Dear‐PSM: A deep learning‐based peptide search engine enables full database search for proteomics

Abstract

Abstract Peptide spectrum matching is the process of linking mass spectrometry data with peptide sequences. An experimental spectrum can match thousands of candidate peptides with variable modifications leading to an exponential increase in candidates. Completing the search within a limited time is a key challenge. Traditional searches expedite the process by restricting peptide mass errors and variable modifications, but this limits interpretive capability. To address this challenge, we propose Dear‐PSM, a peptide search engine that supports full database searching. Dear‐PSM does not restrict peptide mass errors, matching each spectrum to all peptides in the database and increasing the number of variable modifications per peptide from the conventional 3–20. Leveraging inverted index technology, Dear‐PSM creates a high‐performance index table of experimental spectra and utilizes deep learning algorithms for peptide validation. Through these techniques, Dear‐PSM achieves a speed breakthrough 7 times faster than mainstream search engines on a regular desktop computer, with a remarkable 240‐fold reduction in memory consumption. Benchmark test results demonstrate that Dear‐PSM, in full database search mode, can reproduce over 90% of the results obtained by mainstream search engines when handling complex mass spectrometry data collected from different species using various instruments. Furthermore, it uncovers a substantial number of new peptides and proteins. Dear‐PSM has been publicly released on the GitHub repository https://github.com/jianweishuai/Dear‐PSM.

Keywords

• deep learning
• inverted index
• mass spectrometry
• peptide search
• proteomics