Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at
https://deepspecs.github.io/.
SpeCollate network consists of two branch, i.e., Spectrum Sub-Network (SSN) and Peptide Sub-Network (PSN). SSN processes spectra and generates spectral embeddings while PSN processes peptide sequences and generates peptides embeddings. Both types of embeddings are generated in real space of dimension 256.
1from specollate import SpeCollateSearch
2
3searcher = SpeCollateSearch(device='cuda') # or 'cpu'
4searcher.search(
5 mgf_dir='path/to/mgf_dir',
6 peptide_db='path/to/peptide_db',
7 output_dir='./specollate_output'
8)
9
1from specollate import SpeCollateSearch
2
3searcher = SpeCollateSearch(device='cuda') # or 'cpu'
4searcher.search_with_sample_data(output_dir='./specollate_output')
5
Tariq, Muhammad Usman, and Fahad Saeed. "SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions." PloS one 16.10 (2021): e0259349.
This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this model and its derivatives, which include models trained on outputs from the model or datasets created from the model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.
For any additional questions or comments, contact Fahad Saeed (
fsaeed@fiu.edu).
