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1. Efficient Shared Peak Counting in Database Peptide Search Using Compact Data Structure for Fragment-Ion Index

   https://doi.org/10.1109/BIBM47256.2019.8983152  

   Haseeb, Muhammad; Saeed, Fahad (November 2019, Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

   Database search is the most commonly employed method for identification of peptides from MS/MS spectra data. The search involves comparing experimentally obtained MS/MS spectra against a set of theoretical spectra predicted from a protein sequence database. One of the most commonly employed similarity metrics for spectral comparison is the shared-peak count between a pair of MS/MS spectra. Most modern methods index all generated fragment-ion data from theoretical spectra to speed up the shared peak count computations between a given experimental spectrum and all theoretical spectra. However, the bottleneck for this method is the gigantic memory footprint of fragment-ion index that leads to non-scalable solutions. In this paper, we present a novel data structure, called Compact Fragment-Ion Index Representation (CFIR), that efficiently compresses highly redundant ion-mass information in the data to reduce the index size. Our proposed data structure outperforms all existing fragment-ion indexing data structures by at least 2× in memory consumption while exhibiting the same time complexity for index construction and peptide search. The results also show comparable indexing speed, search speed and speedup scalability for CFIR-index and the state-of-the-art algorithms. 

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2. SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions

   https://doi.org/10.1371/journal.pone.0259349  

   Tariq, Muhammad Usman; Saeed, Fahad (October 2021, PLOS ONE)

   Lisacek, Frederique (Ed.)

   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/ . 

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3. Accurate de novo peptide sequencing using fully convolutional neural networks

   https://doi.org/10.1038/s41467-023-43010-x  

   Liu, Kaiyuan; Ye, Yuzhen; Li, Sujun; Tang, Haixu (December 2023, Nature Communications)

   Abstract De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we presentPepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data. 

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4. SpecEncoder: deep metric learning for accurate peptide identification in proteomics

   https://doi.org/10.1093/bioinformatics/btae220  

   Liu, Kaiyuan; Tao, Chenghua; Ye, Yuzhen; Tang, Haixu (June 2024, Bioinformatics)

   Abstract MotivationTandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. ResultsWe evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%–2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%–15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%–12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder’s potential to enhance peptide identification for proteomic data analyses. Availability and ImplementationThe source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu. 

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5. Exact Distributed Stochastic Block Partitioning

   https://doi.org/10.1109/CLUSTER52292.2023.00010  

   Wanye, Frank; Gleyzer, Vitaliy; Kao, Edward; Feng, Wu-Chun (October 2023, IEEE)

   Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure, but its inherently serial nature hinders its widespread adoption by the wider scientific community. To make it practical to analyze large real-world graphs with SBP, there is a growing need to parallelize and distribute the algorithm. The current state-of-the-art distributed SBP algorithm is a divide-and-conquer approach that limits communication between compute nodes until the end of inference. This leads to the breaking of computational dependencies, which causes convergence issues as the number of compute nodes increases and when the graph is sufficiently sparse. To address this shortcoming, we introduce EDiSt — an exact distributed stochastic block partitioning algorithm. Under EDiSt, compute nodes periodically share community assignments during inference. Due to this additional communication, EDiSt improves upon the divide-and-conquer algorithm by allowing it to scale out to a larger number of compute nodes without suffering from convergence issues, even on sparse graphs. We show that EDiSt provides speedups of up to 26.9x over the divide-and-conquer approach and speedups up to 44.0x over shared memory parallel SBP when scaled out to 64 compute nodes. 

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