Scribe: Next Generation Library Searching for DDA Experiments

Abstract

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics

“…Spectral library searching has been shown previously to increase the sensitivity of peptide detection in quantitative proteomics by leveraging either acquired spectra or predicted fragment ion intensities. , With a diverse array of scoring metrics, analysis pipelines, and applications, spectral library searching has proved to be a robust method for a wide array of quantitative proteomics methods although it is predominantly used for label-free quantitation, and DIA methods . To enable spectral library searching for real-time decision making, we had to (1) determine a method and memory compatible means to store full-proteome spectral libraries, (2) enable conversion of diverse spectral library formats into this common format, and (3) optimize scoring functions for performant real-time decision making (Figure A).…”

“…Building from the efforts of real-time methods for quantitative proteomics and now RTLS, we wanted to explore the use of spectral library peptide matching for proteome-wide quantitative methods . Early work with spectral library searching for proteomics relied on the construction of empirically derived spectra to generate libraries using well established workflows such as SpectraST to confidently match peptides based on common score metrics (dot product, cosine score, spectral similarity). , Recent advances in deep learning have now contributed multiple pipelines for the in silico prediction of peptide spectra. , Algorithms such as Prosit enable users to predict peptide spectra for whole proteomes (2.6 million peptide spectra for human cells) and have recently been extended to incorporate isobaric labeled samples. , These predicted spectral libraries can then be used to efficiently score new empirical spectra or combined with database searching algorithms to re-score spectra for improved sensitivity. − Spectral library searching has been shown to be a sensitive and accurate way to identify peptides, especially those of complex spectra such as DIA experiments. − Lessons learned from using spectral libraries in DIA experiments have recently been leveraged to improve spectral library searches for data-dependent acquisition methods as well . These latest spectral library search algorithms show that while using even a predicted library, there is a sensitivity gain compared to cutting-edge database search methods .…”

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

“…We have also used MaxQuant within Galaxy to search for additional microbial peptides from the same sample. In the future, search algorithms such as FragPipe (45) and Scribe (46) will also be available within the Galaxy framework and add to the repertoire of tools and workflows for advanced multiomics analysis (9). One step that is often overlooked when using MS-based metaproteomics is the need to ascertain the quality of the PSMs for microbial peptide identification and protein inference.…”

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

“…Library generation (Figure 1) requires either a peptides file in .csv format of the precursors for which spectra are requested, or a .fasta file which is then digested and prepared in silico by Oktoberfest. Two output formats are supported by Oktoberfest, Spectronaut-compatible .csv and .msp files, which can be used directly in combination with most spectral library search engines such as SpectraST [52] or Scribe [37], analysis pipelines such as Skyline [53], or DIA workflows such as Spectronaut [54] or DIA-NN [8].…”

“…For this dataset, data-driven rescoring increased peptide coverage by 40% and protein coverage by 25% across 18 tissues for a draft tomato proteome [35]. Spectral libraries generated by Oktoberfest can be used for the analysis of data acquired using DIA, showcased for non-model organisms and non-canonical databases [36], or enable full proteome spectral library searching workflows as recently showcased in Scribe [37].…”