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Mistle: Metaproteomic index and spectral library search engine

  • Introduction: With the introduction of accurate deep learning predictors, spectral matching applications might experience a renaissance in tandem mass spectrometry (MS/MS) driven proteomics. Deep learning models, e.g., Prosit, predict complete MS/MS spectra from peptide sequences and give the unprecedented ability to accurately predict mass spectra that may arise from any given proteome. However, the amount of spectral data is enormous when querying large search spaces, e.g., metaproteomes composed of many different species. Current spectral library search software, such as SpectraST, is not equipped to meet run time and memory constraints imposed by such large MS/MS databases, covering several millions of peptide spectrum predictions. Methods: Inspired by the fragment index data structure that had been introduced with MSFragger, we implement an efficient peak matching algorithm for computing spectral similarity between query and library spectra. Mistle (Metaproteomic index andIntroduction: With the introduction of accurate deep learning predictors, spectral matching applications might experience a renaissance in tandem mass spectrometry (MS/MS) driven proteomics. Deep learning models, e.g., Prosit, predict complete MS/MS spectra from peptide sequences and give the unprecedented ability to accurately predict mass spectra that may arise from any given proteome. However, the amount of spectral data is enormous when querying large search spaces, e.g., metaproteomes composed of many different species. Current spectral library search software, such as SpectraST, is not equipped to meet run time and memory constraints imposed by such large MS/MS databases, covering several millions of peptide spectrum predictions. Methods: Inspired by the fragment index data structure that had been introduced with MSFragger, we implement an efficient peak matching algorithm for computing spectral similarity between query and library spectra. Mistle (Metaproteomic index and spectral library search engine) uses index partitioning and SIMD (Single instruction, multiple data) intrinsics, which greatly improves speed and memory efficiency for searching large spectral libraries. Mistle is written in C++20 and highly parallelized. Results: We demonstrate the efficiency of Mistle on two predicted spectral libraries for the lab-assembled microbial communities 9MM and SIHUMIx. Compared to the spectral library search engine SpectraST, Mistle shows a >10-fold runtime improvement and is also faster than msSLASH, which uses locality-sensitive hashing. Although Mistle is slower than MSFragger, Mistle‘s memory footprint is an order of magnitude smaller. Furthermore, we find evidence that the spectral matching approach to predicted libraries identifies peptides with higher precision. Mistle detects peptides not found by database search via MSFragger and in turn uncovers unnoticed false discoveries among their matches. Conclusion: In this study, we show that predicted spectral libraries can enhance peptide identification for metaproteomics. Mistle provides the means to efficiently search large-scale spectral libraries, highlighted for the microbiota 9MM and SIHUMIx.zeige mehrzeige weniger

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  • Mistle_HUPO_final.pdf
    eng

    Oral presentation at HUPO2022 in Cancun, Mexico. The spectral library search engine, Mistle, is presented alongside novel ideas to facilitate peptide identification in metaproteomics.

  • Poster_Mistle_03_22_final Druck.pdf
    eng

    Poster presentation at EuPA 2022 in Leibzig and EuBIC-MS Winter School 2022 in Oeiras, Portugal. The spectral library search engine, Mistle, is presented alongside novel ideas to facilitate peptide identification in metaproteomics.

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Metadaten
Autor*innen:Yannek NowatzkyORCiD
Koautor*innen:Philipp Benner, K. Reinert, Thilo MuthORCiD
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:VP Vizepräsident
VP Vizepräsident / VP.1 eScience
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Algorithms; Mass spectrometry; Metaproteomics; Proteomics
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Veranstaltung:HUPO2022
Veranstaltungsort:Cancun, Mexico
Beginndatum der Veranstaltung:04.12.2022
Enddatum der Veranstaltung:08.12.2022
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:22.12.2022
Referierte Publikation:Nein
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