristic scoring functions, and statistical estimations must be introduced to achieve a higher identification rate. Here we introduce DeepSearch, a deep learning-based end-to-end database search method for tandem mass spectrometry. DeepSearch leverages a modified transformer-based encoder–decoder architecture under the contrastive learning framework. Unlike conventional methods, which rely on ion-to-ion matching, DeepSearch adopts a data-driven approach to score peptide–spectrum matches. DeepSearch can also profile variable post-translational modifications in a zero-shot manner. We show that DeepSearch’s scoring scheme expresses less bias and does not require any statistical estimation. We validate DeepSearch’s accuracy and robustness across various datasets, including those from species with diverse protein compositions and a modification-enriched dataset. DeepSearch sheds new light on database search methods in tandem mass spectrometry. Yu and Li present DeepSearch, a deep learning-based method for peptide identification in mass spectrometry, offering unbiased, data-driven scoring without statistical estimation. It accurately profiles post-translational modifications in a zero-shot manner.