  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 have to be introduced for a higher identification rate.
Here, we introduce DeepSearch, the first 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 that rely on ion-to-ion
matching, DeepSearch adopts a data-driven approach to score peptide spectrum
matches. DeepSearch is also the first deep learning-based method that can
profile variable post-translational modifications in a zero-shot manner. We
showed that DeepSearch's scoring scheme expressed less bias and did not require
any statistical estimation. We validated 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.
