Towards Less Biased Data-driven Scoring with Deep Learning-Based End-to-end Database Search in Tandem Mass Spectrometry

TL;DR

DeepSearch introduces a novel deep learning-based end-to-end approach for peptide identification in mass spectrometry, reducing bias and eliminating the need for statistical estimation, with robust performance across diverse datasets.

Contribution

It is the first to apply a transformer-based deep learning model for end-to-end peptide spectrum matching, profiling modifications in a zero-shot manner, and reducing bias in scoring.

Findings

DeepSearch outperforms traditional methods in accuracy.

It profiles post-translational modifications without prior training.

The scoring scheme shows less bias and no statistical estimation needed.

Abstract

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.