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

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 . The full database search strategy proposed in this study expands the search scope to include all peptide sequences within the database, with peptide mass tolerances extending to several thousand Daltons. Dear-PSM utilizes an inverted index algorithm to construct an index table for experimental spectra, enabling rapid searches, and employs deep learning algorithms for peptide validation. Moreover, Dear-PSM supports up to 20 variable modifications per peptide sequence and considers all possible combinations of these modifications, significantly expanding the peptide search space. image